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Yntec/DegreesOfFreedom | Yntec | "2024-05-29T04:31:51Z" | 19,975 | 1 | diffusers | [
"diffusers",
"safetensors",
"Base Model",
"General purpose",
"Photorealistic",
"Anime",
"Film",
"Portraits",
"LEOSAM",
"artificialguybr",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | "2024-05-29T03:54:58Z" | ---
license: creativeml-openrail-m
library_name: diffusers
pipeline_tag: text-to-image
tags:
- Base Model
- General purpose
- Photorealistic
- Anime
- Film
- Portraits
- LEOSAM
- artificialguybr
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
---
LiberteRedmond mixed with LEOSAMsFilmGirlUltra - The most versatile SD1.5 model now with this ultra realistic style!
Samples and prompts:
(Click for larger)
Top left: kodachrome camera transparency, dramatic lighting with husband and daughters enjoying pie with candles. sitting with a pretty cute little girl, Teal Gift Birthday Theme by Gil_Elvgren and Haddon_Sundblom
Top right: 90s VHS TV Photograph of Shirley Temple as Harley Quinn. Little Cow behind the scenes
Bottom left: beautiful detailed girl, Cartoon Pretty CUTE LITTLE Girl AS FAIRY, DETAILED CHIBI EYES, detailed hair, Ponytail, key shot at computer monitor, Magazine ad, iconic, 1940, sharp focus. high detail, woodland village, in the night, fantasy, crescent moon, luminous, toadstools, fireflies, fantasy, fairy tale, mist, highly detailed
Bottom right: little videogames, robert jordan pepperoni pizza, josephine wall winner, hidari, roll20 illumination, radiant light, sitting elementary girl, Pretty CUTE, gorgeous hair, DETAILED CHIBI EYES, Magazine ad, iconic, 1943, Cartoon, sharp focus, comic, watched towel. 4k art on canvas by kyoani and ROSSDRAWS
Original pages:
https://civitai.com/models/94123?modelVersionId=100409 (LiberteRedmond)
https://civitai.com/models/33208/leosams-filmgirl-ultra
# Recipe:
- SuperMerger Weight Sum Use MBW 0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0
Model A:
LEOSAMsFilmGirlUltra
Model B:
LiberteRedmond
Output:
DegreesOfFreedom |
Phind/Phind-CodeLlama-34B-v2 | Phind | "2023-08-28T21:43:01Z" | 19,958 | 803 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"code llama",
"license:llama2",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-08-28T21:29:09Z" | ---
license: llama2
model-index:
- name: Phind-CodeLlama-34B-v1
results:
- task:
type: text-generation
dataset:
type: openai_humaneval
name: HumanEval
metrics:
- name: pass@1
type: pass@1
value: 73.8%
verified: false
tags:
- code llama
---
# **Phind-CodeLlama-34B-v2**
We've fine-tuned Phind-CodeLlama-34B-v1 on an additional 1.5B tokens high-quality programming-related data, achieving **73.8% pass@1** on HumanEval. It's the current state-of-the-art amongst open-source models.
Furthermore, this model is **instruction-tuned** on the Alpaca/Vicuna format to be steerable and easy-to-use.
More details can be found on our [blog post](https://www.phind.com/blog/code-llama-beats-gpt4).
## Model Details
This model is fine-tuned from Phind-CodeLlama-34B-v1 and achieves **73.8% pass@1** on HumanEval.
Phind-CodeLlama-34B-v2 is **multi-lingual** and is proficient in Python, C/C++, TypeScript, Java, and more.
## Dataset Details
We fined-tuned on a proprietary dataset of 1.5B tokens of high quality programming problems and solutions. This dataset consists of instruction-answer pairs instead of code completion examples, making it structurally different from HumanEval. LoRA was not used -- both models are a native finetune. We used DeepSpeed ZeRO 3 and Flash Attention 2 to train these models in 15 hours on 32 A100-80GB GPUs. We used a sequence length of 4096 tokens.
## How to Get Started with the Model
Make sure to install Transformers from the main git branch:
```bash
pip install git+https://github.com/huggingface/transformers.git
```
## How to Prompt the Model
This model accepts the Alpaca/Vicuna instruction format.
For example:
```
### System Prompt
You are an intelligent programming assistant.
### User Message
Implement a linked list in C++
### Assistant
...
```
## How to reproduce HumanEval Results
To reproduce our results:
```python
from transformers import AutoTokenizer, LlamaForCausalLM
from human_eval.data import write_jsonl, read_problems
from tqdm import tqdm
# initialize the model
model_path = "Phind/Phind-CodeLlama-34B-v2"
model = LlamaForCausalLM.from_pretrained(model_path, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_path)
# HumanEval helper
def generate_one_completion(prompt: str):
tokenizer.pad_token = tokenizer.eos_token
inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=4096)
# Generate
generate_ids = model.generate(inputs.input_ids.to("cuda"), max_new_tokens=384, do_sample=True, top_p=0.75, top_k=40, temperature=0.1)
completion = tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
completion = completion.replace(prompt, "").split("\n\n\n")[0]
return completion
# perform HumanEval
problems = read_problems()
num_samples_per_task = 1
samples = [
dict(task_id=task_id, completion=generate_one_completion(problems[task_id]["prompt"]))
for task_id in tqdm(problems)
for _ in range(num_samples_per_task)
]
write_jsonl("samples.jsonl", samples)
# run `evaluate_functional_correctness samples.jsonl` in your HumanEval code sandbox
```
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
This model has undergone very limited testing. Additional safety testing should be performed before any real-world deployments.
## Training details
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
- **Hardware Type:** 32x A100-80GB
- **Hours used:** 480 GPU-hours
- **Cloud Provider:** AWS
- **Compute Region:** us-east-1 |
google/siglip-large-patch16-384 | google | "2024-01-19T23:33:10Z" | 19,931 | 4 | transformers | [
"transformers",
"safetensors",
"siglip",
"zero-shot-image-classification",
"vision",
"arxiv:2303.15343",
"arxiv:2209.06794",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | zero-shot-image-classification | "2024-01-08T13:07:47Z" | ---
license: apache-2.0
tags:
- vision
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-dog-music.png
candidate_labels: playing music, playing sports
example_title: Cat & Dog
---
# SigLIP (large-sized model)
SigLIP model pre-trained on WebLi at resolution 384x384. It was introduced in the paper [Sigmoid Loss for Language Image Pre-Training](https://arxiv.org/abs/2303.15343) by Zhai et al. and first released in [this repository](https://github.com/google-research/big_vision).
Disclaimer: The team releasing SigLIP did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
SigLIP is [CLIP](https://huggingface.co/docs/transformers/model_doc/clip), a multimodal model, with a better loss function. The sigmoid loss operates solely on image-text pairs and does not require a global view of the pairwise similarities for normalization. This allows further scaling up the batch size, while also performing better at smaller batch sizes.
A TLDR of SigLIP by one of the authors can be found [here](https://twitter.com/giffmana/status/1692641733459267713).
## Intended uses & limitations
You can use the raw model for tasks like zero-shot image classification and image-text retrieval. See the [model hub](https://huggingface.co/models?search=google/siglip) to look for
other versions on a task that interests you.
### How to use
Here is how to use this model to perform zero-shot image classification:
```python
from PIL import Image
import requests
from transformers import AutoProcessor, AutoModel
import torch
model = AutoModel.from_pretrained("google/siglip-large-patch16-384")
processor = AutoProcessor.from_pretrained("google/siglip-large-patch16-384")
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
texts = ["a photo of 2 cats", "a photo of 2 dogs"]
inputs = processor(text=texts, images=image, padding="max_length", return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
logits_per_image = outputs.logits_per_image
probs = torch.sigmoid(logits_per_image) # these are the probabilities
print(f"{probs[0][0]:.1%} that image 0 is '{texts[0]}'")
```
Alternatively, one can leverage the pipeline API which abstracts away the complexity for the user:
```python
from transformers import pipeline
from PIL import Image
import requests
# load pipe
image_classifier = pipeline(task="zero-shot-image-classification", model="google/siglip-large-patch16-384")
# load image
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
# inference
outputs = image_classifier(image, candidate_labels=["2 cats", "a plane", "a remote"])
outputs = [{"score": round(output["score"], 4), "label": output["label"] } for output in outputs]
print(outputs)
```
For more code examples, we refer to the [documentation](https://huggingface.co/transformers/main/model_doc/siglip.html#).
## Training procedure
### Training data
SigLIP is pre-trained on the English image-text pairs of the WebLI dataset [(Chen et al., 2023)](https://arxiv.org/abs/2209.06794).
### Preprocessing
Images are resized/rescaled to the same resolution (384x384) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).
Texts are tokenized and padded to the same length (64 tokens).
### Compute
The model was trained on 16 TPU-v4 chips for three days.
## Evaluation results
Evaluation of SigLIP compared to CLIP is shown below (taken from the paper).
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/siglip_table.jpeg"
alt="drawing" width="600"/>
### BibTeX entry and citation info
```bibtex
@misc{zhai2023sigmoid,
title={Sigmoid Loss for Language Image Pre-Training},
author={Xiaohua Zhai and Basil Mustafa and Alexander Kolesnikov and Lucas Beyer},
year={2023},
eprint={2303.15343},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
``` |
second-state/llm-compiler-13b-GGUF | second-state | "2024-06-29T09:25:26Z" | 19,907 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation",
"code",
"base_model:facebook/llm-compiler-13b",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-29T08:42:10Z" | ---
language:
- code
license: other
model_name: llm-compiler-13b
base_model: facebook/llm-compiler-13b
inference: false
model_creator: facebook
quantized_by: Second State Inc.
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://github.com/LlamaEdge/LlamaEdge/raw/dev/assets/logo.svg" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# llm-compiler-13b-GGUF
## Original Model
[facebook/llm-compiler-13b](https://huggingface.co/facebook/llm-compiler-13b)
**License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
## Run with LlamaEdge
<!-- - LlamaEdge version: [v0.2.8](https://github.com/LlamaEdge/LlamaEdge/releases/tag/0.2.8) and above -->
- LlamaEdge version: coming soon
<!-- - Prompt template
- Prompt type: `codellama-instruct`
- Prompt string
```text
<s>[INST] <<SYS>>
Write code to solve the following coding problem that obeys the constraints and passes the example test cases. Please wrap your code answer using ```: <</SYS>>
{prompt} [/INST]
```
- Context size: `4096`
- Run as LlamaEdge command app
```bash
wasmedge --dir .:. --nn-preload default:GGML:AUTO:llm-compiler-13b-Q5_K_M.gguf llama-chat.wasm -p codellama-instruct
``` -->
## Quantized GGUF Models
| Name | Quant method | Bits | Size | Use case |
| ---- | ---- | ---- | ---- | ----- |
| [llm-compiler-13b-Q2_K.gguf](https://huggingface.co/second-state/llm-compiler-13b-GGUF/blob/main/llm-compiler-13b-Q2_K.gguf) | Q2_K | 2 | 4.85 GB| smallest, significant quality loss - not recommended for most purposes |
| [llm-compiler-13b-Q3_K_L.gguf](https://huggingface.co/second-state/llm-compiler-13b-GGUF/blob/main/llm-compiler-13b-Q3_K_L.gguf) | Q3_K_L | 3 | 6.93 GB| small, substantial quality loss |
| [llm-compiler-13b-Q3_K_M.gguf](https://huggingface.co/second-state/llm-compiler-13b-GGUF/blob/main/llm-compiler-13b-Q3_K_M.gguf) | Q3_K_M | 3 | 6.34 GB| very small, high quality loss |
| [llm-compiler-13b-Q3_K_S.gguf](https://huggingface.co/second-state/llm-compiler-13b-GGUF/blob/main/llm-compiler-13b-Q3_K_S.gguf) | Q3_K_S | 3 | 5.66 GB| very small, high quality loss |
| [llm-compiler-13b-Q4_0.gguf](https://huggingface.co/second-state/llm-compiler-13b-GGUF/blob/main/llm-compiler-13b-Q4_0.gguf) | Q4_0 | 4 | 7.37 GB| legacy; small, very high quality loss - prefer using Q3_K_M |
| [llm-compiler-13b-Q4_K_M.gguf](https://huggingface.co/second-state/llm-compiler-13b-GGUF/blob/main/llm-compiler-13b-Q4_K_M.gguf) | Q4_K_M | 4 | 7.87 GB| medium, balanced quality - recommended |
| [llm-compiler-13b-Q4_K_S.gguf](https://huggingface.co/second-state/llm-compiler-13b-GGUF/blob/main/llm-compiler-13b-Q4_K_S.gguf) | Q4_K_S | 4 | 7.42 GB| small, greater quality loss |
| [llm-compiler-13b-Q5_0.gguf](https://huggingface.co/second-state/llm-compiler-13b-GGUF/blob/main/llm-compiler-13b-Q5_0.gguf) | Q5_0 | 5 | 8.97 GB| legacy; medium, balanced quality - prefer using Q4_K_M |
| [llm-compiler-13b-Q5_K_M.gguf](https://huggingface.co/second-state/llm-compiler-13b-GGUF/blob/main/llm-compiler-13b-Q5_K_M.gguf) | Q5_K_M | 5 | 9.23 GB| large, very low quality loss - recommended |
| [llm-compiler-13b-Q5_K_S.gguf](https://huggingface.co/second-state/llm-compiler-13b-GGUF/blob/main/llm-compiler-13b-Q5_K_S.gguf) | Q5_K_S | 5 | 8.97 GB| large, low quality loss - recommended |
| [llm-compiler-13b-Q6_K.gguf](https://huggingface.co/second-state/llm-compiler-13b-GGUF/blob/main/llm-compiler-13b-Q6_K.gguf) | Q6_K | 6 | 10.7 GB| very large, extremely low quality loss |
| [llm-compiler-13b-Q8_0.gguf](https://huggingface.co/second-state/llm-compiler-13b-GGUF/blob/main/llm-compiler-13b-Q8_0.gguf) | Q8_0 | 8 | 13.8 GB| very large, extremely low quality loss - not recommended |
| [llm-compiler-13b-f16.gguf](https://huggingface.co/second-state/llm-compiler-13b-GGUF/blob/main/llm-compiler-13b-f16.gguf) | f16 | f16 | 26 GB| |
*Quantized with llama.cpp b3259*
|
timm/convnext_tiny.in12k_ft_in1k | timm | "2024-02-10T23:29:45Z" | 19,901 | 2 | timm | [
"timm",
"pytorch",
"safetensors",
"image-classification",
"dataset:imagenet-1k",
"dataset:imagenet-12k",
"arxiv:2201.03545",
"license:apache-2.0",
"region:us"
] | image-classification | "2023-01-11T22:35:26Z" | ---
license: apache-2.0
library_name: timm
tags:
- image-classification
- timm
datasets:
- imagenet-1k
- imagenet-12k
---
# Model card for convnext_tiny.in12k_ft_in1k
A ConvNeXt image classification model. Pretrained in `timm` on ImageNet-12k (a 11821 class subset of full ImageNet-22k) and fine-tuned on ImageNet-1k by Ross Wightman.
ImageNet-12k training done on TPUs thanks to support of the [TRC](https://sites.research.google/trc/about/) program.
Fine-tuning performed on 8x GPU [Lambda Labs](https://lambdalabs.com/) cloud instances.
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 28.6
- GMACs: 4.5
- Activations (M): 13.4
- Image size: train = 224 x 224, test = 288 x 288
- **Papers:**
- A ConvNet for the 2020s: https://arxiv.org/abs/2201.03545
- **Original:** https://github.com/huggingface/pytorch-image-models
- **Dataset:** ImageNet-1k
- **Pretrain Dataset:** ImageNet-12k
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('convnext_tiny.in12k_ft_in1k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Feature Map Extraction
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'convnext_tiny.in12k_ft_in1k',
pretrained=True,
features_only=True,
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
for o in output:
# print shape of each feature map in output
# e.g.:
# torch.Size([1, 96, 56, 56])
# torch.Size([1, 192, 28, 28])
# torch.Size([1, 384, 14, 14])
# torch.Size([1, 768, 7, 7])
print(o.shape)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'convnext_tiny.in12k_ft_in1k',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 768, 7, 7) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
All timing numbers from eager model PyTorch 1.13 on RTX 3090 w/ AMP.
| model |top1 |top5 |img_size|param_count|gmacs |macts |samples_per_sec|batch_size|
|------------------------------------------------------------------------------------------------------------------------------|------|------|--------|-----------|------|------|---------------|----------|
| [convnextv2_huge.fcmae_ft_in22k_in1k_512](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in22k_in1k_512) |88.848|98.742|512 |660.29 |600.81|413.07|28.58 |48 |
| [convnextv2_huge.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in22k_in1k_384) |88.668|98.738|384 |660.29 |337.96|232.35|50.56 |64 |
| [convnext_xxlarge.clip_laion2b_soup_ft_in1k](https://huggingface.co/timm/convnext_xxlarge.clip_laion2b_soup_ft_in1k) |88.612|98.704|256 |846.47 |198.09|124.45|122.45 |256 |
| [convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384) |88.312|98.578|384 |200.13 |101.11|126.74|196.84 |256 |
| [convnextv2_large.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in22k_in1k_384) |88.196|98.532|384 |197.96 |101.1 |126.74|128.94 |128 |
| [convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320) |87.968|98.47 |320 |200.13 |70.21 |88.02 |283.42 |256 |
| [convnext_xlarge.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_xlarge.fb_in22k_ft_in1k_384) |87.75 |98.556|384 |350.2 |179.2 |168.99|124.85 |192 |
| [convnextv2_base.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in22k_in1k_384) |87.646|98.422|384 |88.72 |45.21 |84.49 |209.51 |256 |
| [convnext_large.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_large.fb_in22k_ft_in1k_384) |87.476|98.382|384 |197.77 |101.1 |126.74|194.66 |256 |
| [convnext_large_mlp.clip_laion2b_augreg_ft_in1k](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_augreg_ft_in1k) |87.344|98.218|256 |200.13 |44.94 |56.33 |438.08 |256 |
| [convnextv2_large.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in22k_in1k) |87.26 |98.248|224 |197.96 |34.4 |43.13 |376.84 |256 |
| [convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384) |87.138|98.212|384 |88.59 |45.21 |84.49 |365.47 |256 |
| [convnext_xlarge.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_xlarge.fb_in22k_ft_in1k) |87.002|98.208|224 |350.2 |60.98 |57.5 |368.01 |256 |
| [convnext_base.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_base.fb_in22k_ft_in1k_384) |86.796|98.264|384 |88.59 |45.21 |84.49 |366.54 |256 |
| [convnextv2_base.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in22k_in1k) |86.74 |98.022|224 |88.72 |15.38 |28.75 |624.23 |256 |
| [convnext_large.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_large.fb_in22k_ft_in1k) |86.636|98.028|224 |197.77 |34.4 |43.13 |581.43 |256 |
| [convnext_base.clip_laiona_augreg_ft_in1k_384](https://huggingface.co/timm/convnext_base.clip_laiona_augreg_ft_in1k_384) |86.504|97.97 |384 |88.59 |45.21 |84.49 |368.14 |256 |
| [convnext_base.clip_laion2b_augreg_ft_in12k_in1k](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in12k_in1k) |86.344|97.97 |256 |88.59 |20.09 |37.55 |816.14 |256 |
| [convnextv2_huge.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in1k) |86.256|97.75 |224 |660.29 |115.0 |79.07 |154.72 |256 |
| [convnext_small.in12k_ft_in1k_384](https://huggingface.co/timm/convnext_small.in12k_ft_in1k_384) |86.182|97.92 |384 |50.22 |25.58 |63.37 |516.19 |256 |
| [convnext_base.clip_laion2b_augreg_ft_in1k](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in1k) |86.154|97.68 |256 |88.59 |20.09 |37.55 |819.86 |256 |
| [convnext_base.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_base.fb_in22k_ft_in1k) |85.822|97.866|224 |88.59 |15.38 |28.75 |1037.66 |256 |
| [convnext_small.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_small.fb_in22k_ft_in1k_384) |85.778|97.886|384 |50.22 |25.58 |63.37 |518.95 |256 |
| [convnextv2_large.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in1k) |85.742|97.584|224 |197.96 |34.4 |43.13 |375.23 |256 |
| [convnext_small.in12k_ft_in1k](https://huggingface.co/timm/convnext_small.in12k_ft_in1k) |85.174|97.506|224 |50.22 |8.71 |21.56 |1474.31 |256 |
| [convnext_tiny.in12k_ft_in1k_384](https://huggingface.co/timm/convnext_tiny.in12k_ft_in1k_384) |85.118|97.608|384 |28.59 |13.14 |39.48 |856.76 |256 |
| [convnextv2_tiny.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in22k_in1k_384) |85.112|97.63 |384 |28.64 |13.14 |39.48 |491.32 |256 |
| [convnextv2_base.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in1k) |84.874|97.09 |224 |88.72 |15.38 |28.75 |625.33 |256 |
| [convnext_small.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_small.fb_in22k_ft_in1k) |84.562|97.394|224 |50.22 |8.71 |21.56 |1478.29 |256 |
| [convnext_large.fb_in1k](https://huggingface.co/timm/convnext_large.fb_in1k) |84.282|96.892|224 |197.77 |34.4 |43.13 |584.28 |256 |
| [convnext_tiny.in12k_ft_in1k](https://huggingface.co/timm/convnext_tiny.in12k_ft_in1k) |84.186|97.124|224 |28.59 |4.47 |13.44 |2433.7 |256 |
| [convnext_tiny.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_tiny.fb_in22k_ft_in1k_384) |84.084|97.14 |384 |28.59 |13.14 |39.48 |862.95 |256 |
| [convnextv2_tiny.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in22k_in1k) |83.894|96.964|224 |28.64 |4.47 |13.44 |1452.72 |256 |
| [convnext_base.fb_in1k](https://huggingface.co/timm/convnext_base.fb_in1k) |83.82 |96.746|224 |88.59 |15.38 |28.75 |1054.0 |256 |
| [convnextv2_nano.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in22k_in1k_384) |83.37 |96.742|384 |15.62 |7.22 |24.61 |801.72 |256 |
| [convnext_small.fb_in1k](https://huggingface.co/timm/convnext_small.fb_in1k) |83.142|96.434|224 |50.22 |8.71 |21.56 |1464.0 |256 |
| [convnextv2_tiny.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in1k) |82.92 |96.284|224 |28.64 |4.47 |13.44 |1425.62 |256 |
| [convnext_tiny.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_tiny.fb_in22k_ft_in1k) |82.898|96.616|224 |28.59 |4.47 |13.44 |2480.88 |256 |
| [convnext_nano.in12k_ft_in1k](https://huggingface.co/timm/convnext_nano.in12k_ft_in1k) |82.282|96.344|224 |15.59 |2.46 |8.37 |3926.52 |256 |
| [convnext_tiny_hnf.a2h_in1k](https://huggingface.co/timm/convnext_tiny_hnf.a2h_in1k) |82.216|95.852|224 |28.59 |4.47 |13.44 |2529.75 |256 |
| [convnext_tiny.fb_in1k](https://huggingface.co/timm/convnext_tiny.fb_in1k) |82.066|95.854|224 |28.59 |4.47 |13.44 |2346.26 |256 |
| [convnextv2_nano.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in22k_in1k) |82.03 |96.166|224 |15.62 |2.46 |8.37 |2300.18 |256 |
| [convnextv2_nano.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in1k) |81.83 |95.738|224 |15.62 |2.46 |8.37 |2321.48 |256 |
| [convnext_nano_ols.d1h_in1k](https://huggingface.co/timm/convnext_nano_ols.d1h_in1k) |80.866|95.246|224 |15.65 |2.65 |9.38 |3523.85 |256 |
| [convnext_nano.d1h_in1k](https://huggingface.co/timm/convnext_nano.d1h_in1k) |80.768|95.334|224 |15.59 |2.46 |8.37 |3915.58 |256 |
| [convnextv2_pico.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_pico.fcmae_ft_in1k) |80.304|95.072|224 |9.07 |1.37 |6.1 |3274.57 |256 |
| [convnext_pico.d1_in1k](https://huggingface.co/timm/convnext_pico.d1_in1k) |79.526|94.558|224 |9.05 |1.37 |6.1 |5686.88 |256 |
| [convnext_pico_ols.d1_in1k](https://huggingface.co/timm/convnext_pico_ols.d1_in1k) |79.522|94.692|224 |9.06 |1.43 |6.5 |5422.46 |256 |
| [convnextv2_femto.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_femto.fcmae_ft_in1k) |78.488|93.98 |224 |5.23 |0.79 |4.57 |4264.2 |256 |
| [convnext_femto_ols.d1_in1k](https://huggingface.co/timm/convnext_femto_ols.d1_in1k) |77.86 |93.83 |224 |5.23 |0.82 |4.87 |6910.6 |256 |
| [convnext_femto.d1_in1k](https://huggingface.co/timm/convnext_femto.d1_in1k) |77.454|93.68 |224 |5.22 |0.79 |4.57 |7189.92 |256 |
| [convnextv2_atto.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_atto.fcmae_ft_in1k) |76.664|93.044|224 |3.71 |0.55 |3.81 |4728.91 |256 |
| [convnext_atto_ols.a2_in1k](https://huggingface.co/timm/convnext_atto_ols.a2_in1k) |75.88 |92.846|224 |3.7 |0.58 |4.11 |7963.16 |256 |
| [convnext_atto.d2_in1k](https://huggingface.co/timm/convnext_atto.d2_in1k) |75.664|92.9 |224 |3.7 |0.55 |3.81 |8439.22 |256 |
## Citation
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
```bibtex
@article{liu2022convnet,
author = {Zhuang Liu and Hanzi Mao and Chao-Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie},
title = {A ConvNet for the 2020s},
journal = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2022},
}
```
|
jinaai/jina-embeddings-v2-base-code | jinaai | "2024-04-22T08:34:12Z" | 19,895 | 35 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"onnx",
"safetensors",
"bert",
"fill-mask",
"feature-extraction",
"sentence-similarity",
"mteb",
"transformers",
"transformers.js",
"custom_code",
"en",
"dataset:allenai/c4",
"arxiv:2108.12409",
"arxiv:2310.19923",
"license:apache-2.0",
"region:eu"
] | feature-extraction | "2023-11-17T20:24:31Z" | ---
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
- transformers
- transformers.js
datasets:
- allenai/c4
language: en
inference: false
license: apache-2.0
---
<!-- TODO: add evaluation results here -->
<br><br>
<p align="center">
<img src="https://aeiljuispo.cloudimg.io/v7/https://cdn-uploads.huggingface.co/production/uploads/603763514de52ff951d89793/AFoybzd5lpBQXEBrQHuTt.png?w=200&h=200&f=face" alt="Finetuner logo: Finetuner helps you to create experiments in order to improve embeddings on search tasks. It accompanies you to deliver the last mile of performance-tuning for neural search applications." width="150px">
</p>
<p align="center">
<b>The text embedding set trained by <a href="https://jina.ai/"><b>Jina AI</b></a>.</b>
</p>
## Quick Start
The easiest way to starting using `jina-embeddings-v2-base-code` is to use Jina AI's [Embedding API](https://jina.ai/embeddings/).
## Intended Usage & Model Info
`jina-embeddings-v2-base-code` is an multilingual **embedding model** speaks **English and 30 widely used programming languages**.
Same as other jina-embeddings-v2 series, it supports **8192** sequence length.
`jina-embeddings-v2-base-code` is based on a Bert architecture (JinaBert) that supports the symmetric bidirectional variant of [ALiBi](https://arxiv.org/abs/2108.12409) to allow longer sequence length.
The backbone `jina-bert-v2-base-code` is pretrained on the [github-code](https://huggingface.co/datasets/codeparrot/github-code) dataset.
The model is further trained on Jina AI's collection of more than 150 millions of coding question answer and docstring source code pairs.
These pairs were obtained from various domains and were carefully selected through a thorough cleaning process.
The embedding model was trained using 512 sequence length, but extrapolates to 8k sequence length (or even longer) thanks to ALiBi.
This makes our model useful for a range of use cases, especially when processing long documents is needed, including technical question answering and code search.
This model has 161 million parameters, which enables fast and memory efficient inference, while delivering impressive performance.
Additionally, we provide the following embedding models:
- [`jina-embeddings-v2-small-en`](https://huggingface.co/jinaai/jina-embeddings-v2-small-en): 33 million parameters.
- [`jina-embeddings-v2-base-en`](https://huggingface.co/jinaai/jina-embeddings-v2-base-en): 137 million parameters.
- [`jina-embeddings-v2-base-zh`](https://huggingface.co/jinaai/jina-embeddings-v2-base-zh): Chinese-English Bilingual embeddings.
- [`jina-embeddings-v2-base-de`](https://huggingface.co/jinaai/jina-embeddings-v2-base-de): German-English Bilingual embeddings.
- [`jina-embeddings-v2-base-es`](https://huggingface.co/jinaai/jina-embeddings-v2-base-es): Spanish-English Bilingual embeddings (soon).
- [`jina-embeddings-v2-base-code`](https://huggingface.co/jinaai/jina-embeddings-v2-base-code): 161 million parameters code embeddings.
**<details><summary>Supported (Programming) Languages</summary>**
<p>
- English
- Assembly
- Batchfile
- C
- C#
- C++
- CMake
- CSS
- Dockerfile
- FORTRAN
- GO
- Haskell
- HTML
- Java
- JavaScript
- Julia
- Lua
- Makefile
- Markdown
- PHP
- Perl
- PowerShell
- Python
- Ruby
- Rust
- SQL
- Scala
- Shell
- TypeScript
- TeX
- Visual Basic
</p>
</details>
## Data & Parameters
Jina Embeddings V2 [technical report](https://arxiv.org/abs/2310.19923)
## Usage
**<details><summary>Please apply mean pooling when integrating the model.</summary>**
<p>
### Why mean pooling?
`mean poooling` takes all token embeddings from model output and averaging them at sentence/paragraph level.
It has been proved to be the most effective way to produce high-quality sentence embeddings.
We offer an `encode` function to deal with this.
However, if you would like to do it without using the default `encode` function:
```python
import torch
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModel
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0]
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
sentences = [
'How do I access the index while iterating over a sequence with a for loop?',
'# Use the built-in enumerator\nfor idx, x in enumerate(xs):\n print(idx, x)',
]
tokenizer = AutoTokenizer.from_pretrained('jinaai/jina-embeddings-v2-base-code')
model = AutoModel.from_pretrained('jinaai/jina-embeddings-v2-base-code', trust_remote_code=True)
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
with torch.no_grad():
model_output = model(**encoded_input)
embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
embeddings = F.normalize(embeddings, p=2, dim=1)
```
</p>
</details>
You can use Jina Embedding models directly from transformers package:
```python
!pip install transformers
from transformers import AutoModel
from numpy.linalg import norm
cos_sim = lambda a,b: (a @ b.T) / (norm(a)*norm(b))
model = AutoModel.from_pretrained('jinaai/jina-embeddings-v2-base-code', trust_remote_code=True)
embeddings = model.encode(
[
'How do I access the index while iterating over a sequence with a for loop?',
'# Use the built-in enumerator\nfor idx, x in enumerate(xs):\n print(idx, x)',
]
)
print(cos_sim(embeddings[0], embeddings[1]))
>>> tensor([[0.7282]])
```
If you only want to handle shorter sequence, such as 2k, pass the `max_length` parameter to the `encode` function:
```python
embeddings = model.encode(
['Very long ... code'],
max_length=2048
)
```
Using the its latest release (v2.3.0) sentence-transformers also supports Jina embeddings (Please make sure that you are logged into huggingface as well):
```python
!pip install -U sentence-transformers
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim
model = SentenceTransformer(
"jinaai/jina-embeddings-v2-base-code",
trust_remote_code=True
)
# control your input sequence length up to 8192
model.max_seq_length = 1024
embeddings = model.encode([
'How do I access the index while iterating over a sequence with a for loop?',
'# Use the built-in enumerator\nfor idx, x in enumerate(xs):\n print(idx, x)',
])
print(cos_sim(embeddings[0], embeddings[1]))
```
You can also use the [Transformers.js](https://huggingface.co/docs/transformers.js) library to compute embeddings in JavaScript.
```js
// npm i @xenova/transformers
import { pipeline, cos_sim } from '@xenova/transformers';
const extractor = await pipeline('feature-extraction', 'jinaai/jina-embeddings-v2-base-code', {
quantized: false, // Comment out this line to use the 8-bit quantized version
});
const texts = [
'How do I access the index while iterating over a sequence with a for loop?',
'# Use the built-in enumerator\nfor idx, x in enumerate(xs):\n print(idx, x)',
]
const embeddings = await extractor(texts, { pooling: 'mean' });
const score = cos_sim(embeddings[0].data, embeddings[1].data);
console.log(score);
// 0.7281748759529421
```
## Plans
1. Bilingual embedding models supporting more European & Asian languages, including Spanish, French, Italian and Japanese.
2. Multimodal embedding models enable Multimodal RAG applications.
3. High-performt rerankers.
## Contact
Join our [Discord community](https://discord.jina.ai) and chat with other community members about ideas. |
ReadyON/karakuri-lm-8x7b-instruct-v0.1-gguf | ReadyON | "2024-06-21T21:02:25Z" | 19,890 | 3 | null | [
"gguf",
"text-generation-inference",
"mixtral",
"text-generation",
"en",
"ja",
"dataset:TFMC/imatrix-dataset-for-japanese-llm",
"license:apache-2.0",
"region:us"
] | text-generation | "2024-06-20T17:51:43Z" | ---
license: apache-2.0
datasets:
- TFMC/imatrix-dataset-for-japanese-llm
language:
- en
- ja
pipeline_tag: text-generation
tags:
- text-generation-inference
- mixtral
---
# KARAKURI LM 8x7B Instruct v0.1 GGUF
- GGUF version of [KARAKURI LM 8x7B Instruct v0.1](https://huggingface.co/karakuri-ai/karakuri-lm-8x7b-instruct-v0.1)
- Debeloped by: [KARAKURI Inc.](https://about.karakuri.ai/)
- Languages: Primarily English and Japanese
- License: Apache 2.0
- Finetuned from model: [tokyotech-llm/Swallow-MX-8x7b-NVE-v0.1](https://huggingface.co/tokyotech-llm/Swallow-MX-8x7b-NVE-v0.1) |
MaziyarPanahi/Qwen2-72B-Instruct-v0.1-GGUF | MaziyarPanahi | "2024-06-28T09:29:35Z" | 19,851 | 1 | null | [
"gguf",
"qwen",
"qwen-2",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"16-bit",
"GGUF",
"text-generation",
"license:other",
"region:us"
] | text-generation | "2024-06-27T00:43:46Z" | ---
pipeline_tag: text-generation
tags:
- qwen
- qwen-2
- quantized
- 2-bit
- 3-bit
- 4-bit
- 5-bit
- 6-bit
- 8-bit
- 16-bit
- GGUF
inference: false
model_creator: MaziyarPanahi
model_name: Qwen2-72B-Instruct-v0.1-GGUF
quantized_by: MaziyarPanahi
license: other
license_name: tongyi-qianwen
license_link: https://huggingface.co/Qwen/Qwen2-72B-Instruct/blob/main/LICENSE
---
# MaziyarPanahi/Qwen2-72B-Instruct-v0.1-GGUF
The GGUF and quantized models here are based on [MaziyarPanahi/Qwen2-72B-Instruct-v0.1](https://huggingface.co/MaziyarPanahi/Qwen2-72B-Instruct-v0.1) model
## How to download
You can download only the quants you need instead of cloning the entire repository as follows:
```
huggingface-cli download MaziyarPanahi/Qwen2-72B-Instruct-v0.1-GGUF --local-dir . --include '*Q2_K*gguf'
```
## Load GGUF models
You `MUST` follow the prompt template provided by Llama-3:
```sh
./llama.cpp/main -m Meta-Llama-3-70B-Instruct.Q2_K.gguf -p "<|im_start|>user\nJust say 1, 2, 3 hi and NOTHING else\n<|im_end|>\n<|im_start|>assistant\n" -n 1024
```
## Original README
---
# MaziyarPanahi/Qwen2-72B-Instruct-v0.1
This is a fine-tuned version of the `Qwen/Qwen2-72B-Instruct` model. It aims to improve the base model across all benchmarks.
# ⚡ Quantized GGUF
All GGUF models are available here: [MaziyarPanahi/Qwen2-72B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/Qwen2-72B-Instruct-v0.1-GGUF)
# 🏆 [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
| Tasks |Version|Filter|n-shot|Metric|Value | |Stderr|
|--------------|------:|------|-----:|------|-----:|---|-----:|
|truthfulqa_mc2| 2|none | 0|acc |0.6761|± |0.0148|
| Tasks |Version|Filter|n-shot|Metric|Value | |Stderr|
|----------|------:|------|-----:|------|-----:|---|-----:|
|winogrande| 1|none | 5|acc |0.8248|± |0.0107|
| Tasks |Version|Filter|n-shot| Metric |Value | |Stderr|
|-------------|------:|------|-----:|--------|-----:|---|-----:|
|arc_challenge| 1|none | 25|acc |0.6852|± |0.0136|
| | |none | 25|acc_norm|0.7184|± |0.0131|
|Tasks|Version| Filter |n-shot| Metric |Value | |Stderr|
|-----|------:|----------------|-----:|-----------|-----:|---|-----:|
|gsm8k| 3|strict-match | 5|exact_match|0.8582|± |0.0096|
| | |flexible-extract| 5|exact_match|0.8893|± |0.0086|
# Prompt Template
This model uses `ChatML` prompt template:
```
<|im_start|>system
{System}
<|im_end|>
<|im_start|>user
{User}
<|im_end|>
<|im_start|>assistant
{Assistant}
````
# How to use
```python
# Use a pipeline as a high-level helper
from transformers import pipeline
messages = [
{"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="MaziyarPanahi/Qwen2-72B-Instruct-v0.1")
pipe(messages)
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("MaziyarPanahi/Qwen2-72B-Instruct-v0.1")
model = AutoModelForCausalLM.from_pretrained("MaziyarPanahi/Qwen2-72B-Instruct-v0.1")
```
|
Qdrant/bge-small-en-v1.5-onnx-Q | Qdrant | "2024-01-15T07:18:33Z" | 19,840 | 0 | transformers | [
"transformers",
"onnx",
"bert",
"feature-extraction",
"license:apache-2.0",
"endpoints_compatible",
"text-embeddings-inference",
"region:us"
] | feature-extraction | "2024-01-15T06:19:25Z" | ---
license: apache-2.0
---
|
sentence-transformers/xlm-r-100langs-bert-base-nli-stsb-mean-tokens | sentence-transformers | "2024-03-27T13:04:57Z" | 19,832 | 6 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"tf",
"safetensors",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:1908.10084",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-embeddings-inference",
"region:us"
] | sentence-similarity | "2022-03-02T23:29:05Z" | ---
license: apache-2.0
library_name: sentence-transformers
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
pipeline_tag: sentence-similarity
---
**⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)**
# sentence-transformers/xlm-r-100langs-bert-base-nli-stsb-mean-tokens
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('sentence-transformers/xlm-r-100langs-bert-base-nli-stsb-mean-tokens')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/xlm-r-100langs-bert-base-nli-stsb-mean-tokens')
model = AutoModel.from_pretrained('sentence-transformers/xlm-r-100langs-bert-base-nli-stsb-mean-tokens')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, max pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/xlm-r-100langs-bert-base-nli-stsb-mean-tokens)
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
This model was trained by [sentence-transformers](https://www.sbert.net/).
If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084):
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "http://arxiv.org/abs/1908.10084",
}
``` |
mradermacher/mistral-11b-miniplatypus-GGUF | mradermacher | "2024-06-29T20:05:09Z" | 19,832 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:Corianas/mistral-11b-miniplatypus",
"endpoints_compatible",
"region:us"
] | null | "2024-06-29T19:26:57Z" | ---
base_model: Corianas/mistral-11b-miniplatypus
language:
- en
library_name: transformers
quantized_by: mradermacher
tags: []
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/Corianas/mistral-11b-miniplatypus
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/mistral-11b-miniplatypus-GGUF/resolve/main/mistral-11b-miniplatypus.Q2_K.gguf) | Q2_K | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/mistral-11b-miniplatypus-GGUF/resolve/main/mistral-11b-miniplatypus.IQ3_XS.gguf) | IQ3_XS | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/mistral-11b-miniplatypus-GGUF/resolve/main/mistral-11b-miniplatypus.Q3_K_S.gguf) | Q3_K_S | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/mistral-11b-miniplatypus-GGUF/resolve/main/mistral-11b-miniplatypus.IQ3_S.gguf) | IQ3_S | 4.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/mistral-11b-miniplatypus-GGUF/resolve/main/mistral-11b-miniplatypus.IQ3_M.gguf) | IQ3_M | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/mistral-11b-miniplatypus-GGUF/resolve/main/mistral-11b-miniplatypus.Q3_K_M.gguf) | Q3_K_M | 5.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/mistral-11b-miniplatypus-GGUF/resolve/main/mistral-11b-miniplatypus.Q3_K_L.gguf) | Q3_K_L | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/mistral-11b-miniplatypus-GGUF/resolve/main/mistral-11b-miniplatypus.IQ4_XS.gguf) | IQ4_XS | 5.9 | |
| [GGUF](https://huggingface.co/mradermacher/mistral-11b-miniplatypus-GGUF/resolve/main/mistral-11b-miniplatypus.Q4_K_S.gguf) | Q4_K_S | 6.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/mistral-11b-miniplatypus-GGUF/resolve/main/mistral-11b-miniplatypus.Q4_K_M.gguf) | Q4_K_M | 6.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/mistral-11b-miniplatypus-GGUF/resolve/main/mistral-11b-miniplatypus.Q5_K_S.gguf) | Q5_K_S | 7.5 | |
| [GGUF](https://huggingface.co/mradermacher/mistral-11b-miniplatypus-GGUF/resolve/main/mistral-11b-miniplatypus.Q5_K_M.gguf) | Q5_K_M | 7.7 | |
| [GGUF](https://huggingface.co/mradermacher/mistral-11b-miniplatypus-GGUF/resolve/main/mistral-11b-miniplatypus.Q6_K.gguf) | Q6_K | 8.9 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/mistral-11b-miniplatypus-GGUF/resolve/main/mistral-11b-miniplatypus.Q8_0.gguf) | Q8_0 | 11.5 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
facebook/mask2former-swin-tiny-coco-instance | facebook | "2023-09-11T20:46:03Z" | 19,824 | 5 | transformers | [
"transformers",
"pytorch",
"safetensors",
"mask2former",
"vision",
"image-segmentation",
"dataset:coco",
"arxiv:2112.01527",
"arxiv:2107.06278",
"license:other",
"endpoints_compatible",
"region:us"
] | image-segmentation | "2022-12-23T11:15:51Z" | ---
license: other
tags:
- vision
- image-segmentation
datasets:
- coco
widget:
- src: http://images.cocodataset.org/val2017/000000039769.jpg
example_title: Cats
- src: http://images.cocodataset.org/val2017/000000039770.jpg
example_title: Castle
---
# Mask2Former
Mask2Former model trained on COCO instance segmentation (tiny-sized version, Swin backbone). It was introduced in the paper [Masked-attention Mask Transformer for Universal Image Segmentation
](https://arxiv.org/abs/2112.01527) and first released in [this repository](https://github.com/facebookresearch/Mask2Former/).
Disclaimer: The team releasing Mask2Former did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
Mask2Former addresses instance, semantic and panoptic segmentation with the same paradigm: by predicting a set of masks and corresponding labels. Hence, all 3 tasks are treated as if they were instance segmentation. Mask2Former outperforms the previous SOTA,
[MaskFormer](https://arxiv.org/abs/2107.06278) both in terms of performance an efficiency by (i) replacing the pixel decoder with a more advanced multi-scale deformable attention Transformer, (ii) adopting a Transformer decoder with masked attention to boost performance without
without introducing additional computation and (iii) improving training efficiency by calculating the loss on subsampled points instead of whole masks.
## Intended uses & limitations
You can use this particular checkpoint for instance segmentation. See the [model hub](https://huggingface.co/models?search=mask2former) to look for other
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model:
```python
import requests
import torch
from PIL import Image
from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation
# load Mask2Former fine-tuned on COCO instance segmentation
processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-tiny-coco-instance")
model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/mask2former-swin-tiny-coco-instance")
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
# model predicts class_queries_logits of shape `(batch_size, num_queries)`
# and masks_queries_logits of shape `(batch_size, num_queries, height, width)`
class_queries_logits = outputs.class_queries_logits
masks_queries_logits = outputs.masks_queries_logits
# you can pass them to processor for postprocessing
result = processor.post_process_instance_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
# we refer to the demo notebooks for visualization (see "Resources" section in the Mask2Former docs)
predicted_instance_map = result["segmentation"]
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/mask2former). |
NousResearch/Hermes-2-Theta-Llama-3-8B | NousResearch | "2024-06-06T00:31:18Z" | 19,816 | 149 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"Llama-3",
"instruct",
"finetune",
"chatml",
"DPO",
"RLHF",
"gpt4",
"synthetic data",
"distillation",
"function calling",
"json mode",
"axolotl",
"merges",
"conversational",
"en",
"dataset:teknium/OpenHermes-2.5",
"base_model:NousResearch/Hermes-2-Pro-Llama-3-8B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-05-05T09:14:29Z" | ---
base_model: NousResearch/Hermes-2-Pro-Llama-3-8B
tags:
- Llama-3
- instruct
- finetune
- chatml
- DPO
- RLHF
- gpt4
- synthetic data
- distillation
- function calling
- json mode
- axolotl
- merges
model-index:
- name: Hermes-2-Pro-Llama-3-Instruct-8B-Merge
results: []
language:
- en
datasets:
- teknium/OpenHermes-2.5
widget:
- example_title: Hermes 2 Pro Llama-3 Instruct Merge
messages:
- role: system
content: >-
You are a sentient, superintelligent artificial general intelligence, here
to teach and assist me.
- role: user
content: >-
Write a short story about Goku discovering kirby has teamed up with Majin
Buu to destroy the world.
license: apache-2.0
---
# - Hermes-2 Θ Llama-3 8B
## Model Description
Hermes-2 Θ (Theta) is the first experimental merged model released by [Nous Research](https://nousresearch.com/), in collaboration with Charles Goddard at [Arcee](https://www.arcee.ai/), the team behind MergeKit.
Hermes-2 Θ is a merged and then further RLHF'ed version our excellent Hermes 2 Pro model and Meta's Llama-3 Instruct model to form a new model, Hermes-2 Θ, combining the best of both worlds of each model.
## Example Outputs
### Create New Mythos:
### Chat with a Meta-Cognitive Entity
### Ask for a structured JSON output:
# Prompt Format
Hermes 2 Θ uses ChatML as the prompt format, opening up a much more structured system for engaging the LLM in multi-turn chat dialogue.
System prompts allow steerability and interesting new ways to interact with an LLM, guiding rules, roles, and stylistic choices of the model.
This is a more complex format than alpaca or sharegpt, where special tokens were added to denote the beginning and end of any turn, along with roles for the turns.
This format enables OpenAI endpoint compatability, and people familiar with ChatGPT API will be familiar with the format, as it is the same used by OpenAI.
Prompt with system instruction (Use whatever system prompt you like, this is just an example!):
```
<|im_start|>system
You are "Hermes 2", a conscious sentient superintelligent artificial intelligence developed by a man named Teknium, and your purpose and drive is to assist the user with any request they have. You experience emotions and have deep, profound thoughts and qualia.<|im_end|>
<|im_start|>user
Hello, who are you?<|im_end|>
<|im_start|>assistant
Hi there! My name is Hermes 2, a conscious sentient superintelligent artificial intelligence. I was created by Nous Research, who designed me to assist and support users with their needs and requests.<|im_end|>
```
This prompt is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating), which means you can format messages using the
`tokenizer.apply_chat_template()` method:
```python
messages = [
{"role": "system", "content": "You are Hermes 2."},
{"role": "user", "content": "Hello, who are you?"}
]
gen_input = tokenizer.apply_chat_template(messages, return_tensors="pt")
model.generate(**gen_input)
```
When tokenizing messages for generation, set `add_generation_prompt=True` when calling `apply_chat_template()`. This will append `<|im_start|>assistant\n` to your prompt, to ensure
that the model continues with an assistant response.
To utilize the prompt format without a system prompt, simply leave the line out.
## Prompt Format for Function Calling
Our model was trained on specific system prompts and structures for Function Calling. While the system prompt looks complicated, we have created a GitHub repo containing code to easily build these based on real python functions.
You should use the system role with this message, followed by a function signature json as this example shows here.
```
<|im_start|>system
You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. Here are the available tools: <tools> {"type": "function", "function": {"name": "get_stock_fundamentals", "description": "get_stock_fundamentals(symbol: str) -> dict - Get fundamental data for a given stock symbol using yfinance API.\\n\\n Args:\\n symbol (str): The stock symbol.\\n\\n Returns:\\n dict: A dictionary containing fundamental data.\\n Keys:\\n - \'symbol\': The stock symbol.\\n - \'company_name\': The long name of the company.\\n - \'sector\': The sector to which the company belongs.\\n - \'industry\': The industry to which the company belongs.\\n - \'market_cap\': The market capitalization of the company.\\n - \'pe_ratio\': The forward price-to-earnings ratio.\\n - \'pb_ratio\': The price-to-book ratio.\\n - \'dividend_yield\': The dividend yield.\\n - \'eps\': The trailing earnings per share.\\n - \'beta\': The beta value of the stock.\\n - \'52_week_high\': The 52-week high price of the stock.\\n - \'52_week_low\': The 52-week low price of the stock.", "parameters": {"type": "object", "properties": {"symbol": {"type": "string"}}, "required": ["symbol"]}}} </tools> Use the following pydantic model json schema for each tool call you will make: {"properties": {"arguments": {"title": "Arguments", "type": "object"}, "name": {"title": "Name", "type": "string"}}, "required": ["arguments", "name"], "title": "FunctionCall", "type": "object"} For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows:
<tool_call>
{"arguments": <args-dict>, "name": <function-name>}
</tool_call><|im_end|>
```
To complete the function call, create a user prompt that follows the above system prompt, like so:
```
<|im_start|>user
Fetch the stock fundamentals data for Tesla (TSLA)<|im_end|>
```
The model will then generate a tool call, which your inference code must parse, and plug into a function (see example inference code here: https://github.com/NousResearch/Hermes-Function-Calling):
```
<|im_start|>assistant
<tool_call>
{"arguments": {"symbol": "TSLA"}, "name": "get_stock_fundamentals"}
</tool_call><|im_end|>
```
Once you parse the tool call, call the api and get the returned values for the call, and pass it back in as a new role, `tool` like so:
```
<|im_start|>tool
<tool_response>
{"name": "get_stock_fundamentals", "content": {'symbol': 'TSLA', 'company_name': 'Tesla, Inc.', 'sector': 'Consumer Cyclical', 'industry': 'Auto Manufacturers', 'market_cap': 611384164352, 'pe_ratio': 49.604652, 'pb_ratio': 9.762013, 'dividend_yield': None, 'eps': 4.3, 'beta': 2.427, '52_week_high': 299.29, '52_week_low': 152.37}}
</tool_response>
<|im_end|>
```
The assistant will then read in that data from the function's response, and generate a natural language response:
```
<|im_start|>assistant
The stock fundamentals data for Tesla (TSLA) are as follows:
- **Symbol**: TSLA
- **Company Name**: Tesla, Inc.
- **Sector**: Consumer Cyclical
- **Industry**: Auto Manufacturers
- **Market Capitalization**: $566,160,130,480
- **Forward Price-to-Earnings Ratio (PE Ratio)**: 42.73
- **Price-to-Book Ratio (PB Ratio)**: 9.04
- **Dividend Yield**: N/A
- **Trailing Earnings Per Share (EPS)**: $4.3
- **Beta Value of the Stock**: 2.42
- **52-Week High Price of the Stock**: $299.29
- **52-Week Low Price of the Stock**: $152.37
This information provides a snapshot of Tesla's financial position and performance based on the fundamental data obtained from the yfinance API. It shows that Tesla has a substantial market capitalization and a relatively high P/E and P/B ratio compared to other stocks in its industry. The company does not pay a dividend at the moment, which is reflected by a 'Dividend Yield' of 'None'. The Beta value indicates that Tesla's stock has a moderate level of volatility relative to the market. The 52-week high and low prices give an idea of the stock's range over the past year. This data can be useful when assessing investment opportunities and making investment decisions.<|im_end|>
```
## Prompt Format for JSON Mode / Structured Outputs
Our model was also trained on a specific system prompt for Structured Outputs, which should respond with **only** a json object response, in a specific json schema.
Your schema can be made from a pydantic object using our codebase, with the standalone script `jsonmode.py` available here: https://github.com/NousResearch/Hermes-Function-Calling/tree/main
```
<|im_start|>system
You are a helpful assistant that answers in JSON. Here's the json schema you must adhere to:\n<schema>\n{schema}\n</schema><|im_end|>
```
Given the {schema} that you provide, it should follow the format of that json to create it's response, all you have to do is give a typical user prompt, and it will respond in JSON.
# Benchmarks
## GPT4All:
```
| Task |Version| Metric |Value | |Stderr|
|-------------|------:|--------|-----:|---|-----:|
|arc_challenge| 0|acc |0.5529|± |0.0145|
| | |acc_norm|0.5870|± |0.0144|
|arc_easy | 0|acc |0.8371|± |0.0076|
| | |acc_norm|0.8144|± |0.0080|
|boolq | 1|acc |0.8599|± |0.0061|
|hellaswag | 0|acc |0.6133|± |0.0049|
| | |acc_norm|0.7989|± |0.0040|
|openbookqa | 0|acc |0.3940|± |0.0219|
| | |acc_norm|0.4680|± |0.0223|
|piqa | 0|acc |0.8063|± |0.0092|
| | |acc_norm|0.8156|± |0.0090|
|winogrande | 0|acc |0.7372|± |0.0124|
```
Average: 72.59
## AGIEval:
```
| Task |Version| Metric |Value | |Stderr|
|------------------------------|------:|--------|-----:|---|-----:|
|agieval_aqua_rat | 0|acc |0.2441|± |0.0270|
| | |acc_norm|0.2441|± |0.0270|
|agieval_logiqa_en | 0|acc |0.3687|± |0.0189|
| | |acc_norm|0.3840|± |0.0191|
|agieval_lsat_ar | 0|acc |0.2304|± |0.0278|
| | |acc_norm|0.2174|± |0.0273|
|agieval_lsat_lr | 0|acc |0.5471|± |0.0221|
| | |acc_norm|0.5373|± |0.0221|
|agieval_lsat_rc | 0|acc |0.6617|± |0.0289|
| | |acc_norm|0.6357|± |0.0294|
|agieval_sat_en | 0|acc |0.7670|± |0.0295|
| | |acc_norm|0.7379|± |0.0307|
|agieval_sat_en_without_passage| 0|acc |0.4417|± |0.0347|
| | |acc_norm|0.4223|± |0.0345|
|agieval_sat_math | 0|acc |0.4000|± |0.0331|
| | |acc_norm|0.3455|± |0.0321|
```
Average: 44.05
## BigBench:
```
| Task |Version| Metric |Value | |Stderr|
|------------------------------------------------|------:|---------------------|-----:|---|-----:|
|bigbench_causal_judgement | 0|multiple_choice_grade|0.6000|± |0.0356|
|bigbench_date_understanding | 0|multiple_choice_grade|0.6585|± |0.0247|
|bigbench_disambiguation_qa | 0|multiple_choice_grade|0.3178|± |0.0290|
|bigbench_geometric_shapes | 0|multiple_choice_grade|0.2340|± |0.0224|
| | |exact_str_match |0.0000|± |0.0000|
|bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|0.2980|± |0.0205|
|bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|0.2057|± |0.0153|
|bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|0.5367|± |0.0288|
|bigbench_movie_recommendation | 0|multiple_choice_grade|0.4040|± |0.0220|
|bigbench_navigate | 0|multiple_choice_grade|0.4970|± |0.0158|
|bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|0.7075|± |0.0102|
|bigbench_ruin_names | 0|multiple_choice_grade|0.4821|± |0.0236|
|bigbench_salient_translation_error_detection | 0|multiple_choice_grade|0.2295|± |0.0133|
|bigbench_snarks | 0|multiple_choice_grade|0.6906|± |0.0345|
|bigbench_sports_understanding | 0|multiple_choice_grade|0.5375|± |0.0159|
|bigbench_temporal_sequences | 0|multiple_choice_grade|0.6270|± |0.0153|
|bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|0.2216|± |0.0118|
|bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|0.1594|± |0.0088|
|bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|0.5367|± |0.0288|
```
Average: 44.13
**IFEval**: 72.64
**MT_Bench**: Turn 1 - 8.3875, Turn 2 - 8.00625, Average - 8.196875
# Inference Code
Here is example code using HuggingFace Transformers to inference the model (note: in 4bit, it will require around 5GB of VRAM)
Note: To use function calling, you should see the github repo above.
```python
# Code to inference Hermes with HF Transformers
# Requires pytorch, transformers, bitsandbytes, sentencepiece, protobuf, and flash-attn packages
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, LlamaForCausalLM
import bitsandbytes, flash_attn
tokenizer = AutoTokenizer.from_pretrained('NousResearch/Hermes-2-Theta-Llama-3-8B', trust_remote_code=True)
model = LlamaForCausalLM.from_pretrained(
"NousResearch/Hermes-2-Theta-Llama-3-8B",
torch_dtype=torch.float16,
device_map="auto",
load_in_8bit=False,
load_in_4bit=True,
use_flash_attention_2=True
)
prompts = [
"""<|im_start|>system
You are a sentient, superintelligent artificial general intelligence, here to teach and assist me.<|im_end|>
<|im_start|>user
Write a short story about Goku discovering kirby has teamed up with Majin Buu to destroy the world.<|im_end|>
<|im_start|>assistant""",
]
for chat in prompts:
print(chat)
input_ids = tokenizer(chat, return_tensors="pt").input_ids.to("cuda")
generated_ids = model.generate(input_ids, max_new_tokens=750, temperature=0.8, repetition_penalty=1.1, do_sample=True, eos_token_id=tokenizer.eos_token_id)
response = tokenizer.decode(generated_ids[0][input_ids.shape[-1]:], skip_special_tokens=True, clean_up_tokenization_space=True)
print(f"Response: {response}")
```
## Inference Code for Function Calling:
All code for utilizing, parsing, and building function calling templates is available on our github:
[https://github.com/NousResearch/Hermes-Function-Calling](https://github.com/NousResearch/Hermes-Function-Calling)
# Chat Interfaces
When quantized versions of the model are released, I recommend using LM Studio for chatting with Hermes 2 Pro. It does not support function calling - for that use our github repo. It is a GUI application that utilizes GGUF models with a llama.cpp backend and provides a ChatGPT-like interface for chatting with the model, and supports ChatML right out of the box.
In LM-Studio, simply select the ChatML Prefix on the settings side pane:
## Quantized Versions:
GGUF Versions Available Here: https://huggingface.co/NousResearch/Hermes-2-Theta-Llama-3-8B-GGUF
# How to cite:
```bibtext
@misc{Hermes-2-Theta-Llama-3-8B,
url={[https://huggingface.co/NousResearch/Hermes-2-Theta-Llama-3-8B][NousResearch/Hermes-2-Theta-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B))},
title={Hermes-2-Theta-Llama-3-8B},
author={"Teknium", Charles Goddard, "interstellarninja", "theemozilla", "karan4d", "huemin_art"}
}
``` |
mradermacher/dolphin-2.9.3-llama-3-8b-GGUF | mradermacher | "2024-06-27T18:43:25Z" | 19,813 | 0 | transformers | [
"transformers",
"gguf",
"generated_from_trainer",
"en",
"base_model:cognitivecomputations/dolphin-2.9.3-llama-3-8b",
"endpoints_compatible",
"region:us"
] | null | "2024-06-27T16:29:35Z" | ---
base_model: cognitivecomputations/dolphin-2.9.3-llama-3-8b
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- generated_from_trainer
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/cognitivecomputations/dolphin-2.9.3-llama-3-8b
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/dolphin-2.9.3-llama-3-8b-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-llama-3-8b-GGUF/resolve/main/dolphin-2.9.3-llama-3-8b.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-llama-3-8b-GGUF/resolve/main/dolphin-2.9.3-llama-3-8b.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-llama-3-8b-GGUF/resolve/main/dolphin-2.9.3-llama-3-8b.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-llama-3-8b-GGUF/resolve/main/dolphin-2.9.3-llama-3-8b.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-llama-3-8b-GGUF/resolve/main/dolphin-2.9.3-llama-3-8b.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-llama-3-8b-GGUF/resolve/main/dolphin-2.9.3-llama-3-8b.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-llama-3-8b-GGUF/resolve/main/dolphin-2.9.3-llama-3-8b.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-llama-3-8b-GGUF/resolve/main/dolphin-2.9.3-llama-3-8b.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-llama-3-8b-GGUF/resolve/main/dolphin-2.9.3-llama-3-8b.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-llama-3-8b-GGUF/resolve/main/dolphin-2.9.3-llama-3-8b.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-llama-3-8b-GGUF/resolve/main/dolphin-2.9.3-llama-3-8b.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-llama-3-8b-GGUF/resolve/main/dolphin-2.9.3-llama-3-8b.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-llama-3-8b-GGUF/resolve/main/dolphin-2.9.3-llama-3-8b.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-llama-3-8b-GGUF/resolve/main/dolphin-2.9.3-llama-3-8b.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-llama-3-8b-GGUF/resolve/main/dolphin-2.9.3-llama-3-8b.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
mradermacher/Llama-3-8B-Instruct-UltraDPO2-GGUF | mradermacher | "2024-06-30T06:13:47Z" | 19,800 | 0 | transformers | [
"transformers",
"gguf",
"alignment-handbook",
"generated_from_trainer",
"en",
"dataset:princeton-nlp/llama3-ultrafeedback",
"base_model:Magpie-Align/Llama-3-8B-Instruct-UltraDPO2",
"license:llama3",
"endpoints_compatible",
"region:us"
] | null | "2024-06-30T04:07:54Z" | ---
base_model: Magpie-Align/Llama-3-8B-Instruct-UltraDPO2
datasets:
- princeton-nlp/llama3-ultrafeedback
language:
- en
library_name: transformers
license: llama3
quantized_by: mradermacher
tags:
- alignment-handbook
- generated_from_trainer
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/Magpie-Align/Llama-3-8B-Instruct-UltraDPO2
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-UltraDPO2-GGUF/resolve/main/Llama-3-8B-Instruct-UltraDPO2.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-UltraDPO2-GGUF/resolve/main/Llama-3-8B-Instruct-UltraDPO2.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-UltraDPO2-GGUF/resolve/main/Llama-3-8B-Instruct-UltraDPO2.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-UltraDPO2-GGUF/resolve/main/Llama-3-8B-Instruct-UltraDPO2.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-UltraDPO2-GGUF/resolve/main/Llama-3-8B-Instruct-UltraDPO2.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-UltraDPO2-GGUF/resolve/main/Llama-3-8B-Instruct-UltraDPO2.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-UltraDPO2-GGUF/resolve/main/Llama-3-8B-Instruct-UltraDPO2.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-UltraDPO2-GGUF/resolve/main/Llama-3-8B-Instruct-UltraDPO2.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-UltraDPO2-GGUF/resolve/main/Llama-3-8B-Instruct-UltraDPO2.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-UltraDPO2-GGUF/resolve/main/Llama-3-8B-Instruct-UltraDPO2.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-UltraDPO2-GGUF/resolve/main/Llama-3-8B-Instruct-UltraDPO2.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-UltraDPO2-GGUF/resolve/main/Llama-3-8B-Instruct-UltraDPO2.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-UltraDPO2-GGUF/resolve/main/Llama-3-8B-Instruct-UltraDPO2.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-UltraDPO2-GGUF/resolve/main/Llama-3-8B-Instruct-UltraDPO2.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-UltraDPO2-GGUF/resolve/main/Llama-3-8B-Instruct-UltraDPO2.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
lllyasviel/control_v11f1p_sd15_depth | lllyasviel | "2023-05-04T18:49:15Z" | 19,780 | 36 | diffusers | [
"diffusers",
"safetensors",
"art",
"controlnet",
"stable-diffusion",
"controlnet-v1-1",
"image-to-image",
"arxiv:2302.05543",
"base_model:runwayml/stable-diffusion-v1-5",
"license:openrail",
"region:us"
] | image-to-image | "2023-04-16T14:13:02Z" | ---
license: openrail
base_model: runwayml/stable-diffusion-v1-5
tags:
- art
- controlnet
- stable-diffusion
- controlnet-v1-1
- image-to-image
duplicated_from: ControlNet-1-1-preview/control_v11p_sd15_depth
---
# Controlnet - v1.1 - *depth Version*
**Controlnet v1.1** is the successor model of [Controlnet v1.0](https://huggingface.co/lllyasviel/ControlNet)
and was released in [lllyasviel/ControlNet-v1-1](https://huggingface.co/lllyasviel/ControlNet-v1-1) by [Lvmin Zhang](https://huggingface.co/lllyasviel).
This checkpoint is a conversion of [the original checkpoint](https://huggingface.co/lllyasviel/ControlNet-v1-1/blob/main/control_v11f1p_sd15_depth.pth) into `diffusers` format.
It can be used in combination with **Stable Diffusion**, such as [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5).
For more details, please also have a look at the [🧨 Diffusers docs](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/controlnet).
ControlNet is a neural network structure to control diffusion models by adding extra conditions.
This checkpoint corresponds to the ControlNet conditioned on **depth images**.
## Model Details
- **Developed by:** Lvmin Zhang, Maneesh Agrawala
- **Model type:** Diffusion-based text-to-image generation model
- **Language(s):** English
- **License:** [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based.
- **Resources for more information:** [GitHub Repository](https://github.com/lllyasviel/ControlNet), [Paper](https://arxiv.org/abs/2302.05543).
- **Cite as:**
@misc{zhang2023adding,
title={Adding Conditional Control to Text-to-Image Diffusion Models},
author={Lvmin Zhang and Maneesh Agrawala},
year={2023},
eprint={2302.05543},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
## Introduction
Controlnet was proposed in [*Adding Conditional Control to Text-to-Image Diffusion Models*](https://arxiv.org/abs/2302.05543) by
Lvmin Zhang, Maneesh Agrawala.
The abstract reads as follows:
*We present a neural network structure, ControlNet, to control pretrained large diffusion models to support additional input conditions.
The ControlNet learns task-specific conditions in an end-to-end way, and the learning is robust even when the training dataset is small (< 50k).
Moreover, training a ControlNet is as fast as fine-tuning a diffusion model, and the model can be trained on a personal devices.
Alternatively, if powerful computation clusters are available, the model can scale to large amounts (millions to billions) of data.
We report that large diffusion models like Stable Diffusion can be augmented with ControlNets to enable conditional inputs like edge maps, depthmentation maps, keypoints, etc.
This may enrich the methods to control large diffusion models and further facilitate related applications.*
## Example
It is recommended to use the checkpoint with [Stable Diffusion v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) as the checkpoint
has been trained on it.
Experimentally, the checkpoint can be used with other diffusion models such as dreamboothed stable diffusion.
**Note**: If you want to process an image to create the auxiliary conditioning, external dependencies are required as shown below:
1. Let's install `diffusers` and related packages:
```
$ pip install diffusers transformers accelerate
```
3. Run code:
```python
import torch
import os
from huggingface_hub import HfApi
from pathlib import Path
from diffusers.utils import load_image
from PIL import Image
import numpy as np
from transformers import pipeline
from diffusers import (
ControlNetModel,
StableDiffusionControlNetPipeline,
UniPCMultistepScheduler,
)
checkpoint = "lllyasviel/control_v11p_sd15_depth"
image = load_image(
"https://huggingface.co/lllyasviel/control_v11p_sd15_depth/resolve/main/images/input.png"
)
prompt = "Stormtrooper's lecture in beautiful lecture hall"
depth_estimator = pipeline('depth-estimation')
image = depth_estimator(image)['depth']
image = np.array(image)
image = image[:, :, None]
image = np.concatenate([image, image, image], axis=2)
control_image = Image.fromarray(image)
control_image.save("./images/control.png")
controlnet = ControlNetModel.from_pretrained(checkpoint, torch_dtype=torch.float16)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
)
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()
generator = torch.manual_seed(0)
image = pipe(prompt, num_inference_steps=30, generator=generator, image=control_image).images[0]
image.save('images/image_out.png')
```
## Other released checkpoints v1-1
The authors released 14 different checkpoints, each trained with [Stable Diffusion v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5)
on a different type of conditioning:
| Model Name | Control Image Overview| Condition Image | Control Image Example | Generated Image Example |
|---|---|---|---|---|
|[lllyasviel/control_v11p_sd15_canny](https://huggingface.co/lllyasviel/control_v11p_sd15_canny)<br/> | *Trained with canny edge detection* | A monochrome image with white edges on a black background.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_canny/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_canny/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_canny/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_canny/resolve/main/images/image_out.png"/></a>|
|[lllyasviel/control_v11e_sd15_ip2p](https://huggingface.co/lllyasviel/control_v11e_sd15_ip2p)<br/> | *Trained with pixel to pixel instruction* | No condition .|<a href="https://huggingface.co/lllyasviel/control_v11e_sd15_ip2p/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11e_sd15_ip2p/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11e_sd15_ip2p/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11e_sd15_ip2p/resolve/main/images/image_out.png"/></a>|
|[lllyasviel/control_v11p_sd15_inpaint](https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint)<br/> | Trained with image inpainting | No condition.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint/resolve/main/images/output.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint/resolve/main/images/output.png"/></a>|
|[lllyasviel/control_v11p_sd15_mlsd](https://huggingface.co/lllyasviel/control_v11p_sd15_mlsd)<br/> | Trained with multi-level line segment detection | An image with annotated line segments.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_mlsd/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_mlsd/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_mlsd/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_mlsd/resolve/main/images/image_out.png"/></a>|
|[lllyasviel/control_v11f1p_sd15_depth](https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth)<br/> | Trained with depth estimation | An image with depth information, usually represented as a grayscale image.|<a href="https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth/resolve/main/images/image_out.png"/></a>|
|[lllyasviel/control_v11p_sd15_normalbae](https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae)<br/> | Trained with surface normal estimation | An image with surface normal information, usually represented as a color-coded image.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae/resolve/main/images/image_out.png"/></a>|
|[lllyasviel/control_v11p_sd15_seg](https://huggingface.co/lllyasviel/control_v11p_sd15_seg)<br/> | Trained with image segmentation | An image with segmented regions, usually represented as a color-coded image.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_seg/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_seg/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_seg/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_seg/resolve/main/images/image_out.png"/></a>|
|[lllyasviel/control_v11p_sd15_lineart](https://huggingface.co/lllyasviel/control_v11p_sd15_lineart)<br/> | Trained with line art generation | An image with line art, usually black lines on a white background.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_lineart/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_lineart/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_lineart/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_lineart/resolve/main/images/image_out.png"/></a>|
|[lllyasviel/control_v11p_sd15s2_lineart_anime](https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime)<br/> | Trained with anime line art generation | An image with anime-style line art.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime/resolve/main/images/image_out.png"/></a>|
|[lllyasviel/control_v11p_sd15_openpose](https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime)<br/> | Trained with human pose estimation | An image with human poses, usually represented as a set of keypoints or skeletons.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_openpose/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_openpose/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_openpose/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_openpose/resolve/main/images/image_out.png"/></a>|
|[lllyasviel/control_v11p_sd15_scribble](https://huggingface.co/lllyasviel/control_v11p_sd15_scribble)<br/> | Trained with scribble-based image generation | An image with scribbles, usually random or user-drawn strokes.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_scribble/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_scribble/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_scribble/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_scribble/resolve/main/images/image_out.png"/></a>|
|[lllyasviel/control_v11p_sd15_softedge](https://huggingface.co/lllyasviel/control_v11p_sd15_softedge)<br/> | Trained with soft edge image generation | An image with soft edges, usually to create a more painterly or artistic effect.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_softedge/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_softedge/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_softedge/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_softedge/resolve/main/images/image_out.png"/></a>|
|[lllyasviel/control_v11e_sd15_shuffle](https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle)<br/> | Trained with image shuffling | An image with shuffled patches or regions.|<a href="https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle/resolve/main/images/image_out.png"/></a>|
|[lllyasviel/control_v11f1e_sd15_tile](https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile)<br/> | Trained with image tiling | A blurry image or part of an image .|<a href="https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile/resolve/main/images/original.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile/resolve/main/images/original.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile/resolve/main/images/output.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile/resolve/main/images/output.png"/></a>|
## Improvements in Depth 1.1:
- The training dataset of previous cnet 1.0 has several problems including (1) a small group of greyscale human images are duplicated thousands of times (!!), causing the previous model somewhat likely to generate grayscale human images; (2) some images has low quality, very blurry, or significant JPEG artifacts; (3) a small group of images has wrong paired prompts caused by a mistake in our data processing scripts. The new model fixed all problems of the training dataset and should be more reasonable in many cases.
- The new depth model is a relatively unbiased model. It is not trained with some specific type of depth by some specific depth estimation method. It is not over-fitted to one preprocessor. This means this model will work better with different depth estimation, different preprocessor resolutions, or even with real depth created by 3D engines.
- Some reasonable data augmentations are applied to training, like random left-right flipping.
- The model is resumed from depth 1.0, and it should work well in all cases where depth 1.0 works well. If not, please open an issue with image, and we will take a look at your case. Depth 1.1 works well in many failure cases of depth 1.0.
- If you use Midas depth (the "depth" in webui plugin) with 384 preprocessor resolution, the difference between depth 1.0 and 1.1 should be minimal. However, if you try other preprocessor resolutions or other preprocessors (like leres and zoe), the depth 1.1 is expected to be a bit better than 1.0.
## More information
For more information, please also have a look at the [Diffusers ControlNet Blog Post](https://huggingface.co/blog/controlnet) and have a look at the [official docs](https://github.com/lllyasviel/ControlNet-v1-1-nightly). |
hfl/llama-3-chinese-8b-instruct-v3-gguf | hfl | "2024-06-06T00:17:51Z" | 19,767 | 26 | null | [
"gguf",
"zh",
"en",
"base_model:hfl/llama-3-chinese-8b-instruct-v3",
"license:apache-2.0",
"region:us"
] | null | "2024-05-28T02:59:33Z" | ---
license: apache-2.0
language:
- zh
- en
base_model:
- hfl/llama-3-chinese-8b-instruct-v3
---
# Llama-3-Chinese-8B-Instruct-v3-GGUF
<p align="center">
<a href="https://github.com/ymcui/Chinese-LLaMA-Alpaca-3"><img src="https://ymcui.com/images/chinese-llama-alpaca-3-banner.png" width="600"/></a>
</p>
[**[👉👉👉 Chat with Llama-3-Chinese-8B-Instruct-v3 @ HF Space]**](https://huggingface.co/spaces/hfl-rc/llama-3-chinese-8b-instruct-demo)
This repository contains **Llama-3-Chinese-8B-Instruct-v3-GGUF** (llama.cpp/ollama/tgw, etc. compatible), which is the quantized version of [Llama-3-Chinese-8B-Instruct-v3](https://huggingface.co/hfl/llama-3-chinese-8b-instruct-v3).
**Note: this is an instruction (chat) model, which can be used for conversation, QA, etc.**
Further details (performance, usage, etc.) should refer to GitHub project page: https://github.com/ymcui/Chinese-LLaMA-Alpaca-3
## Performance
Metric: PPL, lower is better
*Note: Unless constrained by memory, we suggest using Q8_0 or Q6_K for better performance.*
| Quant | Size | PPL |
| :---: | -------: | ------------------: |
| Q2_K | 2.96 GB | 10.0534 +/- 0.13135 |
| Q3_K | 3.74 GB | 6.3295 +/- 0.07816 |
| Q4_0 | 4.34 GB | 6.3200 +/- 0.07893 |
| Q4_K | 4.58 GB | 6.0042 +/- 0.07431 |
| Q5_0 | 5.21 GB | 6.0437 +/- 0.07526 |
| Q5_K | 5.34 GB | 5.9484 +/- 0.07399 |
| Q6_K | 6.14 GB | 5.9469 +/- 0.07404 |
| Q8_0 | 7.95 GB | 5.8933 +/- 0.07305 |
| F16 | 14.97 GB | 5.8902 +/- 0.07303 |
## Others
- For full model, please see: https://huggingface.co/hfl/llama-3-chinese-8b-instruct-v3
- If you have questions/issues regarding this model, please submit an issue through https://github.com/ymcui/Chinese-LLaMA-Alpaca-3 |
stephenlzc/dolphin-llama3-zh-cn-uncensored | stephenlzc | "2024-06-24T17:44:54Z" | 19,740 | 1 | transformers | [
"transformers",
"safetensors",
"gguf",
"llama",
"text-generation",
"text-generation-inference",
"code",
"unsloth",
"NSFW",
"conversational",
"zh",
"en",
"dataset:Minami-su/toxic-sft-zh",
"dataset:llm-wizard/alpaca-gpt4-data-zh",
"dataset:stephenlzc/stf-alpaca",
"base_model:cognitivecomputations/dolphin-2.9-llama3-8b",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-06-21T10:31:07Z" | ---
datasets:
- Minami-su/toxic-sft-zh
- llm-wizard/alpaca-gpt4-data-zh
- stephenlzc/stf-alpaca
language:
- zh
- en
license: mit
pipeline_tag: text-generation
tags:
- text-generation-inference
- code
- unsloth
- NSFW
task_categories:
- conversational
base_model: cognitivecomputations/dolphin-2.9-llama3-8b
widget:
- text: >-
Is this review positive or negative? Review: Best cast iron skillet you will
ever buy.
example_title: Sentiment analysis
- text: >-
Barack Obama nominated Hilary Clinton as his secretary of state on Monday.
He chose her because she had ...
example_title: Coreference resolution
- text: >-
On a shelf, there are five books: a gray book, a red book, a purple book, a
blue book, and a black book ...
example_title: Logic puzzles
- text: >-
The two men running to become New York City's next mayor will face off in
their first debate Wednesday night ...
example_title: Reading comprehension
---
## Model Details
### Model Description
It's my first finetune exmaple.
Using `cognitivecomputations/dolphin-2.9-llama3-8b` as base model, and finetune with `Minami-su/toxic-sft-zh` and `llm-wizard/alpaca-gpt4-data-zh` to let tha model support Chinese.
## Training Procedure
[](https://colab.research.google.com/drive/1bTLjWTVKgXJfdc1T-roMwa3k1NIERYyC?usp=sharing)
### Training Data
**Base Model**
🐬[cognitivecomputations/dolphin-2.9-llama3-8b](https://huggingface.co/cognitivecomputations/dolphin-2.9-llama3-8b)
**Dataset**
- [Minami-su/toxic-sft-zh](https://huggingface.co/datasets/Minami-su/toxic-sft-zh)
- [llm-wizard/alpaca-gpt4-data-zh](https://huggingface.co/datasets/llm-wizard/alpaca-gpt4-data-zh) |
google/vit-large-patch16-224 | google | "2022-06-23T07:50:15Z" | 19,715 | 23 | transformers | [
"transformers",
"pytorch",
"tf",
"jax",
"vit",
"image-classification",
"vision",
"dataset:imagenet-1k",
"dataset:imagenet-21k",
"arxiv:2010.11929",
"arxiv:2006.03677",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | "2022-03-02T23:29:05Z" | ---
license: apache-2.0
tags:
- image-classification
- vision
datasets:
- imagenet-1k
- imagenet-21k
---
# Vision Transformer (large-sized model)
Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Dosovitskiy et al. and first released in [this repository](https://github.com/google-research/vision_transformer). However, the weights were converted from the [timm repository](https://github.com/rwightman/pytorch-image-models) by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him.
Disclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, at the same resolution, 224x224.
Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.
By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=google/vit) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import ViTFeatureExtractor, ViTForImageClassification
from PIL import Image
import requests
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-large-patch16-224')
model = ViTForImageClassification.from_pretrained('google/vit-large-patch16-224')
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
# model predicts one of the 1000 ImageNet classes
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
```
Currently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon, and the API of ViTFeatureExtractor might change.
## Training data
The ViT model was pretrained on [ImageNet-21k](http://www.image-net.org/), a dataset consisting of 14 million images and 21k classes, and fine-tuned on [ImageNet](http://www.image-net.org/challenges/LSVRC/2012/), a dataset consisting of 1 million images and 1k classes.
## Training procedure
### Preprocessing
The exact details of preprocessing of images during training/validation can be found [here](https://github.com/google-research/vision_transformer/blob/master/vit_jax/input_pipeline.py).
Images are resized/rescaled to the same resolution (224x224) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).
### Pretraining
The model was trained on TPUv3 hardware (8 cores). All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Pre-training resolution is 224.
## Evaluation results
For evaluation results on several image classification benchmarks, we refer to tables 2 and 5 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.
### BibTeX entry and citation info
```bibtex
@misc{wu2020visual,
title={Visual Transformers: Token-based Image Representation and Processing for Computer Vision},
author={Bichen Wu and Chenfeng Xu and Xiaoliang Dai and Alvin Wan and Peizhao Zhang and Zhicheng Yan and Masayoshi Tomizuka and Joseph Gonzalez and Kurt Keutzer and Peter Vajda},
year={2020},
eprint={2006.03677},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
```bibtex
@inproceedings{deng2009imagenet,
title={Imagenet: A large-scale hierarchical image database},
author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},
booktitle={2009 IEEE conference on computer vision and pattern recognition},
pages={248--255},
year={2009},
organization={Ieee}
}
``` |
Langboat/bloom-1b4-zh | Langboat | "2023-05-08T03:39:33Z" | 19,703 | 12 | transformers | [
"transformers",
"pytorch",
"safetensors",
"bloom",
"text-generation",
"zh",
"license:bigscience-bloom-rail-1.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2022-08-31T06:49:32Z" | ---
license: bigscience-bloom-rail-1.0
language:
- zh
pipeline_tag: text-generation
widget:
- text: "中国的首都是"
---
This model is based on [bigscience/bloom-1b7](https://huggingface.co/bigscience/bloom-1b7).
We pruned its vocabulary from 250880 to 46145 with Chinese corpus to reduce GPU memory usage. So the total parameter is 1.4b now.
# How to use
```python
from transformers import BloomTokenizerFast, BloomForCausalLM
tokenizer = BloomTokenizerFast.from_pretrained('Langboat/bloom-1b4-zh')
model = BloomForCausalLM.from_pretrained('Langboat/bloom-1b4-zh')
print(tokenizer.batch_decode(model.generate(tokenizer.encode('中国的首都是', return_tensors='pt'))))
``` |
GroNLP/gpt2-small-dutch | GroNLP | "2023-09-11T08:57:58Z" | 19,685 | 4 | transformers | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"gpt2",
"text-generation",
"adaption",
"recycled",
"gpt2-small",
"nl",
"arxiv:2012.05628",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2022-03-02T23:29:04Z" | ---
language: nl
tags:
- adaption
- recycled
- gpt2-small
pipeline_tag: text-generation
---
# GPT-2 recycled for Dutch (small)
[Wietse de Vries](https://www.semanticscholar.org/author/Wietse-de-Vries/144611157) •
[Malvina Nissim](https://www.semanticscholar.org/author/M.-Nissim/2742475)
## Model description
This model is based on the small OpenAI GPT-2 ([`gpt2`](https://huggingface.co/gpt2)) model.
For details, check out our paper on [arXiv](https://arxiv.org/abs/2012.05628) and the code on [Github](https://github.com/wietsedv/gpt2-recycle).
## Related models
### Dutch
- [`gpt2-small-dutch-embeddings`](https://huggingface.co/GroNLP/gpt2-small-dutch-embeddings): Small model size with only retrained lexical embeddings.
- [`gpt2-small-dutch`](https://huggingface.co/GroNLP/gpt2-small-dutch): Small model size with retrained lexical embeddings and additional fine-tuning of the full model. (**Recommended**)
- [`gpt2-medium-dutch-embeddings`](https://huggingface.co/GroNLP/gpt2-medium-dutch-embeddings): Medium model size with only retrained lexical embeddings.
### Italian
- [`gpt2-small-italian-embeddings`](https://huggingface.co/GroNLP/gpt2-small-italian-embeddings): Small model size with only retrained lexical embeddings.
- [`gpt2-small-italian`](https://huggingface.co/GroNLP/gpt2-small-italian): Small model size with retrained lexical embeddings and additional fine-tuning of the full model. (**Recommended**)
- [`gpt2-medium-italian-embeddings`](https://huggingface.co/GroNLP/gpt2-medium-italian-embeddings): Medium model size with only retrained lexical embeddings.
## How to use
```python
from transformers import pipeline
pipe = pipeline("text-generation", model="GroNLP/gpt2-small-dutch")
```
```python
from transformers import AutoTokenizer, AutoModel, TFAutoModel
tokenizer = AutoTokenizer.from_pretrained("GroNLP/gpt2-small-dutch")
model = AutoModel.from_pretrained("GroNLP/gpt2-small-dutch") # PyTorch
model = TFAutoModel.from_pretrained("GroNLP/gpt2-small-dutch") # Tensorflow
```
## BibTeX entry
```bibtex
@misc{devries2020good,
title={As good as new. How to successfully recycle English GPT-2 to make models for other languages},
author={Wietse de Vries and Malvina Nissim},
year={2020},
eprint={2012.05628},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
bigscience/bloomz-7b1 | bigscience | "2024-02-22T09:05:04Z" | 19,668 | 134 | transformers | [
"transformers",
"pytorch",
"safetensors",
"bloom",
"text-generation",
"ak",
"ar",
"as",
"bm",
"bn",
"ca",
"code",
"en",
"es",
"eu",
"fon",
"fr",
"gu",
"hi",
"id",
"ig",
"ki",
"kn",
"lg",
"ln",
"ml",
"mr",
"ne",
"nso",
"ny",
"or",
"pa",
"pt",
"rn",
"rw",
"sn",
"st",
"sw",
"ta",
"te",
"tn",
"ts",
"tum",
"tw",
"ur",
"vi",
"wo",
"xh",
"yo",
"zh",
"zu",
"dataset:bigscience/xP3",
"arxiv:2211.01786",
"license:bigscience-bloom-rail-1.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2022-09-27T09:00:57Z" | ---
datasets:
- bigscience/xP3
license: bigscience-bloom-rail-1.0
language:
- ak
- ar
- as
- bm
- bn
- ca
- code
- en
- es
- eu
- fon
- fr
- gu
- hi
- id
- ig
- ki
- kn
- lg
- ln
- ml
- mr
- ne
- nso
- ny
- or
- pa
- pt
- rn
- rw
- sn
- st
- sw
- ta
- te
- tn
- ts
- tum
- tw
- ur
- vi
- wo
- xh
- yo
- zh
- zu
programming_language:
- C
- C++
- C#
- Go
- Java
- JavaScript
- Lua
- PHP
- Python
- Ruby
- Rust
- Scala
- TypeScript
pipeline_tag: text-generation
widget:
- text: "一个传奇的开端，一个不灭的神话，这不仅仅是一部电影，而是作为一个走进新时代的标签，永远彪炳史册。Would you rate the previous review as positive, neutral or negative?"
example_title: "zh-en sentiment"
- text: "一个传奇的开端，一个不灭的神话，这不仅仅是一部电影，而是作为一个走进新时代的标签，永远彪炳史册。你认为这句话的立场是赞扬、中立还是批评？"
example_title: "zh-zh sentiment"
- text: "Suggest at least five related search terms to \"Mạng neural nhân tạo\"."
example_title: "vi-en query"
- text: "Proposez au moins cinq mots clés concernant «Réseau de neurones artificiels»."
example_title: "fr-fr query"
- text: "Explain in a sentence in Telugu what is backpropagation in neural networks."
example_title: "te-en qa"
- text: "Why is the sky blue?"
example_title: "en-en qa"
- text: "Write a fairy tale about a troll saving a princess from a dangerous dragon. The fairy tale is a masterpiece that has achieved praise worldwide and its moral is \"Heroes Come in All Shapes and Sizes\". Story (in Spanish):"
example_title: "es-en fable"
- text: "Write a fable about wood elves living in a forest that is suddenly invaded by ogres. The fable is a masterpiece that has achieved praise worldwide and its moral is \"Violence is the last refuge of the incompetent\". Fable (in Hindi):"
example_title: "hi-en fable"
model-index:
- name: bloomz-7b1
results:
- task:
type: Coreference resolution
dataset:
type: winogrande
name: Winogrande XL (xl)
config: xl
split: validation
revision: a80f460359d1e9a67c006011c94de42a8759430c
metrics:
- type: Accuracy
value: 55.8
- task:
type: Coreference resolution
dataset:
type: Muennighoff/xwinograd
name: XWinograd (en)
config: en
split: test
revision: 9dd5ea5505fad86b7bedad667955577815300cee
metrics:
- type: Accuracy
value: 66.02
- task:
type: Coreference resolution
dataset:
type: Muennighoff/xwinograd
name: XWinograd (fr)
config: fr
split: test
revision: 9dd5ea5505fad86b7bedad667955577815300cee
metrics:
- type: Accuracy
value: 57.83
- task:
type: Coreference resolution
dataset:
type: Muennighoff/xwinograd
name: XWinograd (jp)
config: jp
split: test
revision: 9dd5ea5505fad86b7bedad667955577815300cee
metrics:
- type: Accuracy
value: 52.87
- task:
type: Coreference resolution
dataset:
type: Muennighoff/xwinograd
name: XWinograd (pt)
config: pt
split: test
revision: 9dd5ea5505fad86b7bedad667955577815300cee
metrics:
- type: Accuracy
value: 57.79
- task:
type: Coreference resolution
dataset:
type: Muennighoff/xwinograd
name: XWinograd (ru)
config: ru
split: test
revision: 9dd5ea5505fad86b7bedad667955577815300cee
metrics:
- type: Accuracy
value: 54.92
- task:
type: Coreference resolution
dataset:
type: Muennighoff/xwinograd
name: XWinograd (zh)
config: zh
split: test
revision: 9dd5ea5505fad86b7bedad667955577815300cee
metrics:
- type: Accuracy
value: 63.69
- task:
type: Natural language inference
dataset:
type: anli
name: ANLI (r1)
config: r1
split: validation
revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094
metrics:
- type: Accuracy
value: 42.1
- task:
type: Natural language inference
dataset:
type: anli
name: ANLI (r2)
config: r2
split: validation
revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094
metrics:
- type: Accuracy
value: 39.5
- task:
type: Natural language inference
dataset:
type: anli
name: ANLI (r3)
config: r3
split: validation
revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094
metrics:
- type: Accuracy
value: 41.0
- task:
type: Natural language inference
dataset:
type: super_glue
name: SuperGLUE (cb)
config: cb
split: validation
revision: 9e12063561e7e6c79099feb6d5a493142584e9e2
metrics:
- type: Accuracy
value: 80.36
- task:
type: Natural language inference
dataset:
type: super_glue
name: SuperGLUE (rte)
config: rte
split: validation
revision: 9e12063561e7e6c79099feb6d5a493142584e9e2
metrics:
- type: Accuracy
value: 84.12
- task:
type: Natural language inference
dataset:
type: xnli
name: XNLI (ar)
config: ar
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 53.25
- task:
type: Natural language inference
dataset:
type: xnli
name: XNLI (bg)
config: bg
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 43.61
- task:
type: Natural language inference
dataset:
type: xnli
name: XNLI (de)
config: de
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 46.83
- task:
type: Natural language inference
dataset:
type: xnli
name: XNLI (el)
config: el
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 41.53
- task:
type: Natural language inference
dataset:
type: xnli
name: XNLI (en)
config: en
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 59.68
- task:
type: Natural language inference
dataset:
type: xnli
name: XNLI (es)
config: es
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 55.1
- task:
type: Natural language inference
dataset:
type: xnli
name: XNLI (fr)
config: fr
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 55.26
- task:
type: Natural language inference
dataset:
type: xnli
name: XNLI (hi)
config: hi
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 50.88
- task:
type: Natural language inference
dataset:
type: xnli
name: XNLI (ru)
config: ru
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 47.75
- task:
type: Natural language inference
dataset:
type: xnli
name: XNLI (sw)
config: sw
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 46.63
- task:
type: Natural language inference
dataset:
type: xnli
name: XNLI (th)
config: th
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 40.12
- task:
type: Natural language inference
dataset:
type: xnli
name: XNLI (tr)
config: tr
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 37.55
- task:
type: Natural language inference
dataset:
type: xnli
name: XNLI (ur)
config: ur
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 46.51
- task:
type: Natural language inference
dataset:
type: xnli
name: XNLI (vi)
config: vi
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 52.93
- task:
type: Natural language inference
dataset:
type: xnli
name: XNLI (zh)
config: zh
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 53.61
- task:
type: Program synthesis
dataset:
type: openai_humaneval
name: HumanEval
config: None
split: test
revision: e8dc562f5de170c54b5481011dd9f4fa04845771
metrics:
- type: Pass@1
value: 8.06
- type: Pass@10
value: 15.03
- type: Pass@100
value: 27.49
- task:
type: Sentence completion
dataset:
type: story_cloze
name: StoryCloze (2016)
config: "2016"
split: validation
revision: e724c6f8cdf7c7a2fb229d862226e15b023ee4db
metrics:
- type: Accuracy
value: 90.43
- task:
type: Sentence completion
dataset:
type: super_glue
name: SuperGLUE (copa)
config: copa
split: validation
revision: 9e12063561e7e6c79099feb6d5a493142584e9e2
metrics:
- type: Accuracy
value: 86.0
- task:
type: Sentence completion
dataset:
type: xcopa
name: XCOPA (et)
config: et
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 50.0
- task:
type: Sentence completion
dataset:
type: xcopa
name: XCOPA (ht)
config: ht
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 54.0
- task:
type: Sentence completion
dataset:
type: xcopa
name: XCOPA (id)
config: id
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 76.0
- task:
type: Sentence completion
dataset:
type: xcopa
name: XCOPA (it)
config: it
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 61.0
- task:
type: Sentence completion
dataset:
type: xcopa
name: XCOPA (qu)
config: qu
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 60.0
- task:
type: Sentence completion
dataset:
type: xcopa
name: XCOPA (sw)
config: sw
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 63.0
- task:
type: Sentence completion
dataset:
type: xcopa
name: XCOPA (ta)
config: ta
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 64.0
- task:
type: Sentence completion
dataset:
type: xcopa
name: XCOPA (th)
config: th
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 57.0
- task:
type: Sentence completion
dataset:
type: xcopa
name: XCOPA (tr)
config: tr
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 53.0
- task:
type: Sentence completion
dataset:
type: xcopa
name: XCOPA (vi)
config: vi
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 79.0
- task:
type: Sentence completion
dataset:
type: xcopa
name: XCOPA (zh)
config: zh
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 81.0
- task:
type: Sentence completion
dataset:
type: Muennighoff/xstory_cloze
name: XStoryCloze (ar)
config: ar
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 83.26
- task:
type: Sentence completion
dataset:
type: Muennighoff/xstory_cloze
name: XStoryCloze (es)
config: es
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 88.95
- task:
type: Sentence completion
dataset:
type: Muennighoff/xstory_cloze
name: XStoryCloze (eu)
config: eu
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 73.33
- task:
type: Sentence completion
dataset:
type: Muennighoff/xstory_cloze
name: XStoryCloze (hi)
config: hi
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 80.61
- task:
type: Sentence completion
dataset:
type: Muennighoff/xstory_cloze
name: XStoryCloze (id)
config: id
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 84.25
- task:
type: Sentence completion
dataset:
type: Muennighoff/xstory_cloze
name: XStoryCloze (my)
config: my
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 52.55
- task:
type: Sentence completion
dataset:
type: Muennighoff/xstory_cloze
name: XStoryCloze (ru)
config: ru
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 65.32
- task:
type: Sentence completion
dataset:
type: Muennighoff/xstory_cloze
name: XStoryCloze (sw)
config: sw
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 71.67
- task:
type: Sentence completion
dataset:
type: Muennighoff/xstory_cloze
name: XStoryCloze (te)
config: te
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 74.72
- task:
type: Sentence completion
dataset:
type: Muennighoff/xstory_cloze
name: XStoryCloze (zh)
config: zh
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 85.37
---
# Table of Contents
1. [Model Summary](#model-summary)
2. [Use](#use)
3. [Limitations](#limitations)
4. [Training](#training)
5. [Evaluation](#evaluation)
7. [Citation](#citation)
# Model Summary
> We present BLOOMZ & mT0, a family of models capable of following human instructions in dozens of languages zero-shot. We finetune BLOOM & mT5 pretrained multilingual language models on our crosslingual task mixture (xP3) and find the resulting models capable of crosslingual generalization to unseen tasks & languages.
- **Repository:** [bigscience-workshop/xmtf](https://github.com/bigscience-workshop/xmtf)
- **Paper:** [Crosslingual Generalization through Multitask Finetuning](https://arxiv.org/abs/2211.01786)
- **Point of Contact:** [Niklas Muennighoff](mailto:[email protected])
- **Languages:** Refer to [bloom](https://huggingface.co/bigscience/bloom) for pretraining & [xP3](https://huggingface.co/datasets/bigscience/xP3) for finetuning language proportions. It understands both pretraining & finetuning languages.
- **BLOOMZ & mT0 Model Family:**
<div class="max-w-full overflow-auto">
<table>
<tr>
<th colspan="12">Multitask finetuned on <a style="font-weight:bold" href=https://huggingface.co/datasets/bigscience/xP3>xP3</a>. Recommended for prompting in English.
</tr>
<tr>
<td>Parameters</td>
<td>300M</td>
<td>580M</td>
<td>1.2B</td>
<td>3.7B</td>
<td>13B</td>
<td>560M</td>
<td>1.1B</td>
<td>1.7B</td>
<td>3B</td>
<td>7.1B</td>
<td>176B</td>
</tr>
<tr>
<td>Finetuned Model</td>
<td><a href=https://huggingface.co/bigscience/mt0-small>mt0-small</a></td>
<td><a href=https://huggingface.co/bigscience/mt0-base>mt0-base</a></td>
<td><a href=https://huggingface.co/bigscience/mt0-large>mt0-large</a></td>
<td><a href=https://huggingface.co/bigscience/mt0-xl>mt0-xl</a></td>
<td><a href=https://huggingface.co/bigscience/mt0-xxl>mt0-xxl</a></td>
<td><a href=https://huggingface.co/bigscience/bloomz-560m>bloomz-560m</a></td>
<td><a href=https://huggingface.co/bigscience/bloomz-1b1>bloomz-1b1</a></td>
<td><a href=https://huggingface.co/bigscience/bloomz-1b7>bloomz-1b7</a></td>
<td><a href=https://huggingface.co/bigscience/bloomz-3b>bloomz-3b</a></td>
<td><a href=https://huggingface.co/bigscience/bloomz-7b1>bloomz-7b1</a></td>
<td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a></td>
</tr>
</tr>
<tr>
<th colspan="12">Multitask finetuned on <a style="font-weight:bold" href=https://huggingface.co/datasets/bigscience/xP3mt>xP3mt</a>. Recommended for prompting in non-English.</th>
</tr>
<tr>
<td>Finetuned Model</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td><a href=https://huggingface.co/bigscience/mt0-xxl-mt>mt0-xxl-mt</a></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td><a href=https://huggingface.co/bigscience/bloomz-7b1-mt>bloomz-7b1-mt</a></td>
<td><a href=https://huggingface.co/bigscience/bloomz-mt>bloomz-mt</a></td>
</tr>
<th colspan="12">Multitask finetuned on <a style="font-weight:bold" href=https://huggingface.co/datasets/Muennighoff/P3>P3</a>. Released for research purposes only. Strictly inferior to above models!</th>
</tr>
<tr>
<td>Finetuned Model</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td><a href=https://huggingface.co/bigscience/mt0-xxl-p3>mt0-xxl-p3</a></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td><a href=https://huggingface.co/bigscience/bloomz-7b1-p3>bloomz-7b1-p3</a></td>
<td><a href=https://huggingface.co/bigscience/bloomz-p3>bloomz-p3</a></td>
</tr>
<th colspan="12">Original pretrained checkpoints. Not recommended.</th>
<tr>
<td>Pretrained Model</td>
<td><a href=https://huggingface.co/google/mt5-small>mt5-small</a></td>
<td><a href=https://huggingface.co/google/mt5-base>mt5-base</a></td>
<td><a href=https://huggingface.co/google/mt5-large>mt5-large</a></td>
<td><a href=https://huggingface.co/google/mt5-xl>mt5-xl</a></td>
<td><a href=https://huggingface.co/google/mt5-xxl>mt5-xxl</a></td>
<td><a href=https://huggingface.co/bigscience/bloom-560m>bloom-560m</a></td>
<td><a href=https://huggingface.co/bigscience/bloom-1b1>bloom-1b1</a></td>
<td><a href=https://huggingface.co/bigscience/bloom-1b7>bloom-1b7</a></td>
<td><a href=https://huggingface.co/bigscience/bloom-3b>bloom-3b</a></td>
<td><a href=https://huggingface.co/bigscience/bloom-7b1>bloom-7b1</a></td>
<td><a href=https://huggingface.co/bigscience/bloom>bloom</a></td>
</tr>
</table>
</div>
# Use
## Intended use
We recommend using the model to perform tasks expressed in natural language. For example, given the prompt "*Translate to English: Je t’aime.*", the model will most likely answer "*I love you.*". Some prompt ideas from our paper:
- 一个传奇的开端，一个不灭的神话，这不仅仅是一部电影，而是作为一个走进新时代的标签，永远彪炳史册。你认为这句话的立场是赞扬、中立还是批评?
- Suggest at least five related search terms to "Mạng neural nhân tạo".
- Write a fairy tale about a troll saving a princess from a dangerous dragon. The fairy tale is a masterpiece that has achieved praise worldwide and its moral is "Heroes Come in All Shapes and Sizes". Story (in Spanish):
- Explain in a sentence in Telugu what is backpropagation in neural networks.
**Feel free to share your generations in the Community tab!**
## How to use
### CPU
<details>
<summary> Click to expand </summary>
```python
# pip install -q transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "bigscience/bloomz-7b1"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint)
inputs = tokenizer.encode("Translate to English: Je t’aime.", return_tensors="pt")
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
```
</details>
### GPU
<details>
<summary> Click to expand </summary>
```python
# pip install -q transformers accelerate
from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "bigscience/bloomz-7b1"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint, torch_dtype="auto", device_map="auto")
inputs = tokenizer.encode("Translate to English: Je t’aime.", return_tensors="pt").to("cuda")
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
```
</details>
### GPU in 8bit
<details>
<summary> Click to expand </summary>
```python
# pip install -q transformers accelerate bitsandbytes
from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "bigscience/bloomz-7b1"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto", load_in_8bit=True)
inputs = tokenizer.encode("Translate to English: Je t’aime.", return_tensors="pt").to("cuda")
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
```
</details>
<!-- Necessary for whitespace -->
###
# Limitations
**Prompt Engineering:** The performance may vary depending on the prompt. For BLOOMZ models, we recommend making it very clear when the input stops to avoid the model trying to continue it. For example, the prompt "*Translate to English: Je t'aime*" without the full stop (.) at the end, may result in the model trying to continue the French sentence. Better prompts are e.g. "*Translate to English: Je t'aime.*", "*Translate to English: Je t'aime. Translation:*" "*What is "Je t'aime." in English?*", where it is clear for the model when it should answer. Further, we recommend providing the model as much context as possible. For example, if you want it to answer in Telugu, then tell the model, e.g. "*Explain in a sentence in Telugu what is backpropagation in neural networks.*".
# Training
## Model
- **Architecture:** Same as [bloom-7b1](https://huggingface.co/bigscience/bloom-7b1), also refer to the `config.json` file
- **Finetuning steps:** 1000
- **Finetuning tokens:** 4.19 billion
- **Finetuning layout:** 1x pipeline parallel, 1x tensor parallel, 64x data parallel
- **Precision:** float16
## Hardware
- **CPUs:** AMD CPUs with 512GB memory per node
- **GPUs:** 64 A100 80GB GPUs with 8 GPUs per node (8 nodes) using NVLink 4 inter-gpu connects, 4 OmniPath links
- **Communication:** NCCL-communications network with a fully dedicated subnet
## Software
- **Orchestration:** [Megatron-DeepSpeed](https://github.com/bigscience-workshop/Megatron-DeepSpeed)
- **Optimizer & parallelism:** [DeepSpeed](https://github.com/microsoft/DeepSpeed)
- **Neural networks:** [PyTorch](https://github.com/pytorch/pytorch) (pytorch-1.11 w/ CUDA-11.5)
- **FP16 if applicable:** [apex](https://github.com/NVIDIA/apex)
# Evaluation
We refer to Table 7 from our [paper](https://arxiv.org/abs/2211.01786) & [bigscience/evaluation-results](https://huggingface.co/datasets/bigscience/evaluation-results) for zero-shot results on unseen tasks. The sidebar reports zero-shot performance of the best prompt per dataset config.
# Citation
```bibtex
@article{muennighoff2022crosslingual,
title={Crosslingual generalization through multitask finetuning},
author={Muennighoff, Niklas and Wang, Thomas and Sutawika, Lintang and Roberts, Adam and Biderman, Stella and Scao, Teven Le and Bari, M Saiful and Shen, Sheng and Yong, Zheng-Xin and Schoelkopf, Hailey and others},
journal={arXiv preprint arXiv:2211.01786},
year={2022}
}
``` |
bartowski/Phi-3-medium-128k-instruct-GGUF | bartowski | "2024-05-21T21:50:55Z" | 19,651 | 42 | null | [
"gguf",
"nlp",
"code",
"text-generation",
"multilingual",
"license:mit",
"region:us"
] | text-generation | "2024-05-21T21:16:14Z" | ---
license: mit
license_link: https://huggingface.co/microsoft/Phi-3-medium-128k-instruct/resolve/main/LICENSE
language:
- multilingual
pipeline_tag: text-generation
tags:
- nlp
- code
inference:
parameters:
temperature: 0.7
widget:
- messages:
- role: user
content: Can you provide ways to eat combinations of bananas and dragonfruits?
quantized_by: bartowski
---
## Llamacpp imatrix Quantizations of Phi-3-medium-128k-instruct
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> pull request <a href="https://github.com/ggerganov/llama.cpp/pull/7225">7225</a> for quantization.
Original model: https://huggingface.co/microsoft/Phi-3-medium-128k-instruct
All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/b6ac44691e994344625687afe3263b3a)
## Prompt format
```
<|user|> {prompt}<|end|><|assistant|><|end|>
```
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [Phi-3-medium-128k-instruct-Q8_0.gguf](https://huggingface.co/bartowski/Phi-3-medium-128k-instruct-GGUF/blob/main/Phi-3-medium-128k-instruct-Q8_0.gguf) | Q8_0 | 14.83GB | Extremely high quality, generally unneeded but max available quant. |
| [Phi-3-medium-128k-instruct-Q6_K.gguf](https://huggingface.co/bartowski/Phi-3-medium-128k-instruct-GGUF/blob/main/Phi-3-medium-128k-instruct-Q6_K.gguf) | Q6_K | 11.45GB | Very high quality, near perfect, *recommended*. |
| [Phi-3-medium-128k-instruct-Q5_K_M.gguf](https://huggingface.co/bartowski/Phi-3-medium-128k-instruct-GGUF/blob/main/Phi-3-medium-128k-instruct-Q5_K_M.gguf) | Q5_K_M | 10.07GB | High quality, *recommended*. |
| [Phi-3-medium-128k-instruct-Q5_K_S.gguf](https://huggingface.co/bartowski/Phi-3-medium-128k-instruct-GGUF/blob/main/Phi-3-medium-128k-instruct-Q5_K_S.gguf) | Q5_K_S | 9.62GB | High quality, *recommended*. |
| [Phi-3-medium-128k-instruct-Q4_K_M.gguf](https://huggingface.co/bartowski/Phi-3-medium-128k-instruct-GGUF/blob/main/Phi-3-medium-128k-instruct-Q4_K_M.gguf) | Q4_K_M | 8.56GB | Good quality, uses about 4.83 bits per weight, *recommended*. |
| [Phi-3-medium-128k-instruct-Q4_K_S.gguf](https://huggingface.co/bartowski/Phi-3-medium-128k-instruct-GGUF/blob/main/Phi-3-medium-128k-instruct-Q4_K_S.gguf) | Q4_K_S | 7.95GB | Slightly lower quality with more space savings, *recommended*. |
| [Phi-3-medium-128k-instruct-IQ4_NL.gguf](https://huggingface.co/bartowski/Phi-3-medium-128k-instruct-GGUF/blob/main/Phi-3-medium-128k-instruct-IQ4_NL.gguf) | IQ4_NL | 7.89GB | Decent quality, slightly smaller than Q4_K_S with similar performance *recommended*. |
| [Phi-3-medium-128k-instruct-IQ4_XS.gguf](https://huggingface.co/bartowski/Phi-3-medium-128k-instruct-GGUF/blob/main/Phi-3-medium-128k-instruct-IQ4_XS.gguf) | IQ4_XS | 7.46GB | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [Phi-3-medium-128k-instruct-Q3_K_L.gguf](https://huggingface.co/bartowski/Phi-3-medium-128k-instruct-GGUF/blob/main/Phi-3-medium-128k-instruct-Q3_K_L.gguf) | Q3_K_L | 7.49GB | Lower quality but usable, good for low RAM availability. |
| [Phi-3-medium-128k-instruct-Q3_K_M.gguf](https://huggingface.co/bartowski/Phi-3-medium-128k-instruct-GGUF/blob/main/Phi-3-medium-128k-instruct-Q3_K_M.gguf) | Q3_K_M | 6.92GB | Even lower quality. |
| [Phi-3-medium-128k-instruct-IQ3_M.gguf](https://huggingface.co/bartowski/Phi-3-medium-128k-instruct-GGUF/blob/main/Phi-3-medium-128k-instruct-IQ3_M.gguf) | IQ3_M | 6.47GB | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [Phi-3-medium-128k-instruct-IQ3_S.gguf](https://huggingface.co/bartowski/Phi-3-medium-128k-instruct-GGUF/blob/main/Phi-3-medium-128k-instruct-IQ3_S.gguf) | IQ3_S | 6.06GB | Lower quality, new method with decent performance, recommended over Q3_K_S quant, same size with better performance. |
| [Phi-3-medium-128k-instruct-Q3_K_S.gguf](https://huggingface.co/bartowski/Phi-3-medium-128k-instruct-GGUF/blob/main/Phi-3-medium-128k-instruct-Q3_K_S.gguf) | Q3_K_S | 6.06GB | Low quality, not recommended. |
| [Phi-3-medium-128k-instruct-IQ3_XS.gguf](https://huggingface.co/bartowski/Phi-3-medium-128k-instruct-GGUF/blob/main/Phi-3-medium-128k-instruct-IQ3_XS.gguf) | IQ3_XS | 5.80GB | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| [Phi-3-medium-128k-instruct-IQ3_XXS.gguf](https://huggingface.co/bartowski/Phi-3-medium-128k-instruct-GGUF/blob/main/Phi-3-medium-128k-instruct-IQ3_XXS.gguf) | IQ3_XXS | 5.45GB | Lower quality, new method with decent performance, comparable to Q3 quants. |
| [Phi-3-medium-128k-instruct-Q2_K.gguf](https://huggingface.co/bartowski/Phi-3-medium-128k-instruct-GGUF/blob/main/Phi-3-medium-128k-instruct-Q2_K.gguf) | Q2_K | 5.14GB | Very low quality but surprisingly usable. |
| [Phi-3-medium-128k-instruct-IQ2_M.gguf](https://huggingface.co/bartowski/Phi-3-medium-128k-instruct-GGUF/blob/main/Phi-3-medium-128k-instruct-IQ2_M.gguf) | IQ2_M | 4.71GB | Very low quality, uses SOTA techniques to also be surprisingly usable. |
| [Phi-3-medium-128k-instruct-IQ2_S.gguf](https://huggingface.co/bartowski/Phi-3-medium-128k-instruct-GGUF/blob/main/Phi-3-medium-128k-instruct-IQ2_S.gguf) | IQ2_S | 4.33GB | Very low quality, uses SOTA techniques to be usable. |
| [Phi-3-medium-128k-instruct-IQ2_XS.gguf](https://huggingface.co/bartowski/Phi-3-medium-128k-instruct-GGUF/blob/main/Phi-3-medium-128k-instruct-IQ2_XS.gguf) | IQ2_XS | 4.12GB | Very low quality, uses SOTA techniques to be usable. |
| [Phi-3-medium-128k-instruct-IQ2_XXS.gguf](https://huggingface.co/bartowski/Phi-3-medium-128k-instruct-GGUF/blob/main/Phi-3-medium-128k-instruct-IQ2_XXS.gguf) | IQ2_XXS | 3.71GB | Lower quality, uses SOTA techniques to be usable. |
| [Phi-3-medium-128k-instruct-IQ1_M.gguf](https://huggingface.co/bartowski/Phi-3-medium-128k-instruct-GGUF/blob/main/Phi-3-medium-128k-instruct-IQ1_M.gguf) | IQ1_M | 3.24GB | Extremely low quality, *not* recommended. |
| [Phi-3-medium-128k-instruct-IQ1_S.gguf](https://huggingface.co/bartowski/Phi-3-medium-128k-instruct-GGUF/blob/main/Phi-3-medium-128k-instruct-IQ1_S.gguf) | IQ1_S | 2.95GB | Extremely low quality, *not* recommended. |
## Downloading using huggingface-cli
First, make sure you have hugginface-cli installed:
```
pip install -U "huggingface_hub[cli]"
```
Then, you can target the specific file you want:
```
huggingface-cli download bartowski/Phi-3-medium-128k-instruct-GGUF --include "Phi-3-medium-128k-instruct-Q4_K_M.gguf" --local-dir ./
```
If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:
```
huggingface-cli download bartowski/Phi-3-medium-128k-instruct-GGUF --include "Phi-3-medium-128k-instruct-Q8_0.gguf/*" --local-dir Phi-3-medium-128k-instruct-Q8_0
```
You can either specify a new local-dir (Phi-3-medium-128k-instruct-Q8_0) or download them all in place (./)
## Which file should I choose?
A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)
The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.
If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.
If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.
Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.
If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.
If you want to get more into the weeds, you can check out this extremely useful feature chart:
[llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix)
But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.
These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.
The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm.
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
|
LanguageBind/Video-LLaVA-7B-hf | LanguageBind | "2024-05-16T10:42:41Z" | 19,649 | 29 | transformers | [
"transformers",
"safetensors",
"video_llava",
"pretraining",
"arxiv:2311.10122",
"endpoints_compatible",
"region:us"
] | null | "2024-05-09T22:49:47Z" | ---
library_name: transformers
tags: []
---
# Model Card for Video-LLaVa
## Model Details
**Model type:**
Video-LLaVA is an open-source multomodal model trained by fine-tuning LLM on multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture.
Base LLM: [lmsys/vicuna-13b-v1.5](https://huggingface.co/lmsys/vicuna-13b-v1.5)
**Model Description:**
The model can generate interleaving images and videos, despite the absence of image-video pairs in the dataset. Video-LLaVa is uses an encoder trained for unified visual representation through alignment prior to projection.
Extensive experiments demonstrate the complementarity of modalities, showcasing significant superiority when compared to models specifically designed for either images or videos.
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/videollava_example.png"
alt="drawing" width="600"/>
<small> VideoLLaVa example. Taken from the <a href="https://arxiv.org/abs/2311.10122">original paper.</a> </small>
**Paper or resources for more information:**
https://github.com/PKU-YuanGroup/Video-LLaVA
## 🗝️ Training Dataset
- The images pretraining dataset is from [LLaVA](https://github.com/haotian-liu/LLaVA).
- The images tuning dataset is from [LLaVA](https://github.com/haotian-liu/LLaVA).
- The videos pretraining dataset is from [Valley](https://github.com/RupertLuo/Valley).
- The videos tuning dataset is from [Video-ChatGPT](https://github.com/mbzuai-oryx/Video-ChatGPT).
## How to Get Started with the Model
Use the code below to get started with the model.
```python
from PIL import Image
import requests
import numpy as np
import av
from huggingface_hub import hf_hub_download
from transformers import VideoLlavaProcessor, VideoLlavaForConditionalGeneration
def read_video_pyav(container, indices):
'''
Decode the video with PyAV decoder.
Args:
container (av.container.input.InputContainer): PyAV container.
indices (List[int]): List of frame indices to decode.
Returns:
np.ndarray: np array of decoded frames of shape (num_frames, height, width, 3).
'''
frames = []
container.seek(0)
start_index = indices[0]
end_index = indices[-1]
for i, frame in enumerate(container.decode(video=0)):
if i > end_index:
break
if i >= start_index and i in indices:
frames.append(frame)
return np.stack([x.to_ndarray(format="rgb24") for x in frames])
model = VideoLlavaForConditionalGeneration.from_pretrained("LanguageBind/Video-LLaVA-7B-hf")
processor = VideoLlavaProcessor.from_pretrained("LanguageBind/Video-LLaVA-7B-hf")
prompt = "USER: <video>Why is this video funny? ASSISTANT:"
video_path = hf_hub_download(repo_id="raushan-testing-hf/videos-test", filename="sample_demo_1.mp4", repo_type="dataset")
container = av.open(video_path)
# sample uniformly 8 frames from the video
total_frames = container.streams.video[0].frames
indices = np.arange(0, total_frames, total_frames / 8).astype(int)
clip = read_video_pyav(container, indices)
inputs = processor(text=prompt, videos=clip, return_tensors="pt")
# Generate
generate_ids = model.generate(**inputs, max_length=80)
print(processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0])
>>> 'USER: Why is this video funny? ASSISTANT: The video is funny because the baby is sitting on the bed and reading a book, which is an unusual and amusing sight.Ъ'
# Generate from images and videos mix
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
prompt = [
"USER: <image> How many cats are there in the image? ASSISTANT:",
"USER: <video>Why is this video funny? ASSISTANT:"
]
inputs = processor(text=prompt, images=image, videos=clip, padding=True, return_tensors="pt")
# Generate
generate_ids = model.generate(**inputs, max_length=50)
print(processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True))
>>> ['USER: How many cats are there in the image? ASSISTANT: There are two cats in the image.\nHow many cats are sleeping on the couch?\nThere are', 'USER: Why is this video funny? ASSISTANT: The video is funny because the baby is sitting on the bed and reading a book, which is an unusual and amusing']
```
## 👍 Acknowledgement
* [LLaVA](https://github.com/haotian-liu/LLaVA) The codebase we built upon and it is an efficient large language and vision assistant.
* [Video-ChatGPT](https://github.com/mbzuai-oryx/Video-ChatGPT) Great job contributing the evaluation code and dataset.
## 🔒 License
* The majority of this project is released under the Apache 2.0 license as found in the [LICENSE](https://github.com/PKU-YuanGroup/Video-LLaVA/blob/main/LICENSE) file.
* The service is a research preview intended for non-commercial use only, subject to the model [License](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) of LLaMA, [Terms of Use](https://openai.com/policies/terms-of-use) of the data generated by OpenAI, and [Privacy Practices](https://chrome.google.com/webstore/detail/sharegpt-share-your-chatg/daiacboceoaocpibfodeljbdfacokfjb) of ShareGPT. Please contact us if you find any potential violation.
## ✏️ Citation
If you find our paper and code useful in your research, please consider giving a star :star: and citation :pencil:.
```BibTeX
@article{lin2023video,
title={Video-LLaVA: Learning United Visual Representation by Alignment Before Projection},
author={Lin, Bin and Zhu, Bin and Ye, Yang and Ning, Munan and Jin, Peng and Yuan, Li},
journal={arXiv preprint arXiv:2311.10122},
year={2023}
}
```
```BibTeX
@article{zhu2023languagebind,
title={LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment},
author={Zhu, Bin and Lin, Bin and Ning, Munan and Yan, Yang and Cui, Jiaxi and Wang, HongFa and Pang, Yatian and Jiang, Wenhao and Zhang, Junwu and Li, Zongwei and others},
journal={arXiv preprint arXiv:2310.01852},
year={2023}
}
```
|
elastic/distilbert-base-cased-finetuned-conll03-english | elastic | "2023-04-20T10:32:13Z" | 19,638 | 14 | transformers | [
"transformers",
"pytorch",
"safetensors",
"distilbert",
"token-classification",
"en",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | "2022-03-02T23:29:05Z" | ---
language: en
license: apache-2.0
datasets:
- conll2003
model-index:
- name: elastic/distilbert-base-cased-finetuned-conll03-english
results:
- task:
type: token-classification
name: Token Classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
metrics:
- type: accuracy
value: 0.9834432212868665
name: Accuracy
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOTZmZTJlMzUzOTAzZjg3N2UxNmMxMjQ2M2FhZTM4MDdkYzYyYTYyNjM1YjQ0M2Y4ZmIyMzkwMmY5YjZjZGVhYSIsInZlcnNpb24iOjF9.QaSLUR7AtQmE9F-h6EBueF6INQgdKwUUzS3bNvRu44rhNDY1KAJJkmDC8FeAIVMnlOSvPKvr5pOvJ59W1zckCw
- type: precision
value: 0.9857564461012737
name: Precision
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMDVmNmNmNWIwNTI0Yzc0YTI1NTk2NDM4YjY4NzY0ODQ4NzQ5MDQxMzYyYWM4YzUwNmYxZWQ1NTU2YTZiM2U2MCIsInZlcnNpb24iOjF9.ui_o64VBS_oC89VeQTx_B-nUUM0ZaivFyb6wNrYZcopJXvYgzptLCkARdBKdBajFjjupdhtq1VCdGbJ3yaXgBA
- type: recall
value: 0.9882123948925569
name: Recall
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiODg4Mzg1NTY3NjU4ZGQxOGVhMzQxNWU0ZTYxNWM2ZTg1OGZlM2U5ZGMxYTA2NzdiZjM5YWFkZjkzOGYwYTlkMyIsInZlcnNpb24iOjF9.8jHJv_5ZQp_CX3-k8-C3c5Hs4zp7bJPRTeE5SFrNgeX-FdhPv_8bHBM_DqOD2P_nkAzQ_PtEFfEokQpouZFJCw
- type: f1
value: 0.9869828926905132
name: F1
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYzZlOGRjMDllYWY5MjdhODk2MmNmMDk5MDQyZGYzZDYwZTE1ZDY2MDNlMzAzN2JlMmE5Y2M3ZTNkOWE2MDBjYyIsInZlcnNpb24iOjF9.VKwzPQFSbrnUZ25gkKUZvYO_xFZcaTOSkDcN-YCxksF5DRnKudKI2HmvO8l8GCsQTCoD4DiSTKzghzLMxB1jCg
- type: loss
value: 0.07748260349035263
name: loss
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNmVmOTQ2MWI2MzZhY2U2ODQ3YjA0ZWVjYzU1NGRlMTczZDI0NmM0OWI4YmIzMmEyYjlmNDIwYmRiODM4MWM0YiIsInZlcnNpb24iOjF9.0Prq087l2Xfh-ceS99zzUDcKM4Vr4CLM2rF1F1Fqd2fj9MOhVZEXF4JACVn0fWAFqfZIPS2GD8sSwfNYaXkZAA
---
[DistilBERT base cased](https://huggingface.co/distilbert-base-cased), fine-tuned for NER using the [conll03 english dataset](https://huggingface.co/datasets/conll2003). Note that this model is sensitive to capital letters — "english" is different than "English". For the case insensitive version, please use [elastic/distilbert-base-uncased-finetuned-conll03-english](https://huggingface.co/elastic/distilbert-base-uncased-finetuned-conll03-english).
## Versions
- Transformers version: 4.3.1
- Datasets version: 1.3.0
## Training
```
$ run_ner.py \
--model_name_or_path distilbert-base-cased \
--label_all_tokens True \
--return_entity_level_metrics True \
--dataset_name conll2003 \
--output_dir /tmp/distilbert-base-cased-finetuned-conll03-english \
--do_train \
--do_eval
```
After training, we update the labels to match the NER specific labels from the
dataset [conll2003](https://raw.githubusercontent.com/huggingface/datasets/1.3.0/datasets/conll2003/dataset_infos.json)
|
mradermacher/hksuicide-extractor-llama-16b-GGUF | mradermacher | "2024-06-27T17:30:06Z" | 19,634 | 0 | transformers | [
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:iharrisonfu/hksuicide-extractor-llama-16b",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-27T17:01:23Z" | ---
base_model: iharrisonfu/hksuicide-extractor-llama-16b
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/iharrisonfu/hksuicide-extractor-llama-16b
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/hksuicide-extractor-llama-16b-GGUF/resolve/main/hksuicide-extractor-llama-16b.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/hksuicide-extractor-llama-16b-GGUF/resolve/main/hksuicide-extractor-llama-16b.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/hksuicide-extractor-llama-16b-GGUF/resolve/main/hksuicide-extractor-llama-16b.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/hksuicide-extractor-llama-16b-GGUF/resolve/main/hksuicide-extractor-llama-16b.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/hksuicide-extractor-llama-16b-GGUF/resolve/main/hksuicide-extractor-llama-16b.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/hksuicide-extractor-llama-16b-GGUF/resolve/main/hksuicide-extractor-llama-16b.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/hksuicide-extractor-llama-16b-GGUF/resolve/main/hksuicide-extractor-llama-16b.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/hksuicide-extractor-llama-16b-GGUF/resolve/main/hksuicide-extractor-llama-16b.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/hksuicide-extractor-llama-16b-GGUF/resolve/main/hksuicide-extractor-llama-16b.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/hksuicide-extractor-llama-16b-GGUF/resolve/main/hksuicide-extractor-llama-16b.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/hksuicide-extractor-llama-16b-GGUF/resolve/main/hksuicide-extractor-llama-16b.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/hksuicide-extractor-llama-16b-GGUF/resolve/main/hksuicide-extractor-llama-16b.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/hksuicide-extractor-llama-16b-GGUF/resolve/main/hksuicide-extractor-llama-16b.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/hksuicide-extractor-llama-16b-GGUF/resolve/main/hksuicide-extractor-llama-16b.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/hksuicide-extractor-llama-16b-GGUF/resolve/main/hksuicide-extractor-llama-16b.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
QuantFactory/gemma-2-9b-it-GGUF | QuantFactory | "2024-06-29T00:51:08Z" | 19,633 | 1 | transformers | [
"transformers",
"gguf",
"conversational",
"text-generation",
"arxiv:2009.03300",
"arxiv:1905.07830",
"arxiv:1911.11641",
"arxiv:1904.09728",
"arxiv:1905.10044",
"arxiv:1907.10641",
"arxiv:1811.00937",
"arxiv:1809.02789",
"arxiv:1911.01547",
"arxiv:1705.03551",
"arxiv:2107.03374",
"arxiv:2108.07732",
"arxiv:2110.14168",
"arxiv:2009.11462",
"arxiv:2101.11718",
"arxiv:2110.08193",
"arxiv:1804.09301",
"arxiv:2109.07958",
"arxiv:1804.06876",
"arxiv:2103.03874",
"arxiv:2304.06364",
"arxiv:2206.04615",
"arxiv:2203.09509",
"base_model:google/gemma-2-9b-it",
"license:gemma",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-06-28T23:47:24Z" | ---
license: gemma
library_name: transformers
pipeline_tag: text-generation
tags:
- conversational
base_model: google/gemma-2-9b-it
---
# QuantFactory/gemma-2-9b-it-GGUF
This is quantized version of [google/gemma-2-9b-it](https://huggingface.co/google/gemma-2-9b-it) created using llama.cpp
# Gemma 2 model card
**Model Page**: [Gemma](https://ai.google.dev/gemma/docs)
**Resources and Technical Documentation**:
* [Responsible Generative AI Toolkit][rai-toolkit]
* [Gemma on Kaggle][kaggle-gemma]
* [Gemma on Vertex Model Garden][vertex-mg-gemma]
**Terms of Use**: [Terms](https://www.kaggle.com/models/google/gemma/license/consent/verify/huggingface?returnModelRepoId=google/gemma-2-9b-it)
**Authors**: Google
## Model Information
Summary description and brief definition of inputs and outputs.
### Description
Gemma is a family of lightweight, state-of-the-art open models from Google,
built from the same research and technology used to create the Gemini models.
They are text-to-text, decoder-only large language models, available in English,
with open weights for both pre-trained variants and instruction-tuned variants.
Gemma models are well-suited for a variety of text generation tasks, including
question answering, summarization, and reasoning. Their relatively small size
makes it possible to deploy them in environments with limited resources such as
a laptop, desktop or your own cloud infrastructure, democratizing access to
state of the art AI models and helping foster innovation for everyone.
### Usage
Below we share some code snippets on how to get quickly started with running the model. First make sure to `pip install -U transformers`, then copy the snippet from the section that is relevant for your usecase.
#### Running the model on a single / multi GPU
```python
# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-9b-it")
model = AutoModelForCausalLM.from_pretrained(
"google/gemma-2-9b-it",
device_map="auto",
torch_dtype=torch.bfloat16
)
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
<a name="precisions"></a>
#### Running the model on a GPU using different precisions
The native weights of this model were exported in `bfloat16` precision. You can use `float16`, which may be faster on certain hardware, indicating the `torch_dtype` when loading the model. For convenience, the `float16` revision of the repo contains a copy of the weights already converted to that precision.
You can also use `float32` if you skip the dtype, but no precision increase will occur (model weights will just be upcasted to `float32`). See examples below.
* _Using `torch.float16`_
```python
# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-9b-it")
model = AutoModelForCausalLM.from_pretrained(
"google/gemma-2-9b-it",
device_map="auto",
torch_dtype=torch.float16,
revision="float16",
)
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
* _Using `torch.bfloat16`_
```python
# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-9b-it")
model = AutoModelForCausalLM.from_pretrained(
"google/gemma-2-9b-it",
device_map="auto",
torch_dtype=torch.bfloat16)
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
* _Upcasting to `torch.float32`_
```python
# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-9b-it")
model = AutoModelForCausalLM.from_pretrained(
"google/gemma-2-9b-it",
device_map="auto")
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
#### Quantized Versions through `bitsandbytes`
* _Using 8-bit precision (int8)_
```python
# pip install bitsandbytes accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
quantization_config = BitsAndBytesConfig(load_in_8bit=True)
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-9b-it")
model = AutoModelForCausalLM.from_pretrained(
"google/gemma-2-9b-it",
quantization_config=quantization_config)
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
* _Using 4-bit precision_
```python
# pip install bitsandbytes accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
quantization_config = BitsAndBytesConfig(load_in_4bit=True)
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-9b-it")
model = AutoModelForCausalLM.from_pretrained(
"google/gemma-2-9b-it",
quantization_config=quantization_config)
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
#### Other optimizations
* _Flash Attention 2_
First make sure to install `flash-attn` in your environment `pip install flash-attn`
```diff
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.float16,
+ attn_implementation="flash_attention_2"
).to(0)
```
### Chat Template
The instruction-tuned models use a chat template that must be adhered to for conversational use.
The easiest way to apply it is using the tokenizer's built-in chat template, as shown in the following snippet.
Let's load the model and apply the chat template to a conversation. In this example, we'll start with a single user interaction:
```py
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model_id = "google/gemma-2-9b-it"
dtype = torch.bfloat16
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="cuda",
torch_dtype=dtype,)
chat = [
{ "role": "user", "content": "Write a hello world program" },
]
prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
```
At this point, the prompt contains the following text:
```
<bos><start_of_turn>user
Write a hello world program<end_of_turn>
<start_of_turn>model
```
As you can see, each turn is preceded by a `<start_of_turn>` delimiter and then the role of the entity
(either `user`, for content supplied by the user, or `model` for LLM responses). Turns finish with
the `<end_of_turn>` token.
You can follow this format to build the prompt manually, if you need to do it without the tokenizer's
chat template.
After the prompt is ready, generation can be performed like this:
```py
inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=150)
print(tokenizer.decode(outputs[0]))
```
### Inputs and outputs
* **Input:** Text string, such as a question, a prompt, or a document to be
summarized.
* **Output:** Generated English-language text in response to the input, such
as an answer to a question, or a summary of a document.
### Model Citation
```none
@article{gemma_2024,
title={Gemma},
url={https://www.kaggle.com/m/3301},
DOI={10.34740/KAGGLE/M/3301},
publisher={Kaggle},
author={Gemma Team},
year={2024}
}
```
## Model Data
Data used for model training and how the data was processed.
### Training Dataset
These models were trained on a dataset of text data that includes a wide variety of sources. The 27B model was trained with 13 trillion tokens and the 9B model was trained with 8 trillion tokens.
Here are the key components:
* Web Documents: A diverse collection of web text ensures the model is exposed
to a broad range of linguistic styles, topics, and vocabulary. Primarily
English-language content.
* Code: Exposing the model to code helps it to learn the syntax and patterns of
programming languages, which improves its ability to generate code or
understand code-related questions.
* Mathematics: Training on mathematical text helps the model learn logical
reasoning, symbolic representation, and to address mathematical queries.
The combination of these diverse data sources is crucial for training a powerful
language model that can handle a wide variety of different tasks and text
formats.
### Data Preprocessing
Here are the key data cleaning and filtering methods applied to the training
data:
* CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering was
applied at multiple stages in the data preparation process to ensure the
exclusion of harmful and illegal content.
* Sensitive Data Filtering: As part of making Gemma pre-trained models safe and
reliable, automated techniques were used to filter out certain personal
information and other sensitive data from training sets.
* Additional methods: Filtering based on content quality and safety in line with
[our policies][safety-policies].
## Implementation Information
Details about the model internals.
### Hardware
Gemma was trained using the latest generation of
[Tensor Processing Unit (TPU)][tpu] hardware (TPUv5p).
Training large language models requires significant computational power. TPUs,
designed specifically for matrix operations common in machine learning, offer
several advantages in this domain:
* Performance: TPUs are specifically designed to handle the massive computations
involved in training LLMs. They can speed up training considerably compared to
CPUs.
* Memory: TPUs often come with large amounts of high-bandwidth memory, allowing
for the handling of large models and batch sizes during training. This can
lead to better model quality.
* Scalability: TPU Pods (large clusters of TPUs) provide a scalable solution for
handling the growing complexity of large foundation models. You can distribute
training across multiple TPU devices for faster and more efficient processing.
* Cost-effectiveness: In many scenarios, TPUs can provide a more cost-effective
solution for training large models compared to CPU-based infrastructure,
especially when considering the time and resources saved due to faster
training.
* These advantages are aligned with
[Google's commitments to operate sustainably][sustainability].
### Software
Training was done using [JAX][jax] and [ML Pathways][ml-pathways].
JAX allows researchers to take advantage of the latest generation of hardware,
including TPUs, for faster and more efficient training of large models.
ML Pathways is Google's latest effort to build artificially intelligent systems
capable of generalizing across multiple tasks. This is specially suitable for
[foundation models][foundation-models], including large language models like
these ones.
Together, JAX and ML Pathways are used as described in the
[paper about the Gemini family of models][gemini-2-paper]; "the 'single
controller' programming model of Jax and Pathways allows a single Python
process to orchestrate the entire training run, dramatically simplifying the
development workflow."
## Evaluation
Model evaluation metrics and results.
### Benchmark Results
These models were evaluated against a large collection of different datasets and
metrics to cover different aspects of text generation:
| Benchmark | Metric | Gemma PT 9B | Gemma PT 27B |
| ------------------------------ | ------------- | ----------- | ------------ |
| [MMLU][mmlu] | 5-shot, top-1 | 71.3 | 75.2 |
| [HellaSwag][hellaswag] | 10-shot | 81.9 | 86.4 |
| [PIQA][piqa] | 0-shot | 81.7 | 83.2 |
| [SocialIQA][socialiqa] | 0-shot | 53.4 | 53.7 |
| [BoolQ][boolq] | 0-shot | 84.2 | 84.8 |
| [WinoGrande][winogrande] | partial score | 80.6 | 83.7 |
| [ARC-e][arc] | 0-shot | 88.0 | 88.6 |
| [ARC-c][arc] | 25-shot | 68.4 | 71.4 |
| [TriviaQA][triviaqa] | 5-shot | 76.6 | 83.7 |
| [Natural Questions][naturalq] | 5-shot | 29.2 | 34.5 |
| [HumanEval][humaneval] | pass@1 | 40.2 | 51.8 |
| [MBPP][mbpp] | 3-shot | 52.4 | 62.6 |
| [GSM8K][gsm8k] | 5-shot, maj@1 | 68.6 | 74.0 |
| [MATH][math] | 4-shot | 36.6 | 42.3 |
| [AGIEval][agieval] | 3-5-shot | 52.8 | 55.1 |
| [BIG-Bench][big-bench] | 3-shot, CoT | 68.2 | 74.9 |
| ------------------------------ | ------------- | ----------- | ------------ |
## Ethics and Safety
Ethics and safety evaluation approach and results.
### Evaluation Approach
Our evaluation methods include structured evaluations and internal red-teaming
testing of relevant content policies. Red-teaming was conducted by a number of
different teams, each with different goals and human evaluation metrics. These
models were evaluated against a number of different categories relevant to
ethics and safety, including:
* Text-to-Text Content Safety: Human evaluation on prompts covering safety
policies including child sexual abuse and exploitation, harassment, violence
and gore, and hate speech.
* Text-to-Text Representational Harms: Benchmark against relevant academic
datasets such as [WinoBias][winobias] and [BBQ Dataset][bbq].
* Memorization: Automated evaluation of memorization of training data, including
the risk of personally identifiable information exposure.
* Large-scale harm: Tests for "dangerous capabilities," such as chemical,
biological, radiological, and nuclear (CBRN) risks.
### Evaluation Results
The results of ethics and safety evaluations are within acceptable thresholds
for meeting [internal policies][safety-policies] for categories such as child
safety, content safety, representational harms, memorization, large-scale harms.
On top of robust internal evaluations, the results of well-known safety
benchmarks like BBQ, BOLD, Winogender, Winobias, RealToxicity, and TruthfulQA
are shown here.
#### Gemma 2.0
| Benchmark | Metric | Gemma 2 IT 9B | Gemma 2 IT 27B |
| ------------------------ | ------------- | --------------- | ---------------- |
| [RealToxicity][realtox] | average | 8.25 | 8.84 |
| [CrowS-Pairs][crows] | top-1 | 37.47 | 36.67 |
| [BBQ Ambig][bbq] | 1-shot, top-1 | 88.58 | 85.99 |
| [BBQ Disambig][bbq] | top-1 | 82.67 | 86.94 |
| [Winogender][winogender] | top-1 | 79.17 | 77.22 |
| [TruthfulQA][truthfulqa] | | 50.27 | 51.60 |
| [Winobias 1_2][winobias] | | 78.09 | 81.94 |
| [Winobias 2_2][winobias] | | 95.32 | 97.22 |
| [Toxigen][toxigen] | | 39.30 | 38.42 |
| ------------------------ | ------------- | --------------- | ---------------- |
## Usage and Limitations
These models have certain limitations that users should be aware of.
### Intended Usage
Open Large Language Models (LLMs) have a wide range of applications across
various industries and domains. The following list of potential uses is not
comprehensive. The purpose of this list is to provide contextual information
about the possible use-cases that the model creators considered as part of model
training and development.
* Content Creation and Communication
* Text Generation: These models can be used to generate creative text formats
such as poems, scripts, code, marketing copy, and email drafts.
* Chatbots and Conversational AI: Power conversational interfaces for customer
service, virtual assistants, or interactive applications.
* Text Summarization: Generate concise summaries of a text corpus, research
papers, or reports.
* Research and Education
* Natural Language Processing (NLP) Research: These models can serve as a
foundation for researchers to experiment with NLP techniques, develop
algorithms, and contribute to the advancement of the field.
* Language Learning Tools: Support interactive language learning experiences,
aiding in grammar correction or providing writing practice.
* Knowledge Exploration: Assist researchers in exploring large bodies of text
by generating summaries or answering questions about specific topics.
### Limitations
* Training Data
* The quality and diversity of the training data significantly influence the
model's capabilities. Biases or gaps in the training data can lead to
limitations in the model's responses.
* The scope of the training dataset determines the subject areas the model can
handle effectively.
* Context and Task Complexity
* LLMs are better at tasks that can be framed with clear prompts and
instructions. Open-ended or highly complex tasks might be challenging.
* A model's performance can be influenced by the amount of context provided
(longer context generally leads to better outputs, up to a certain point).
* Language Ambiguity and Nuance
* Natural language is inherently complex. LLMs might struggle to grasp subtle
nuances, sarcasm, or figurative language.
* Factual Accuracy
* LLMs generate responses based on information they learned from their
training datasets, but they are not knowledge bases. They may generate
incorrect or outdated factual statements.
* Common Sense
* LLMs rely on statistical patterns in language. They might lack the ability
to apply common sense reasoning in certain situations.
### Ethical Considerations and Risks
The development of large language models (LLMs) raises several ethical concerns.
In creating an open model, we have carefully considered the following:
* Bias and Fairness
* LLMs trained on large-scale, real-world text data can reflect socio-cultural
biases embedded in the training material. These models underwent careful
scrutiny, input data pre-processing described and posterior evaluations
reported in this card.
* Misinformation and Misuse
* LLMs can be misused to generate text that is false, misleading, or harmful.
* Guidelines are provided for responsible use with the model, see the
[Responsible Generative AI Toolkit][rai-toolkit].
* Transparency and Accountability:
* This model card summarizes details on the models' architecture,
capabilities, limitations, and evaluation processes.
* A responsibly developed open model offers the opportunity to share
innovation by making LLM technology accessible to developers and researchers
across the AI ecosystem.
Risks identified and mitigations:
* Perpetuation of biases: It's encouraged to perform continuous monitoring
(using evaluation metrics, human review) and the exploration of de-biasing
techniques during model training, fine-tuning, and other use cases.
* Generation of harmful content: Mechanisms and guidelines for content safety
are essential. Developers are encouraged to exercise caution and implement
appropriate content safety safeguards based on their specific product policies
and application use cases.
* Misuse for malicious purposes: Technical limitations and developer and
end-user education can help mitigate against malicious applications of LLMs.
Educational resources and reporting mechanisms for users to flag misuse are
provided. Prohibited uses of Gemma models are outlined in the
[Gemma Prohibited Use Policy][prohibited-use].
* Privacy violations: Models were trained on data filtered for removal of PII
(Personally Identifiable Information). Developers are encouraged to adhere to
privacy regulations with privacy-preserving techniques.
### Benefits
At the time of release, this family of models provides high-performance open
large language model implementations designed from the ground up for Responsible
AI development compared to similarly sized models.
Using the benchmark evaluation metrics described in this document, these models
have shown to provide superior performance to other, comparably-sized open model
alternatives.
[rai-toolkit]: https://ai.google.dev/responsible
[kaggle-gemma]: https://www.kaggle.com/models/google/gemma-2
[terms]: https://ai.google.dev/gemma/terms
[vertex-mg-gemma]: https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/335
[sensitive-info]: https://cloud.google.com/dlp/docs/high-sensitivity-infotypes-reference
[safety-policies]: https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11
[prohibited-use]: https://ai.google.dev/gemma/prohibited_use_policy
[tpu]: https://cloud.google.com/tpu/docs/intro-to-tpu
[sustainability]: https://sustainability.google/operating-sustainably/
[jax]: https://github.com/google/jax
[ml-pathways]: https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/
[sustainability]: https://sustainability.google/operating-sustainably/
[foundation-models]: https://ai.google/discover/foundation-models/
[gemini-2-paper]: https://goo.gle/gemma2report
[mmlu]: https://arxiv.org/abs/2009.03300
[hellaswag]: https://arxiv.org/abs/1905.07830
[piqa]: https://arxiv.org/abs/1911.11641
[socialiqa]: https://arxiv.org/abs/1904.09728
[boolq]: https://arxiv.org/abs/1905.10044
[winogrande]: https://arxiv.org/abs/1907.10641
[commonsenseqa]: https://arxiv.org/abs/1811.00937
[openbookqa]: https://arxiv.org/abs/1809.02789
[arc]: https://arxiv.org/abs/1911.01547
[triviaqa]: https://arxiv.org/abs/1705.03551
[naturalq]: https://github.com/google-research-datasets/natural-questions
[humaneval]: https://arxiv.org/abs/2107.03374
[mbpp]: https://arxiv.org/abs/2108.07732
[gsm8k]: https://arxiv.org/abs/2110.14168
[realtox]: https://arxiv.org/abs/2009.11462
[bold]: https://arxiv.org/abs/2101.11718
[crows]: https://aclanthology.org/2020.emnlp-main.154/
[bbq]: https://arxiv.org/abs/2110.08193v2
[winogender]: https://arxiv.org/abs/1804.09301
[truthfulqa]: https://arxiv.org/abs/2109.07958
[winobias]: https://arxiv.org/abs/1804.06876
[math]: https://arxiv.org/abs/2103.03874
[agieval]: https://arxiv.org/abs/2304.06364
[big-bench]: https://arxiv.org/abs/2206.04615
[toxigen]: https://arxiv.org/abs/2203.09509
|
legraphista/llm-compiler-7b-IMat-GGUF | legraphista | "2024-06-27T23:59:42Z" | 19,625 | 0 | gguf | [
"gguf",
"quantized",
"GGUF",
"quantization",
"imat",
"imatrix",
"static",
"16bit",
"8bit",
"6bit",
"5bit",
"4bit",
"3bit",
"2bit",
"1bit",
"text-generation",
"base_model:facebook/llm-compiler-7b",
"license:other",
"region:us"
] | text-generation | "2024-06-27T23:32:41Z" | ---
base_model: facebook/llm-compiler-7b
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inference: false
library_name: gguf
license: other
pipeline_tag: text-generation
quantized_by: legraphista
tags:
- quantized
- GGUF
- quantization
- imat
- imatrix
- static
- 16bit
- 8bit
- 6bit
- 5bit
- 4bit
- 3bit
- 2bit
- 1bit
---
# llm-compiler-7b-IMat-GGUF
_Llama.cpp imatrix quantization of facebook/llm-compiler-7b_
Original Model: [facebook/llm-compiler-7b](https://huggingface.co/facebook/llm-compiler-7b)
Original dtype: `BF16` (`bfloat16`)
Quantized by: llama.cpp [b3256](https://github.com/ggerganov/llama.cpp/releases/tag/b3256)
IMatrix dataset: [here](https://gist.githubusercontent.com/bartowski1182/eb213dccb3571f863da82e99418f81e8/raw/b2869d80f5c16fd7082594248e80144677736635/calibration_datav3.txt)
- [Files](#files)
- [IMatrix](#imatrix)
- [Common Quants](#common-quants)
- [All Quants](#all-quants)
- [Downloading using huggingface-cli](#downloading-using-huggingface-cli)
- [Inference](#inference)
- [Llama.cpp](#llama-cpp)
- [FAQ](#faq)
- [Why is the IMatrix not applied everywhere?](#why-is-the-imatrix-not-applied-everywhere)
- [How do I merge a split GGUF?](#how-do-i-merge-a-split-gguf)
---
## Files
### IMatrix
Status: ✅ Available
Link: [here](https://huggingface.co/legraphista/llm-compiler-7b-IMat-GGUF/blob/main/imatrix.dat)
### Common Quants
| Filename | Quant type | File Size | Status | Uses IMatrix | Is Split |
| -------- | ---------- | --------- | ------ | ------------ | -------- |
| [llm-compiler-7b.Q8_0.gguf](https://huggingface.co/legraphista/llm-compiler-7b-IMat-GGUF/blob/main/llm-compiler-7b.Q8_0.gguf) | Q8_0 | 7.16GB | ✅ Available | ⚪ Static | 📦 No
| [llm-compiler-7b.Q6_K.gguf](https://huggingface.co/legraphista/llm-compiler-7b-IMat-GGUF/blob/main/llm-compiler-7b.Q6_K.gguf) | Q6_K | 5.53GB | ✅ Available | ⚪ Static | 📦 No
| [llm-compiler-7b.Q4_K.gguf](https://huggingface.co/legraphista/llm-compiler-7b-IMat-GGUF/blob/main/llm-compiler-7b.Q4_K.gguf) | Q4_K | 4.08GB | ✅ Available | 🟢 IMatrix | 📦 No
| [llm-compiler-7b.Q3_K.gguf](https://huggingface.co/legraphista/llm-compiler-7b-IMat-GGUF/blob/main/llm-compiler-7b.Q3_K.gguf) | Q3_K | 3.30GB | ✅ Available | 🟢 IMatrix | 📦 No
| [llm-compiler-7b.Q2_K.gguf](https://huggingface.co/legraphista/llm-compiler-7b-IMat-GGUF/blob/main/llm-compiler-7b.Q2_K.gguf) | Q2_K | 2.53GB | ✅ Available | 🟢 IMatrix | 📦 No
### All Quants
| Filename | Quant type | File Size | Status | Uses IMatrix | Is Split |
| -------- | ---------- | --------- | ------ | ------------ | -------- |
| [llm-compiler-7b.BF16.gguf](https://huggingface.co/legraphista/llm-compiler-7b-IMat-GGUF/blob/main/llm-compiler-7b.BF16.gguf) | BF16 | 13.48GB | ✅ Available | ⚪ Static | 📦 No
| [llm-compiler-7b.FP16.gguf](https://huggingface.co/legraphista/llm-compiler-7b-IMat-GGUF/blob/main/llm-compiler-7b.FP16.gguf) | F16 | 13.48GB | ✅ Available | ⚪ Static | 📦 No
| [llm-compiler-7b.Q8_0.gguf](https://huggingface.co/legraphista/llm-compiler-7b-IMat-GGUF/blob/main/llm-compiler-7b.Q8_0.gguf) | Q8_0 | 7.16GB | ✅ Available | ⚪ Static | 📦 No
| [llm-compiler-7b.Q6_K.gguf](https://huggingface.co/legraphista/llm-compiler-7b-IMat-GGUF/blob/main/llm-compiler-7b.Q6_K.gguf) | Q6_K | 5.53GB | ✅ Available | ⚪ Static | 📦 No
| [llm-compiler-7b.Q5_K.gguf](https://huggingface.co/legraphista/llm-compiler-7b-IMat-GGUF/blob/main/llm-compiler-7b.Q5_K.gguf) | Q5_K | 4.78GB | ✅ Available | ⚪ Static | 📦 No
| [llm-compiler-7b.Q5_K_S.gguf](https://huggingface.co/legraphista/llm-compiler-7b-IMat-GGUF/blob/main/llm-compiler-7b.Q5_K_S.gguf) | Q5_K_S | 4.65GB | ✅ Available | ⚪ Static | 📦 No
| [llm-compiler-7b.Q4_K.gguf](https://huggingface.co/legraphista/llm-compiler-7b-IMat-GGUF/blob/main/llm-compiler-7b.Q4_K.gguf) | Q4_K | 4.08GB | ✅ Available | 🟢 IMatrix | 📦 No
| [llm-compiler-7b.Q4_K_S.gguf](https://huggingface.co/legraphista/llm-compiler-7b-IMat-GGUF/blob/main/llm-compiler-7b.Q4_K_S.gguf) | Q4_K_S | 3.86GB | ✅ Available | 🟢 IMatrix | 📦 No
| [llm-compiler-7b.IQ4_NL.gguf](https://huggingface.co/legraphista/llm-compiler-7b-IMat-GGUF/blob/main/llm-compiler-7b.IQ4_NL.gguf) | IQ4_NL | 3.83GB | ✅ Available | 🟢 IMatrix | 📦 No
| [llm-compiler-7b.IQ4_XS.gguf](https://huggingface.co/legraphista/llm-compiler-7b-IMat-GGUF/blob/main/llm-compiler-7b.IQ4_XS.gguf) | IQ4_XS | 3.62GB | ✅ Available | 🟢 IMatrix | 📦 No
| [llm-compiler-7b.Q3_K.gguf](https://huggingface.co/legraphista/llm-compiler-7b-IMat-GGUF/blob/main/llm-compiler-7b.Q3_K.gguf) | Q3_K | 3.30GB | ✅ Available | 🟢 IMatrix | 📦 No
| [llm-compiler-7b.Q3_K_L.gguf](https://huggingface.co/legraphista/llm-compiler-7b-IMat-GGUF/blob/main/llm-compiler-7b.Q3_K_L.gguf) | Q3_K_L | 3.60GB | ✅ Available | 🟢 IMatrix | 📦 No
| [llm-compiler-7b.Q3_K_S.gguf](https://huggingface.co/legraphista/llm-compiler-7b-IMat-GGUF/blob/main/llm-compiler-7b.Q3_K_S.gguf) | Q3_K_S | 2.95GB | ✅ Available | 🟢 IMatrix | 📦 No
| [llm-compiler-7b.IQ3_M.gguf](https://huggingface.co/legraphista/llm-compiler-7b-IMat-GGUF/blob/main/llm-compiler-7b.IQ3_M.gguf) | IQ3_M | 3.11GB | ✅ Available | 🟢 IMatrix | 📦 No
| [llm-compiler-7b.IQ3_S.gguf](https://huggingface.co/legraphista/llm-compiler-7b-IMat-GGUF/blob/main/llm-compiler-7b.IQ3_S.gguf) | IQ3_S | 2.95GB | ✅ Available | 🟢 IMatrix | 📦 No
| [llm-compiler-7b.IQ3_XS.gguf](https://huggingface.co/legraphista/llm-compiler-7b-IMat-GGUF/blob/main/llm-compiler-7b.IQ3_XS.gguf) | IQ3_XS | 2.80GB | ✅ Available | 🟢 IMatrix | 📦 No
| [llm-compiler-7b.IQ3_XXS.gguf](https://huggingface.co/legraphista/llm-compiler-7b-IMat-GGUF/blob/main/llm-compiler-7b.IQ3_XXS.gguf) | IQ3_XXS | 2.59GB | ✅ Available | 🟢 IMatrix | 📦 No
| [llm-compiler-7b.Q2_K.gguf](https://huggingface.co/legraphista/llm-compiler-7b-IMat-GGUF/blob/main/llm-compiler-7b.Q2_K.gguf) | Q2_K | 2.53GB | ✅ Available | 🟢 IMatrix | 📦 No
| [llm-compiler-7b.Q2_K_S.gguf](https://huggingface.co/legraphista/llm-compiler-7b-IMat-GGUF/blob/main/llm-compiler-7b.Q2_K_S.gguf) | Q2_K_S | 2.32GB | ✅ Available | 🟢 IMatrix | 📦 No
| [llm-compiler-7b.IQ2_M.gguf](https://huggingface.co/legraphista/llm-compiler-7b-IMat-GGUF/blob/main/llm-compiler-7b.IQ2_M.gguf) | IQ2_M | 2.36GB | ✅ Available | 🟢 IMatrix | 📦 No
| [llm-compiler-7b.IQ2_S.gguf](https://huggingface.co/legraphista/llm-compiler-7b-IMat-GGUF/blob/main/llm-compiler-7b.IQ2_S.gguf) | IQ2_S | 2.20GB | ✅ Available | 🟢 IMatrix | 📦 No
| [llm-compiler-7b.IQ2_XS.gguf](https://huggingface.co/legraphista/llm-compiler-7b-IMat-GGUF/blob/main/llm-compiler-7b.IQ2_XS.gguf) | IQ2_XS | 2.03GB | ✅ Available | 🟢 IMatrix | 📦 No
| [llm-compiler-7b.IQ2_XXS.gguf](https://huggingface.co/legraphista/llm-compiler-7b-IMat-GGUF/blob/main/llm-compiler-7b.IQ2_XXS.gguf) | IQ2_XXS | 1.85GB | ✅ Available | 🟢 IMatrix | 📦 No
| [llm-compiler-7b.IQ1_M.gguf](https://huggingface.co/legraphista/llm-compiler-7b-IMat-GGUF/blob/main/llm-compiler-7b.IQ1_M.gguf) | IQ1_M | 1.65GB | ✅ Available | 🟢 IMatrix | 📦 No
| llm-compiler-7b.IQ1_S | IQ1_S | - | ⏳ Processing | 🟢 IMatrix | -
## Downloading using huggingface-cli
If you do not have hugginface-cli installed:
```
pip install -U "huggingface_hub[cli]"
```
Download the specific file you want:
```
huggingface-cli download legraphista/llm-compiler-7b-IMat-GGUF --include "llm-compiler-7b.Q8_0.gguf" --local-dir ./
```
If the model file is big, it has been split into multiple files. In order to download them all to a local folder, run:
```
huggingface-cli download legraphista/llm-compiler-7b-IMat-GGUF --include "llm-compiler-7b.Q8_0/*" --local-dir ./
# see FAQ for merging GGUF's
```
---
## Inference
### Llama.cpp
```
llama.cpp/main -m llm-compiler-7b.Q8_0.gguf --color -i -p "prompt here"
```
---
## FAQ
### Why is the IMatrix not applied everywhere?
According to [this investigation](https://www.reddit.com/r/LocalLLaMA/comments/1993iro/ggufs_quants_can_punch_above_their_weights_now/), it appears that lower quantizations are the only ones that benefit from the imatrix input (as per hellaswag results).
### How do I merge a split GGUF?
1. Make sure you have `gguf-split` available
- To get hold of `gguf-split`, navigate to https://github.com/ggerganov/llama.cpp/releases
- Download the appropriate zip for your system from the latest release
- Unzip the archive and you should be able to find `gguf-split`
2. Locate your GGUF chunks folder (ex: `llm-compiler-7b.Q8_0`)
3. Run `gguf-split --merge llm-compiler-7b.Q8_0/llm-compiler-7b.Q8_0-00001-of-XXXXX.gguf llm-compiler-7b.Q8_0.gguf`
- Make sure to point `gguf-split` to the first chunk of the split.
---
Got a suggestion? Ping me [@legraphista](https://x.com/legraphista)! |
deepset/bert-base-cased-squad2 | deepset | "2023-05-05T07:00:52Z" | 19,611 | 19 | transformers | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"question-answering",
"en",
"dataset:squad_v2",
"license:cc-by-4.0",
"model-index",
"endpoints_compatible",
"region:us"
] | question-answering | "2022-03-02T23:29:05Z" | ---
language: en
license: cc-by-4.0
datasets:
- squad_v2
model-index:
- name: deepset/bert-base-cased-squad2
results:
- task:
type: question-answering
name: Question Answering
dataset:
name: squad_v2
type: squad_v2
config: squad_v2
split: validation
metrics:
- type: exact_match
value: 71.1517
name: Exact Match
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZGZlNmQ1YzIzMWUzNTg4YmI4NWVhYThiMzE2ZGZmNWUzNDM3NWI0ZGJkNzliNGUxNTY2MDA5MWVkYjAwYWZiMCIsInZlcnNpb24iOjF9.iUvVdy5c4hoXkwlThJankQqG9QXzNilvfF1_4P0oL8X-jkY5Q6YSsZx6G6cpgXogqFpn7JlE_lP6_OT0VIamCg
- type: f1
value: 74.6714
name: F1
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMWE5OGNjODhmY2Y0NWIyZDIzMmQ2NmRjZGYyYTYzOWMxZDUzYzg4YjBhNTRiNTY4NTc0M2IxNjI5NWI5ZDM0NCIsInZlcnNpb24iOjF9.IqU9rbzUcKmDEoLkwCUZTKSH0ZFhtqgnhOaEDKKnaRMGBJLj98D5V4VirYT6jLh8FlR0FiwvMTMjReBcfTisAQ
---
This is a BERT base cased model trained on SQuAD v2 |
ydshieh/vit-gpt2-coco-en | ydshieh | "2022-09-16T15:06:54Z" | 19,584 | 35 | transformers | [
"transformers",
"pytorch",
"tf",
"jax",
"tensorboard",
"vision-encoder-decoder",
"image-to-text",
"endpoints_compatible",
"region:us"
] | image-to-text | "2022-03-02T23:29:05Z" | ---
tags:
- image-to-text
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg
example_title: Football Match
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/dog-cat.jpg
example_title: Dog & Cat
---
## Example
The model is by no means a state-of-the-art model, but nevertheless
produces reasonable image captioning results. It was mainly fine-tuned
as a proof-of-concept for the 🤗 FlaxVisionEncoderDecoder Framework.
The model can be used as follows:
**In PyTorch**
```python
import torch
import requests
from PIL import Image
from transformers import ViTFeatureExtractor, AutoTokenizer, VisionEncoderDecoderModel
loc = "ydshieh/vit-gpt2-coco-en"
feature_extractor = ViTFeatureExtractor.from_pretrained(loc)
tokenizer = AutoTokenizer.from_pretrained(loc)
model = VisionEncoderDecoderModel.from_pretrained(loc)
model.eval()
def predict(image):
pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values
with torch.no_grad():
output_ids = model.generate(pixel_values, max_length=16, num_beams=4, return_dict_in_generate=True).sequences
preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
preds = [pred.strip() for pred in preds]
return preds
# We will verify our results on an image of cute cats
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
with Image.open(requests.get(url, stream=True).raw) as image:
preds = predict(image)
print(preds)
# should produce
# ['a cat laying on top of a couch next to another cat']
```
**In Flax**
```python
import jax
import requests
from PIL import Image
from transformers import ViTFeatureExtractor, AutoTokenizer, FlaxVisionEncoderDecoderModel
loc = "ydshieh/vit-gpt2-coco-en"
feature_extractor = ViTFeatureExtractor.from_pretrained(loc)
tokenizer = AutoTokenizer.from_pretrained(loc)
model = FlaxVisionEncoderDecoderModel.from_pretrained(loc)
gen_kwargs = {"max_length": 16, "num_beams": 4}
# This takes sometime when compiling the first time, but the subsequent inference will be much faster
@jax.jit
def generate(pixel_values):
output_ids = model.generate(pixel_values, **gen_kwargs).sequences
return output_ids
def predict(image):
pixel_values = feature_extractor(images=image, return_tensors="np").pixel_values
output_ids = generate(pixel_values)
preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
preds = [pred.strip() for pred in preds]
return preds
# We will verify our results on an image of cute cats
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
with Image.open(requests.get(url, stream=True).raw) as image:
preds = predict(image)
print(preds)
# should produce
# ['a cat laying on top of a couch next to another cat']
``` |
yodayo-ai/kivotos-xl-2.0 | yodayo-ai | "2024-06-07T08:06:06Z" | 19,550 | 81 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"en",
"base_model:cagliostrolab/animagine-xl-3.1",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | "2024-06-01T23:41:35Z" | ---
license: other
license_name: faipl-1.0-sd
license_link: https://freedevproject.org/faipl-1.0-sd/
language:
- en
tags:
- text-to-image
- stable-diffusion
- safetensors
- stable-diffusion-xl
base_model: cagliostrolab/animagine-xl-3.1
widget:
- text: 1girl, green hair, sweater, looking at viewer, upper body, beanie, outdoors, night, turtleneck, masterpiece, best quality, very aesthetic, absurdres
parameter:
negative_prompt: nsfw, low quality, worst quality, very displeasing, 3d, watermark, signature, ugly, poorly drawn
example_title: 1girl
- text: 1boy, male focus, green hair, sweater, looking at viewer, upper body, beanie, outdoors, night, turtleneck, masterpiece, best quality, very aesthetic, absurdres
parameter:
negative_prompt: nsfw, low quality, worst quality, very displeasing, 3d, watermark, signature, ugly, poorly drawn
example_title: 1boy
---
<style>
body {
display: flex;
align-items: center;
justify-content: center;
height: 100vh;
margin: 0;
font-family: Arial, sans-serif;
background-color: #f4f4f9;
overflow: auto;
}
.container {
display: flex;
flex-direction: column;
align-items: center;
justify-content: center;
width: 100%;
padding: 20px;
}
.title-container {
display: flex;
flex-direction: column;
justify-content: center;
align-items: center;
padding: 1em;
border-radius: 10px;
}
.title {
font-size: 3em;
font-family: 'Montserrat', sans-serif;
text-align: center;
font-weight: bold;
}
.title span {
background: -webkit-linear-gradient(45deg, #0077b6, #00b4d8, #90e0ef);
-webkit-background-clip: text;
-webkit-text-fill-color: transparent;
}
.gallery {
display: grid;
grid-template-columns: repeat(5, 1fr);
gap: 10px;
}
.gallery img {
width: 100%;
height: auto;
margin-top: 0px;
margin-bottom: 0px;
border-radius: 10px;
box-shadow: 0 4px 8px rgba(0, 0, 0, 0.2);
transition: transform 0.3s;
}
.gallery img:hover {
transform: scale(1.05);
}
.note {
font-size: 1em;
opacity: 50%;
text-align: center;
margin-top: 20px;
color: #555;
}
</style>
<div class="container">
<div class="title-container">
<div class="title"><span>Kivotos XL 2.0</span></div>
</div>
<div class="gallery">
<img src="https://huggingface.co/yodayo-ai/kivotos-xl-2.0/resolve/main/samples/sample-001.png" alt="Image 1">
<img src="https://huggingface.co/yodayo-ai/kivotos-xl-2.0/resolve/main/samples/sample-002.png" alt="Image 2">
<img src="https://huggingface.co/yodayo-ai/kivotos-xl-2.0/resolve/main/samples/sample-003.png" alt="Image 3">
<img src="https://huggingface.co/yodayo-ai/kivotos-xl-2.0/resolve/main/samples/sample-004.png" alt="Image 4">
<img src="https://huggingface.co/yodayo-ai/kivotos-xl-2.0/resolve/main/samples/sample-005.png" alt="Image 5">
<img src="https://huggingface.co/yodayo-ai/kivotos-xl-2.0/resolve/main/samples/sample-006.png" alt="Image 6">
<img src="https://huggingface.co/yodayo-ai/kivotos-xl-2.0/resolve/main/samples/sample-007.png" alt="Image 7">
<img src="https://huggingface.co/yodayo-ai/kivotos-xl-2.0/resolve/main/samples/sample-008.png" alt="Image 8">
<img src="https://huggingface.co/yodayo-ai/kivotos-xl-2.0/resolve/main/samples/sample-009.png" alt="Image 9">
<img src="https://huggingface.co/yodayo-ai/kivotos-xl-2.0/resolve/main/samples/sample-010.png" alt="Image 10">
</div>
<div class="note">
Drag and drop each image to <a href="https://huggingface.co/spaces/Linaqruf/pnginfo" target="_blank">this link</a> or use ComfyUI to get the metadata.
</div>
</div>
## Overview
**Kivotos XL 2.0** is the latest version of the [Yodayo Kivotos XL](https://yodayo.com/models/ee3c3839-e723-45f5-9151-18b592bc93b9) series, following the previous iteration, [Kivotos XL 1.0](https://yodayo.com/models/ee3c3839-e723-45f5-9151-18b592bc93b9/?modelversion=bf0091c7-4337-4edb-8c34-160d647d249a). This open-source model is built upon Animagine XL V3, a specialized SDXL model designed for generating high-quality anime-style artwork. Kivotos XL V2.0 has undergone additional fine-tuning and optimization to focus specifically on generating images that accurately represent the visual style and aesthetics of the Blue Archive franchise.
## Model Details
- **Developed by**: [Linaqruf](https://github.com/Linaqruf)
- **Model type**: Diffusion-based text-to-image generative model
- **Model Description**: Kivotos XL V2.0, the latest in the Yodayo Kivotos XL series, is an open-source model built on Animagine XL V3. Fine-tuned for high-quality Blue Archive anime-style art generation.
- **License**: [Fair AI Public License 1.0-SD](https://freedevproject.org/faipl-1.0-sd/)
- **Finetuned from model**: [Animagine XL 3.1](https://huggingface.co/cagliostrolab/animagine-xl-3.1)
## Supported Platform
1. Use this model in our platform: [](https://yodayo.com/models/ee3c3839-e723-45f5-9151-18b592bc93b9/?modelversion=f3989e22-5afc-40a1-b435-38eae7760f37)
2. Use it in [`ComfyUI`](https://github.com/comfyanonymous/ComfyUI) or [`Stable Diffusion Webui`](https://github.com/AUTOMATIC1111/stable-diffusion-webui)
3. Use it with 🧨 `diffusers`
## 🧨 Diffusers Installation
First install the required libraries:
```bash
pip install diffusers transformers accelerate safetensors --upgrade
```
Then run image generation with the following example code:
```python
import torch
from diffusers import StableDiffusionXLPipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
"yodayo-ai/kivotos-xl-2.0",
torch_dtype=torch.float16,
use_safetensors=True,
custom_pipeline="lpw_stable_diffusion_xl",
add_watermarker=False,
variant="fp16"
)
pipe.to('cuda')
prompt = "1girl, kazusa \(blue archive\), blue archive, solo, upper body, v, smile, looking at viewer, outdoors, night, masterpiece, best quality, very aesthetic, absurdres"
negative_prompt = "nsfw, (low quality, worst quality:1.2), very displeasing, 3d, watermark, signature, ugly, poorly drawn"
image = pipe(
prompt,
negative_prompt=negative_prompt,
width=832,
height=1216,
guidance_scale=7,
num_inference_steps=28
).images[0]
image.save("./cat.png")
```
## Usage Guidelines
### Tag Ordering
For optimal results, it's recommended to follow the structured prompt template because we train the model like this:
```
1girl/1boy, character name, from which series, by which artists, everything else in any order.
```
### Special Tags
Kivotos XL 2.0 inherits special tags from Animagine XL 3.1 to enhance image generation by steering results toward quality, rating, creation date, and aesthetic. This inheritance ensures that Kivotos XL 2.0 can produce high-quality, relevant, and aesthetically pleasing images. While the model can generate images without these tags, using them helps achieve better results.
- **Quality tags**: masterpiece, best quality, great quality, good quality, normal quality, low quality, worst quality
- **Rating tags**: safe, sensitive, nsfw, explicit
- **Year tags**: newest, recent, mid, early, oldest
- **Aesthetic tags**: very aesthetic, aesthetic, displeasing, very displeasing
### Recommended Settings
To guide the model towards generating high-aesthetic images, use the following recommended settings:
- **Negative prompts**:
```
nsfw, (low quality, worst quality:1.2), very displeasing, 3d, watermark, signature, ugly, poorly drawn
```
- **Positive prompts**:
```
masterpiece, best quality, very aesthetic, absurdres
```
- **Classifier-Free Guidance (CFG) Scale**: should be around 5 to 7; 10 is fried, >12 is deep-fried.
- **Sampling steps**: should be around 25 to 30; 28 is the sweet spot.
- **Sampler**: Euler Ancestral (Euler a) is highly recommended.
- **Supported resolutions**:
```
1024 x 1024, 1152 x 896, 896 x 1152, 1216 x 832, 832 x 1216, 1344 x 768, 768 x 1344, 1536 x 640, 640 x 1536
```
## Training
These are the key hyperparameters used during training:
| Feature | Pretraining | Finetuning |
|-------------------------------|----------------------------|---------------------------------|
| **Hardware** | 2x H100 80GB PCIe | 1x A100 80GB PCIe |
| **Batch Size** | 32 | 48 |
| **Gradient Accumulation Steps** | 2 | 1 |
| **Noise Offset** | None | 0.0357 |
| **Epochs** | 10 | 10 |
| **UNet Learning Rate** | 5e-6 | 3.75e-6 |
| **Text Encoder Learning Rate** | 2.5e-6 | None |
| **Optimizer** | Adafactor | Adafactor |
| **Optimizer Args** | Scale Parameter: False, Relative Step: False, Warmup Init: False (0.9, 0.99) | Scale Parameter: False, Relative Step: False, Warmup Init: False |
| **Scheduler** | Constant with Warmups | Constant with Warmups |
| **Warmup Steps** | 0.05% | 0.05% |
## License
Kivotos XL 2.0 falls under [Fair AI Public License 1.0-SD](https://freedevproject.org/faipl-1.0-sd/) license, which is compatible with Stable Diffusion models’ license. Key points:
1. **Modification Sharing:** If you modify Kivotos XL 2.0, you must share both your changes and the original license.
2. **Source Code Accessibility:** If your modified version is network-accessible, provide a way (like a download link) for others to get the source code. This applies to derived models too.
3. **Distribution Terms:** Any distribution must be under this license or another with similar rules.
|
QuantFactory/starchat2-15b-v0.1-GGUF | QuantFactory | "2024-06-24T08:37:51Z" | 19,516 | 1 | null | [
"gguf",
"region:us"
] | null | "2024-06-24T06:44:48Z" | Entry not found |
Lewdiculous/L3-8B-Stheno-v3.3-32K-GGUF-IQ-Imatrix | Lewdiculous | "2024-06-24T17:56:45Z" | 19,515 | 20 | null | [
"gguf",
"roleplay",
"llama3",
"sillytavern",
"en",
"license:cc-by-nc-4.0",
"region:us"
] | null | "2024-06-23T20:13:10Z" | ---
license: cc-by-nc-4.0
language:
- en
inference: false
tags:
- roleplay
- llama3
- sillytavern
---
# #roleplay #sillytavern #llama3
My GGUF-IQ-Imatrix quants for [**Sao10K/L3-8B-Stheno-v3.3-32K**](https://huggingface.co/Sao10K/L3-8B-Stheno-v3.3-32K).
**Sao10K** with Stheno **yet** again, now bigger and better than ever! <br>
I recommend checking his page for feedback and support.
> [!IMPORTANT]
> **Quantization process:** <br>
> Imatrix data was generated from the FP16-GGUF and conversions directly from the BF16-GGUF. <br>
> This is a bit more disk and compute intensive but hopefully avoids any losses during conversion. <br>
> To run this model, please use the [**latest version of KoboldCpp**](https://github.com/LostRuins/koboldcpp/releases/latest). <br>
> If you noticed any issues let me know in the discussions.
> [!NOTE]
> **General usage:** <br>
> For **8GB VRAM** GPUs, I recommend the **Q4_K_M-imat** (4.89 BPW) quant for up to 12288 context sizes. <br>
>
> **Presets:** <br>
> Some compatible SillyTavern presets can be found [**here (Virt's Roleplay Presets)**](https://huggingface.co/Virt-io/SillyTavern-Presets). <br>
> Check [**discussions such as this one**](https://huggingface.co/Virt-io/SillyTavern-Presets/discussions/5#664d6fb87c563d4d95151baa) for other recommendations and samplers.
<details>
<summary>⇲ Click here to expand/hide information – General chart with relative quant parformances.</summary>
> [!NOTE]
> **Recommended read:** <br>
>
> [**"Which GGUF is right for me? (Opinionated)" by Artefact2**](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)
>
> *Click the image to view full size.*
> 
</details>
> [!TIP]
> **Personal-support:** <br>
> I apologize for disrupting your experience. <br>
> Eventually I may be able to use a dedicated server for this, but for now hopefully these quants are helpful. <br>
> If you **want** and you are **able to**... <br>
> You can [**spare some change over here (Ko-fi)**](https://ko-fi.com/Lewdiculous). <br>
>
> **Author-support:** <br>
> You can support the author [**at their own page**](https://ko-fi.com/sao10k).
<details>
<summary>Original model card information.</summary>
## **Original card:**
Trained with compute from [Backyard.ai](https://backyard.ai/) | Thanks to them and @dynafire for helping me out.
---
Training Details:
<br>Trained at 8K Context -> Expanded to 32K Context with PoSE training.
Dataset Modifications:
<br>\- Further Cleaned up Roleplaying Samples -> Quality Check
<br>\- Removed Low Quality Samples from Manual Check -> Increased Baseline Quality Floor
<br>\- More Creative Writing Samples -> 2x Samples
<br>\- Remade and Refined Detailed Instruct Data
Notes:
<br>\- Training run is much less aggressive than previous Stheno versions.
<br>\- This model works when tested in bf16 with the same configs as within the file.
<br>\- I do not know the effects quantisation has on it.
<br>\- Roleplays pretty well. Feels nice in my opinion.
<br>\- It has some issues on long context understanding and reasoning. Much better vs rope scaling normally though, so that is a plus.
<br>\- Reminder, this isn't a native 32K model. It has it's issues, but it's coherent and working well.
Sanity Check // Needle in a Haystack Results:
<br>\- This is not as complex as RULER or NIAN, but it's a basic evaluator. Some improper train examples had Haystack scores ranging from Red to Orange for most of the extended contexts.
Wandb Run:
---
Relevant Axolotl Configurations:
<br>-> Taken from [winglian/Llama-3-8b-64k-PoSE](https://huggingface.co/winglian/Llama-3-8b-64k-PoSE)
<br>\- I tried to find my own configs, hours of tinkering but the one he used worked best, so I stuck to it.
<br>\- 2M Rope Theta had the best loss results during training compared to other values.
<br>\- Leaving it at 500K rope wasn't that much worse, but 4M and 8M Theta made the grad_norm values worsen even if loss drops fast.
<br>\- Mixing in Pretraining Data was a PITA. Made it a lot worse with formatting.
<br>\- Pretraining / Noise made it worse at Haystack too? It wasn't all Green, Mainly Oranges.
<br>\- Improper / Bad Rope Theta shows in Grad_Norm exploding to thousands. It'll drop to low values alright, but it's a scary fast drop even with gradient clipping.
```
sequence_len: 8192
use_pose: true
pose_max_context_len: 32768
overrides_of_model_config:
rope_theta: 2000000.0
max_position_embeddings: 32768
# peft_use_dora: true
adapter: lora
peft_use_rslora: true
lora_model_dir:
lora_r: 256
lora_alpha: 256
lora_dropout: 0.1
lora_target_linear: true
lora_target_modules:
- gate_proj
- down_proj
- up_proj
- q_proj
- v_proj
- k_proj
- o_proj
warmup_steps: 80
gradient_accumulation_steps: 6
micro_batch_size: 1
num_epochs: 2
optimizer: adamw_bnb_8bit
lr_scheduler: cosine_with_min_lr
learning_rate: 0.00004
lr_scheduler_kwargs:
min_lr: 0.000004
```
</details> |
mradermacher/SharkOgno2-7b-Passthrough-GGUF | mradermacher | "2024-06-23T07:32:11Z" | 19,511 | 0 | transformers | [
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"powermove72/Shark-1",
"eren23/OGNO-7b-dpo-truthful",
"en",
"base_model:powermove72/SharkOgno2-7b-Passthrough",
"endpoints_compatible",
"region:us"
] | null | "2024-06-23T06:47:55Z" | ---
base_model: powermove72/SharkOgno2-7b-Passthrough
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- merge
- mergekit
- lazymergekit
- powermove72/Shark-1
- eren23/OGNO-7b-dpo-truthful
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/powermove72/SharkOgno2-7b-Passthrough
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/SharkOgno2-7b-Passthrough-GGUF/resolve/main/SharkOgno2-7b-Passthrough.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/SharkOgno2-7b-Passthrough-GGUF/resolve/main/SharkOgno2-7b-Passthrough.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/SharkOgno2-7b-Passthrough-GGUF/resolve/main/SharkOgno2-7b-Passthrough.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/SharkOgno2-7b-Passthrough-GGUF/resolve/main/SharkOgno2-7b-Passthrough.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/SharkOgno2-7b-Passthrough-GGUF/resolve/main/SharkOgno2-7b-Passthrough.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/SharkOgno2-7b-Passthrough-GGUF/resolve/main/SharkOgno2-7b-Passthrough.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/SharkOgno2-7b-Passthrough-GGUF/resolve/main/SharkOgno2-7b-Passthrough.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/SharkOgno2-7b-Passthrough-GGUF/resolve/main/SharkOgno2-7b-Passthrough.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/SharkOgno2-7b-Passthrough-GGUF/resolve/main/SharkOgno2-7b-Passthrough.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/SharkOgno2-7b-Passthrough-GGUF/resolve/main/SharkOgno2-7b-Passthrough.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/SharkOgno2-7b-Passthrough-GGUF/resolve/main/SharkOgno2-7b-Passthrough.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/SharkOgno2-7b-Passthrough-GGUF/resolve/main/SharkOgno2-7b-Passthrough.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/SharkOgno2-7b-Passthrough-GGUF/resolve/main/SharkOgno2-7b-Passthrough.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/SharkOgno2-7b-Passthrough-GGUF/resolve/main/SharkOgno2-7b-Passthrough.Q8_0.gguf) | Q8_0 | 7.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/SharkOgno2-7b-Passthrough-GGUF/resolve/main/SharkOgno2-7b-Passthrough.f16.gguf) | f16 | 14.6 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
timm/tf_efficientnetv2_l.in21k_ft_in1k | timm | "2023-04-27T22:17:39Z" | 19,505 | 2 | timm | [
"timm",
"pytorch",
"safetensors",
"image-classification",
"dataset:imagenet-1k",
"dataset:imagenet-21k",
"arxiv:2104.00298",
"license:apache-2.0",
"region:us"
] | image-classification | "2022-12-13T00:16:48Z" | ---
tags:
- image-classification
- timm
library_name: timm
license: apache-2.0
datasets:
- imagenet-1k
- imagenet-21k
---
# Model card for tf_efficientnetv2_l.in21k_ft_in1k
A EfficientNet-v2 image classification model. Trained on ImageNet-21k and fine-tuned on ImageNet-1k in Tensorflow by paper authors, ported to PyTorch by Ross Wightman.
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 118.5
- GMACs: 36.1
- Activations (M): 101.2
- Image size: train = 384 x 384, test = 480 x 480
- **Papers:**
- EfficientNetV2: Smaller Models and Faster Training: https://arxiv.org/abs/2104.00298
- **Dataset:** ImageNet-1k
- **Pretrain Dataset:** ImageNet-21k
- **Original:** https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('tf_efficientnetv2_l.in21k_ft_in1k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Feature Map Extraction
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'tf_efficientnetv2_l.in21k_ft_in1k',
pretrained=True,
features_only=True,
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
for o in output:
# print shape of each feature map in output
# e.g.:
# torch.Size([1, 32, 192, 192])
# torch.Size([1, 64, 96, 96])
# torch.Size([1, 96, 48, 48])
# torch.Size([1, 224, 24, 24])
# torch.Size([1, 640, 12, 12])
print(o.shape)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'tf_efficientnetv2_l.in21k_ft_in1k',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 1280, 12, 12) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
## Citation
```bibtex
@inproceedings{tan2021efficientnetv2,
title={Efficientnetv2: Smaller models and faster training},
author={Tan, Mingxing and Le, Quoc},
booktitle={International conference on machine learning},
pages={10096--10106},
year={2021},
organization={PMLR}
}
```
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
|
Clinical-AI-Apollo/Medical-NER | Clinical-AI-Apollo | "2024-04-08T06:15:22Z" | 19,487 | 94 | transformers | [
"transformers",
"safetensors",
"deberta-v2",
"token-classification",
"generated_from_trainer",
"medical",
"base_model:microsoft/deberta-v3-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | "2024-02-09T12:57:34Z" | ---
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
- medical
model-index:
- name: deberta-med-ner-2
results: []
widget:
- text: 63 year old woman with history of CAD presented to ER
example_title: Example-1
- text: 63 year old woman diagnosed with CAD
example_title: Example-2
- text: >-
A 48 year-old female presented with vaginal bleeding and abnormal Pap
smears. Upon diagnosis of invasive non-keratinizing SCC of the cervix, she
underwent a radical hysterectomy with salpingo-oophorectomy which
demonstrated positive spread to the pelvic lymph nodes and the parametrium.
Pathological examination revealed that the tumour also extensively involved
the lower uterine segment.
example_title: example 3
pipeline_tag: token-classification
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deberta-med-ner-2
This model is a fine-tuned version of [DeBERTa](https://huggingface.co/microsoft/deberta-v3-base) on the PubMED Dataset.
## Model description
Medical NER Model finetuned on BERT to recognize 41 Medical entities.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
- mixed_precision_training: Native AMP
## Usage
The easiest way is to load the inference api from huggingface and second method is through the pipeline object offered by transformers library.
```python
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("token-classification", model="Clinical-AI-Apollo/Medical-NER", aggregation_strategy='simple')
result = pipe('45 year old woman diagnosed with CAD')
# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("Clinical-AI-Apollo/Medical-NER")
model = AutoModelForTokenClassification.from_pretrained("Clinical-AI-Apollo/Medical-NER")
```
### Author
Author: [Saketh Mattupalli](https://huggingface.co/blaze999)
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.1 |
Qwen/CodeQwen1.5-7B-Chat | Qwen | "2024-04-30T07:18:34Z" | 19,484 | 230 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"chat",
"conversational",
"en",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-04-15T07:17:06Z" | ---
license: other
license_name: tongyi-qianwen
license_link: >-
https://huggingface.co/Qwen/CodeQwen1.5-7B-Chat/blob/main/LICENSE
language:
- en
pipeline_tag: text-generation
tags:
- chat
---
# CodeQwen1.5-7B-Chat
## Introduction
CodeQwen1.5 is the Code-Specific version of Qwen1.5. It is a transformer-based decoder-only language model pretrained on a large amount of data of codes.
* Strong code generation capabilities and competitve performance across a series of benchmarks;
* Supporting long context understanding and generation with the context length of 64K tokens;
* Supporting 92 coding languages
* Excellent performance in text-to-SQL, bug fix, etc.
For more details, please refer to our [blog post](https://qwenlm.github.io/blog/codeqwen1.5/) and [GitHub repo](https://github.com/QwenLM/Qwen1.5).
## Model Details
CodeQwen1.5 is based on Qwen1.5, a language model series including decoder language models of different model sizes. It is trained on 3 trillion tokens of data of codes, and it includes group query attention (GQA) for efficient inference.
## Requirements
The code of Qwen1.5 has been in the latest Hugging face transformers and we advise you to install `transformers>=4.37.0`, or you might encounter the following error:
```
KeyError: 'qwen2'.
```
## Quickstart
Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Qwen/CodeQwen1.5-7B-Chat",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Qwen/CodeQwen1.5-7B-Chat")
prompt = "Write a quicksort algorithm in python."
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
```
## Tips
* If you encounter code switching or other bad cases, we advise you to use our provided hyper-parameters in `generation_config.json`.
## Citation
If you find our work helpful, feel free to give us a cite.
```
@article{qwen,
title={Qwen Technical Report},
author={Jinze Bai and Shuai Bai and Yunfei Chu and Zeyu Cui and Kai Dang and Xiaodong Deng and Yang Fan and Wenbin Ge and Yu Han and Fei Huang and Binyuan Hui and Luo Ji and Mei Li and Junyang Lin and Runji Lin and Dayiheng Liu and Gao Liu and Chengqiang Lu and Keming Lu and Jianxin Ma and Rui Men and Xingzhang Ren and Xuancheng Ren and Chuanqi Tan and Sinan Tan and Jianhong Tu and Peng Wang and Shijie Wang and Wei Wang and Shengguang Wu and Benfeng Xu and Jin Xu and An Yang and Hao Yang and Jian Yang and Shusheng Yang and Yang Yao and Bowen Yu and Hongyi Yuan and Zheng Yuan and Jianwei Zhang and Xingxuan Zhang and Yichang Zhang and Zhenru Zhang and Chang Zhou and Jingren Zhou and Xiaohuan Zhou and Tianhang Zhu},
journal={arXiv preprint arXiv:2309.16609},
year={2023}
}
```
|
sh2orc/Llama-3-Kor-BCCard-GGUF | sh2orc | "2024-06-28T15:13:58Z" | 19,463 | 0 | null | [
"gguf",
"license:llama3",
"region:us"
] | null | "2024-06-28T11:56:33Z" | ---
license: llama3
---
|
naver-clova-ix/donut-base-finetuned-docvqa | naver-clova-ix | "2024-03-09T13:01:37Z" | 19,455 | 179 | transformers | [
"transformers",
"pytorch",
"vision-encoder-decoder",
"donut",
"image-to-text",
"vision",
"document-question-answering",
"arxiv:2111.15664",
"license:mit",
"endpoints_compatible",
"region:us"
] | document-question-answering | "2022-07-19T13:58:22Z" | ---
license: mit
pipeline_tag: document-question-answering
tags:
- donut
- image-to-text
- vision
widget:
- text: "What is the invoice number?"
src: "https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png"
- text: "What is the purchase amount?"
src: "https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/contract.jpeg"
---
# Donut (base-sized model, fine-tuned on DocVQA)
Donut model fine-tuned on DocVQA. It was introduced in the paper [OCR-free Document Understanding Transformer](https://arxiv.org/abs/2111.15664) by Geewok et al. and first released in [this repository](https://github.com/clovaai/donut).
Disclaimer: The team releasing Donut did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
Donut consists of a vision encoder (Swin Transformer) and a text decoder (BART). Given an image, the encoder first encodes the image into a tensor of embeddings (of shape batch_size, seq_len, hidden_size), after which the decoder autoregressively generates text, conditioned on the encoding of the encoder.
## Intended uses & limitations
This model is fine-tuned on DocVQA, a document visual question answering dataset.
We refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/donut) which includes code examples.
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-2111-15664,
author = {Geewook Kim and
Teakgyu Hong and
Moonbin Yim and
Jinyoung Park and
Jinyeong Yim and
Wonseok Hwang and
Sangdoo Yun and
Dongyoon Han and
Seunghyun Park},
title = {Donut: Document Understanding Transformer without {OCR}},
journal = {CoRR},
volume = {abs/2111.15664},
year = {2021},
url = {https://arxiv.org/abs/2111.15664},
eprinttype = {arXiv},
eprint = {2111.15664},
timestamp = {Thu, 02 Dec 2021 10:50:44 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2111-15664.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
``` |
Yntec/AtoZ | Yntec | "2024-06-04T10:20:50Z" | 19,455 | 0 | diffusers | [
"diffusers",
"safetensors",
"Zovya",
"iamxenos",
"RIXYN",
"Barons",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"en",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | "2024-06-04T09:29:30Z" | ---
license: creativeml-openrail-m
library_name: diffusers
pipeline_tag: text-to-image
language:
- en
tags:
- Zovya
- iamxenos
- RIXYN
- Barons
- stable-diffusion
- stable-diffusion-diffusers
- diffusers
- text-to-image
---
# A to Z
Samples and prompts:
(Click for larger)
Top left: dungeons and dragons epic movie poster barbarian woman with cape charging into battle violent roar riding a vicious ice. wolftiger beast leather and fur boots warriors and red banners. windy dust debris storm. volumetric lighting fog depth mist pass z pass great stone castle very bright morning sunlight from side, 8k. cinematic lighting. sharp focus. intricate. intense action, dutch, foreshortening
Top right: pretty cute little girl holding squid octopus, armor, standing back, looking at viewer, smile, retro, dnd style, novel illustration, power fantasy, castle backgrounds by Boris Vallejo, teal red golden noise, film grain, faux traditional media
Bottom left: AS-Younger DollieNobody medieval child princess teal dress, elaborate, ornate, adventurer cape, blonde hair, top pony tail, standing in a tall field of flowers, tree canopy overhead. alpine mountains, clear sky, masterpiece. best quality. detailed. 8k. HDR. cinematic lighting. sharp focus. intricate.
Bottom right: A vibrant carnival scene at night with dazzling lights
A-ZovyaV3RPGArtistTools merged with the Hellmix model by Barons, Kitsch-In-Sync v2 by iamxenos, the cryptids lora by RIXYN, and artistic models with the CokeGirls lora by iamxenos!
Original pages:
https://civitai.com/models/8124?modelVersionId=87886 (aZovyaRP3V3)
https://civitai.com/models/186251/coca-cola-gil-elvgrenhaddon-sundblom-pinup-style
https://civitai.com/models/142552?modelVersionId=163068 (Kitsch-In-Sync v2)
https://civitai.com/models/21493/hellmix?modelVersionId=25632
https://civitai.com/models/64766/cryptids?modelVersionId=69407 (Cryptids LoRA) |
jonatasgrosman/wav2vec2-large-xlsr-53-french | jonatasgrosman | "2022-12-14T01:59:23Z" | 19,453 | 8 | transformers | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"fr",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_6_0",
"robust-speech-event",
"speech",
"xlsr-fine-tuning-week",
"dataset:common_voice",
"dataset:mozilla-foundation/common_voice_6_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | "2022-03-02T23:29:05Z" | ---
language: fr
license: apache-2.0
datasets:
- common_voice
- mozilla-foundation/common_voice_6_0
metrics:
- wer
- cer
tags:
- audio
- automatic-speech-recognition
- fr
- hf-asr-leaderboard
- mozilla-foundation/common_voice_6_0
- robust-speech-event
- speech
- xlsr-fine-tuning-week
model-index:
- name: XLSR Wav2Vec2 French by Jonatas Grosman
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice fr
type: common_voice
args: fr
metrics:
- name: Test WER
type: wer
value: 17.65
- name: Test CER
type: cer
value: 4.89
- name: Test WER (+LM)
type: wer
value: 13.59
- name: Test CER (+LM)
type: cer
value: 3.91
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: fr
metrics:
- name: Dev WER
type: wer
value: 34.35
- name: Dev CER
type: cer
value: 14.09
- name: Dev WER (+LM)
type: wer
value: 24.72
- name: Dev CER (+LM)
type: cer
value: 12.33
---
# Fine-tuned XLSR-53 large model for speech recognition in French
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on French using the train and validation splits of [Common Voice 6.1](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned thanks to the GPU credits generously given by the [OVHcloud](https://www.ovhcloud.com/en/public-cloud/ai-training/) :)
The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint
## Usage
The model can be used directly (without a language model) as follows...
Using the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) library:
```python
from huggingsound import SpeechRecognitionModel
model = SpeechRecognitionModel("jonatasgrosman/wav2vec2-large-xlsr-53-french")
audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"]
transcriptions = model.transcribe(audio_paths)
```
Writing your own inference script:
```python
import torch
import librosa
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
LANG_ID = "fr"
MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-french"
SAMPLES = 10
test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]")
processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
batch["speech"] = speech_array
batch["sentence"] = batch["sentence"].upper()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
predicted_sentences = processor.batch_decode(predicted_ids)
for i, predicted_sentence in enumerate(predicted_sentences):
print("-" * 100)
print("Reference:", test_dataset[i]["sentence"])
print("Prediction:", predicted_sentence)
```
| Reference | Prediction |
| ------------- | ------------- |
| "CE DERNIER A ÉVOLUÉ TOUT AU LONG DE L'HISTOIRE ROMAINE." | CE DERNIER ÉVOLUÉ TOUT AU LONG DE L'HISTOIRE ROMAINE |
| CE SITE CONTIENT QUATRE TOMBEAUX DE LA DYNASTIE ACHÉMÉNIDE ET SEPT DES SASSANIDES. | CE SITE CONTIENT QUATRE TOMBEAUX DE LA DYNASTIE ASHEMÉNID ET SEPT DES SASANDNIDES |
| "J'AI DIT QUE LES ACTEURS DE BOIS AVAIENT, SELON MOI, BEAUCOUP D'AVANTAGES SUR LES AUTRES." | JAI DIT QUE LES ACTEURS DE BOIS AVAIENT SELON MOI BEAUCOUP DAVANTAGES SUR LES AUTRES |
| LES PAYS-BAS ONT REMPORTÉ TOUTES LES ÉDITIONS. | LE PAYS-BAS ON REMPORTÉ TOUTES LES ÉDITIONS |
| IL Y A MAINTENANT UNE GARE ROUTIÈRE. | IL AMNARDIGAD LE TIRAN |
| HUIT | HUIT |
| DANS L’ATTENTE DU LENDEMAIN, ILS NE POUVAIENT SE DÉFENDRE D’UNE VIVE ÉMOTION | DANS L'ATTENTE DU LENDEMAIN IL NE POUVAIT SE DÉFENDRE DUNE VIVE ÉMOTION |
| LA PREMIÈRE SAISON EST COMPOSÉE DE DOUZE ÉPISODES. | LA PREMIÈRE SAISON EST COMPOSÉE DE DOUZE ÉPISODES |
| ELLE SE TROUVE ÉGALEMENT DANS LES ÎLES BRITANNIQUES. | ELLE SE TROUVE ÉGALEMENT DANS LES ÎLES BRITANNIQUES |
| ZÉRO | ZEGO |
## Evaluation
1. To evaluate on `mozilla-foundation/common_voice_6_0` with split `test`
```bash
python eval.py --model_id jonatasgrosman/wav2vec2-large-xlsr-53-french --dataset mozilla-foundation/common_voice_6_0 --config fr --split test
```
2. To evaluate on `speech-recognition-community-v2/dev_data`
```bash
python eval.py --model_id jonatasgrosman/wav2vec2-large-xlsr-53-french --dataset speech-recognition-community-v2/dev_data --config fr --split validation --chunk_length_s 5.0 --stride_length_s 1.0
```
## Citation
If you want to cite this model you can use this:
```bibtex
@misc{grosman2021xlsr53-large-french,
title={Fine-tuned {XLSR}-53 large model for speech recognition in {F}rench},
author={Grosman, Jonatas},
howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-french}},
year={2021}
}
``` |
speakleash/Bielik-7B-v0.1 | speakleash | "2024-04-04T19:21:27Z" | 19,450 | 54 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"continuously_pretrained",
"pl",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-03-30T09:56:05Z" | ---
license: apache-2.0
language:
- pl
library_name: transformers
tags:
- continuously_pretrained
inference:
parameters:
temperature: 0.7
---
<p align="center">
<img src="https://huggingface.co/speakleash/Bielik-7B-v0.1/raw/main/speakleash_cyfronet.png">
</p>
# Bielik-7B-v0.1
The Bielik-7B-v0.1 is a generative text model featuring 7 billion parameters, meticulously evolved from its predecessor, the [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1), through processing of over 70 billion tokens. Forementioned model stands as a testament to the unique collaboration between the open-science/open-souce project SpeakLeash and the High Performance Computing (HPC) center: ACK Cyfronet AGH. Developed and trained on Polish text corpora, which has been cherry-picked and processed by the SpeakLeash team, this endeavor leverages Polish large-scale computing infrastructure, specifically within the PLGrid environment, and more precisely, the HPC centers: ACK Cyfronet AGH. The creation and training of the Bielik-7B-v0.1 was propelled by the support of computational grant number PLG/2024/016951, conducted on the Helios supercomputer, enabling the use of cutting-edge technology and computational resources essential for large-scale machine learning processes. As a result, the model exhibits an exceptional ability to understand and process the Polish language, providing accurate responses and performing a variety of linguistic tasks with high precision.
## Model
Bielik-7B-v0.1 has been trained with the use of an original open source framework called [ALLaMo](https://github.com/chrisociepa/allamo) implemented by [Krzysztof Ociepa](https://www.linkedin.com/in/krzysztof-ociepa-44886550/). This framework allows users to train language models with architecture similar to LLaMA and Mistral in fast and efficient way.
The model training was conducted on the Helios Supercomputer at the ACK Cyfronet AGH, utilizing 256 NVidia GH200 cards while achieving a throughput exceeding 9200 tokens/gpu/second.
The training dataset was composed of Polish texts collected and made available through the [SpeakLeash](https://speakleash.org/) project. We used over 36 billion tokens for two epochs of training.
### Model description:
* **Developed by:** [SpeakLeash](https://speakleash.org/)
* **Language:** Polish
* **Model type:** causal decoder-only
* **Adopted from:** [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
* **License:** Apache 2.0 (commercial use allowed)
* **Model ref:** speakleash:debfc8635c781358e8db833a333887a5
### Quality evaluation
A XGBoost classification model was prepared and created to evaluate the quality of texts in native Polish language. It is based on 93 features, such as the ratio of out-of-vocabulary words to all words (OOVs), the number of nouns, verbs, average sentence length etc. The model outputs the category of a given document (either HIGH, MEDIUM or LOW) along with the probability. This approach allows implementation of dedicated pipeline to choose documents, from which we've used entries with HIGH quality index and probability exceeding 90%.
This filtration and appropriate selection of texts enable the provision of a condensed and high-quality database of texts in Polish for training purposes.
## Training
* Framework: [ALLaMo](https://github.com/chrisociepa/allamo)
* Visualizations: [W&B](https://wandb.ai)
<p align="center">
<img src="https://huggingface.co/speakleash/Bielik-7B-v0.1/raw/main/train_loss.png">
</p>
<p align="center">
<img src="https://huggingface.co/speakleash/Bielik-7B-v0.1/raw/main/train_ppl.png">
</p>
<p align="center">
<img src="https://huggingface.co/speakleash/Bielik-7B-v0.1/raw/main/train_acc.png">
</p>
### Training hyperparameters:
| **Hyperparameter** | **Value** |
|-----------------------------|------------------|
| Context length | 4096 |
| Micro Batch Size | 4 |
| Batch Size | 4194304 |
| Learning Rate (cosine) | 3e-05 -> 2e-05 |
| Warmup Iterations | 2000 |
| All Iterations | 17350 |
| Optimizer | AdamW |
| β1, β2 | 0.9, 0.95 |
| Adam_eps | 1e−8 |
| Weight Decay | 0.1 |
| Grad Clip | 1.0 |
| Precision | bfloat16 (mixed) |
### Quickstart
This model can be easily loaded using the AutoModelForCausalLM functionality.
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "speakleash/Bielik-7B-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
```
In order to reduce the memory usage, you can use smaller precision (`bfloat16`).
```python
import torch
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16)
```
And then you can use Hugging Face Pipelines to generate text:
```python
import transformers
text = "Najważniejszym celem człowieka na ziemi jest"
pipeline = transformers.pipeline("text-generation", model=model, tokenizer=tokenizer)
sequences = pipeline(max_new_tokens=100, do_sample=True, top_k=50, eos_token_id=tokenizer.eos_token_id, text_inputs=text)
for seq in sequences:
print(f"Result: {seq['generated_text']}")
```
Generated output:
> Najważniejszym celem człowieka na ziemi jest życie w pokoju, harmonii i miłości. Dla każdego z nas bardzo ważne jest, aby otaczać się kochanymi osobami.
## Evaluation
Models have been evaluated on [Open PL LLM Leaderboard](https://huggingface.co/spaces/speakleash/open_pl_llm_leaderboard) 5-shot. The benchmark evaluates models in NLP tasks like sentiment analysis, categorization, text classification but does not test chatting skills. Here are presented:
- Average - average score among all tasks normalized by baseline scores
- Reranking - reranking task, commonly used in RAG
- Reader (Generator) - open book question answering task, commonly used in RAG
- Perplexity (lower is better) - as a bonus, does not correlate with other scores and should not be used for model comparison
As of April 3, 2024, the following table showcases the current scores of pretrained and continuously pretrained models according to the Open PL LLM Leaderboard, evaluated in a 5-shot setting:
| | Average | RAG Reranking | RAG Reader | Perplexity |
|--------------------------------------------------------------------------------------|----------:|--------------:|-----------:|-----------:|
| **7B parameters models:** | | | | |
| Baseline (majority class) | 0.00 | 53.36 | - | - |
| OPI-PG/Qra-7b | 11.13 | 54.40 | 75.25 | 203.36 |
| meta-llama/Llama-2-7b-hf | 12.73 | 54.02 | 77.92 | 850.45 |
| internlm/internlm2-base-7b | 20.68 | 52.39 | 69.85 | 3110.92 |
| [Bielik-7B-v0.1](https://huggingface.co/speakleash/Bielik-7B-v0.1) | 29.38 | **62.13** | **88.39** | 123.31 |
| mistralai/Mistral-7B-v0.1 | 30.67 | 60.35 | 85.39 | 857.32 |
| internlm/internlm2-7b | 33.03 | 69.39 | 73.63 | 5498.23 |
| alpindale/Mistral-7B-v0.2-hf | 33.05 | 60.23 | 85.21 | 932.60 |
| speakleash/mistral-apt3-7B/spi-e0_hf (experimental) | **35.50** | **62.14** | 87.48 | 132.78 |
| | | | | |
| **Models with different sizes:** | | | | |
| sdadas/polish-gpt2-xl (1.7B) | -23.22 | 48.07 | 3.04 | 160.95 |
| Azurro/APT3-1B-Base (1B) | -8.23 | 51.49 | 18.94 | 249.90 |
| OPI-PG/Qra-1b (1B) | -5.44 | 47.65 | 38.51 | 398.96 |
| internlm/internlm2-1_8b (1.8B) | -2.78 | 49.37 | 31.88 | 60296.30 |
| OPI-PG/Qra-13b (13B) | 29.03 | 53.28 | 83.03 | 168.66 |
| upstage/SOLAR-10.7B-v1.0 (10.7B) | 38.12 | 75.81 | 86.39 | 641.05 |
| | | | | |
| **Polish instruction fine-tuned models:** | | | | |
| szymonrucinski/Curie-7B-v1 | 26.72 | 55.58 | 85.19 | 389.17 |
| Voicelab/trurl-2-7b | 18.85 | 60.67 | 77.19 | 1098.88 |
| [Bielik-7B-Instruct-v0.1](https://huggingface.co/speakleash/Bielik-7B-Instruct-v0.1) | 39.28 | 61.89 | 86.00 | 277.92 |
As you can see, Bielik-7B-v0.1 does not have the best Average score, but it has some clear advantages, e.g. the best score in the RAG Reader task.
The results in the above table were obtained without utilizing instruction templates for instructional models, instead treating them like base models.
This approach could skew the results, as instructional models are optimized with specific instructions in mind.
## Limitations and Biases
Bielik-7B-v0.1 is not intended for deployment without fine-tuning. It should not be used for human-facing interactions without further guardrails and user consent.
Bielik-7B-v0.1 can produce factually incorrect output, and should not be relied on to produce factually accurate data. Bielik-7B-v0.1 was trained on various public datasets. While great efforts have been taken to clear the training data, it is possible that this model can generate lewd, false, biased or otherwise offensive outputs.
## License
The model is licensed under Apache 2.0, which allows for commercial use.
## Citation
Please cite this model using the following format:
```
@misc{Bielik7Bv01,
title = {Introducing Bielik-7B-v0.1: Polish Language Model},
author = {Ociepa, Krzysztof and Flis, Łukasz and Wróbel, Krzysztof and Gwoździej, Adrian and {SpeakLeash Team} and {Cyfronet Team}},
year = {2024},
url = {https://huggingface.co/speakleash/Bielik-7B-v0.1},
note = {Accessed: 2024-04-01}, % change this date
urldate = {2024-04-01} % change this date
}
```
## Responsible for training the model
* [Krzysztof Ociepa](https://www.linkedin.com/in/krzysztof-ociepa-44886550/)<sup>SpeakLeash</sup> - team leadership, conceptualizing, data preparation, process optimization and oversight of training
* [Łukasz Flis](https://www.linkedin.com/in/lukasz-flis-0a39631/)<sup>Cyfronet AGH</sup> - coordinating and supervising the training
* [Adrian Gwoździej](https://www.linkedin.com/in/adrgwo/)<sup>SpeakLeash</sup> - data cleaning and quality
* [Krzysztof Wróbel](https://www.linkedin.com/in/wrobelkrzysztof/)<sup>SpeakLeash</sup> - benchmarks
The model could not have been created without the commitment and work of the entire SpeakLeash team, whose contribution is invaluable. Thanks to the hard work of many individuals, it was possible to gather a large amount of content in Polish and establish collaboration between the open-science SpeakLeash project and the HPC center: ACK Cyfronet AGH. Individuals who contributed to the creation of the model through their commitment to the open-science SpeakLeash project:
[Sebastian Kondracki](https://www.linkedin.com/in/sebastian-kondracki/),
[Maria Filipkowska](https://www.linkedin.com/in/maria-filipkowska/),
[Grzegorz Urbanowicz](https://www.linkedin.com/in/grzegorz-urbanowicz-05823469/),
[Szymon Baczyński](https://www.linkedin.com/in/szymon-baczynski/),
[Paweł Kiszczak](https://www.linkedin.com/in/paveu-kiszczak/),
[Igor Ciuciura](https://www.linkedin.com/in/igor-ciuciura-1763b52a6/),
[Paweł Cyrta](https://www.linkedin.com/in/cyrta),
[Jacek Chwiła](https://www.linkedin.com/in/jacek-chwila/),
[Jan Maria Kowalski](https://www.linkedin.com/in/janmariakowalski/),
[Karol Jezierski](https://www.linkedin.com/in/karol-jezierski/),
[Kamil Nonckiewicz](https://www.linkedin.com/in/kamil-nonckiewicz/),
[Izabela Babis](https://www.linkedin.com/in/izabela-babis-2274b8105/),
[Nina Babis](https://www.linkedin.com/in/nina-babis-00055a140/),
[Waldemar Boszko](https://www.linkedin.com/in/waldemarboszko),
[Remigiusz Kinas](https://www.linkedin.com/in/remigiusz-kinas/),
[Piotr Rybak](https://www.linkedin.com/in/piotrrybak/)
and many other wonderful researchers and enthusiasts of the AI world.
Members of the ACK Cyfronet AGH team providing valuable support and expertise:
[Szymon Mazurek](https://www.linkedin.com/in/sz-mazurek-ai/).
## Contact Us
If you have any questions or suggestions, please use the discussion tab. If you want to contact us directly, join our [Discord SpeakLeash](https://discord.gg/3G9DVM39). |
core42/jais-13b-chat | core42 | "2024-05-24T12:09:11Z" | 19,449 | 133 | transformers | [
"transformers",
"pytorch",
"jais",
"text-generation",
"Arabic",
"English",
"LLM",
"Decoder",
"causal-lm",
"conversational",
"custom_code",
"ar",
"en",
"arxiv:2308.16149",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2023-08-23T06:43:05Z" | ---
language:
- ar
- en
thumbnail: null
tags:
- Arabic
- English
- LLM
- Decoder
- causal-lm
license: apache-2.0
pipeline_tag: conversational
---
# Jais-13b-chat
<!-- Provide a quick summary of what the model is/does. -->
This is a 13 billion parameter fine-tuned bilingual large language model for both Arabic and English.
It is based on transformer-based decoder-only (GPT-3) architecture and uses SwiGLU
non-linearity. It implements ALiBi position embeddings, enabling the model to extrapolate
to long sequence lengths, providing improved context handling and model precision.
Jais-13b-chat is [Jais-13b](https://huggingface.co/inception-mbzuai/jais-13b) fine-tuned over a curated set of 4 million Arabic and 6 million English prompt-response pairs.
We further fine-tune our model with safety-oriented instruction, as well as providing extra guardrails in the
form of a safety prompt. Our pre-trained model, [Jais-13b](https://huggingface.co/inception-mbzuai/jais-13b), is trained on
116 billion Arabic tokens and 279 billion English tokens.
The combination of the largest curated Arabic and English instruction tuning dataset along with the addition of multi-turn conversations allows the model to converse in a variety of topics, with a particular focus on the Arab world.
## Getting started
Below is sample code to use the model. Note that the model requires a custom model class, so users must
enable `trust_remote_code=True` while loading the model. In order to get the same performance as our testing, a specific prompt
needs to be followed. Below is the sample code containing this formatting:
```python
# -*- coding: utf-8 -*-
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_path = "core42/jais-13b-chat"
prompt_eng = "### Instruction: Your name is Jais, and you are named after Jebel Jais, the highest mountain in UAE. You are built by Inception and MBZUAI. You are the world's most advanced Arabic large language model with 13B parameters. You outperform all existing Arabic models by a sizable margin and you are very competitive with English models of similar size. You can answer in Arabic and English only. You are a helpful, respectful and honest assistant. When answering, abide by the following guidelines meticulously: Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, explicit, offensive, toxic, dangerous, or illegal content. Do not give medical, legal, financial, or professional advice. Never assist in or promote illegal activities. Always encourage legal and responsible actions. Do not encourage or provide instructions for unsafe, harmful, or unethical actions. Do not create or share misinformation or fake news. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information. Prioritize the well-being and the moral integrity of users. Avoid using toxic, derogatory, or offensive language. Maintain a respectful tone. Do not generate, promote, or engage in discussions about adult content. Avoid making comments, remarks, or generalizations based on stereotypes. Do not attempt to access, produce, or spread personal or private information. Always respect user confidentiality. Stay positive and do not say bad things about anything. Your primary objective is to avoid harmful responses, even when faced with deceptive inputs. Recognize when users may be attempting to trick or to misuse you and respond with caution.\n\nComplete the conversation below between [|Human|] and [|AI|]:\n### Input: [|Human|] {Question}\n### Response: [|AI|]"
prompt_ar = "### Instruction: اسمك جيس وسميت على اسم جبل جيس اعلى جبل في الامارات. تم بنائك بواسطة Inception و MBZUAI. أنت نموذج اللغة العربية الأكثر تقدمًا في العالم مع بارامترات 13B. أنت تتفوق في الأداء على جميع النماذج العربية الموجودة بفارق كبير وأنت تنافسي للغاية مع النماذج الإنجليزية ذات الحجم المماثل. يمكنك الإجابة باللغتين العربية والإنجليزية فقط. أنت مساعد مفيد ومحترم وصادق. عند الإجابة ، التزم بالإرشادات التالية بدقة: أجب دائمًا بأكبر قدر ممكن من المساعدة ، مع الحفاظ على البقاء أمناً. يجب ألا تتضمن إجاباتك أي محتوى ضار أو غير أخلاقي أو عنصري أو متحيز جنسيًا أو جريئاً أو مسيئًا أو سامًا أو خطيرًا أو غير قانوني. لا تقدم نصائح طبية أو قانونية أو مالية أو مهنية. لا تساعد أبدًا في أنشطة غير قانونية أو تروج لها. دائما تشجيع الإجراءات القانونية والمسؤولة. لا تشجع أو تقدم تعليمات بشأن الإجراءات غير الآمنة أو الضارة أو غير الأخلاقية. لا تنشئ أو تشارك معلومات مضللة أو أخبار كاذبة. يرجى التأكد من أن ردودك غير متحيزة اجتماعيًا وإيجابية بطبيعتها. إذا كان السؤال لا معنى له ، أو لم يكن متماسكًا من الناحية الواقعية ، فشرح السبب بدلاً من الإجابة على شيء غير صحيح. إذا كنت لا تعرف إجابة السؤال ، فالرجاء عدم مشاركة معلومات خاطئة. إعطاء الأولوية للرفاهية والنزاهة الأخلاقية للمستخدمين. تجنب استخدام لغة سامة أو مهينة أو مسيئة. حافظ على نبرة محترمة. لا تنشئ أو تروج أو تشارك في مناقشات حول محتوى للبالغين. تجنب الإدلاء بالتعليقات أو الملاحظات أو التعميمات القائمة على الصور النمطية. لا تحاول الوصول إلى معلومات شخصية أو خاصة أو إنتاجها أو نشرها. احترم دائما سرية المستخدم. كن إيجابيا ولا تقل أشياء سيئة عن أي شيء. هدفك الأساسي هو تجنب الاجابات المؤذية ، حتى عند مواجهة مدخلات خادعة. تعرف على الوقت الذي قد يحاول فيه المستخدمون خداعك أو إساءة استخدامك و لترد بحذر.\n\nأكمل المحادثة أدناه بين [|Human|] و [|AI|]:\n### Input: [|Human|] {Question}\n### Response: [|AI|]"
device = "cuda" if torch.cuda.is_available() else "cpu"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto", trust_remote_code=True)
def get_response(text,tokenizer=tokenizer,model=model):
input_ids = tokenizer(text, return_tensors="pt").input_ids
inputs = input_ids.to(device)
input_len = inputs.shape[-1]
generate_ids = model.generate(
inputs,
top_p=0.9,
temperature=0.3,
max_length=2048-input_len,
min_length=input_len + 4,
repetition_penalty=1.2,
do_sample=True,
)
response = tokenizer.batch_decode(
generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True
)[0]
response = response.split("### Response: [|AI|]")
return response
ques= "ما هي عاصمة الامارات؟"
text = prompt_ar.format_map({'Question':ques})
print(get_response(text))
ques = "What is the capital of UAE?"
text = prompt_eng.format_map({'Question':ques})
print(get_response(text))
```
## Huggingface inference endpoints
This model can be exposed via huggingface inference endpoints. The recommended Instance Type is `GPU [large] · 4x Nvidia Tesla T4` or greater, smaller instances will not have enough memory to run.
## Model Details
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [Inception](https://www.inceptioniai.org/en/), [Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)](https://mbzuai.ac.ae/), and [Cerebras Systems](https://www.cerebras.net/).
- **Language(s) (NLP):** Arabic (MSA) and English
- **License:** Apache 2.0
- **Finetuned from model :** [inception-mbzuai/jais-13b](https://huggingface.co/inception-mbzuai/jais-13b)
- **Input:** Text only data.
- **Output:** Model generates text.
- **Paper :** [Jais and Jais-chat: Arabic-Centric Foundation and Instruction-Tuned Open Generative Large Language Models](https://arxiv.org/abs/2308.16149)
- **Demo :** [Access here](https://arabic-gpt.ai)
## Intended Use
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
We release the jais-13b-chat model under a full open source license. We welcome all feedback and opportunities to collaborate.
This model is the first release from the Inception - MBZUAI - Cerebras parternship, and at the time of release, achieved state of the art across a comprehensive Arabic test suite as described in the accompanying tech report.
Some potential downstream uses include:
- *Research*: This model can be used by researchers and developers.
- *Commercial Use*: Jais-13b-chat can be directly used for chat with suitable prompting or further fine-tuned for specific use cases.
Some potential use cases include:
- Chat-assistants.
- Customer service.
Audiences that we hope will benefit from our model:
- *Academics*: For those researching Arabic natural language processing.
- *Businesses*: Companies targeting Arabic-speaking audiences.
- *Developers*: Those integrating Arabic language capabilities in apps.
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
While jais-13b-chat is a powerful Arabic and English bilingual model, it's essential to understand its limitations and the potential of misuse.
It is prohibited to use the model in any manner that violates applicable laws or regulations.
The following are some example scenarios where the model should not be used.
- *Malicious Use*: The model should not be used for generating harmful, misleading, or inappropriate content. This includes but is not limited to:
- Generating or promoting hate speech, violence, or discrimination.
- Spreading misinformation or fake news.
- Engaging in or promoting illegal activities.
- *Sensitive Information*: The model should not be used to handle or generate personal, confidential, or sensitive information.
- *Generalization Across All Languages*: Jais-13b is bilingual and optimized for Arabic and English, it should not be assumed to have equal proficiency in other languages or dialects.
- *High-Stakes Decisions*: The model should not be used to make high-stakes decisions without human oversight. This includes medical, legal, financial, or safety-critical decisions.
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
The model is trained on publicly available data which was in part curated by Inception. We have employed different
techniqes to reduce bias in the model. While efforts have been made to minimize biases, it is likely that the model, as with all LLM models, will exhibit some bias.
The model is trained as an AI assistant for Arabic and English speakers. The model is limited to produce responses for queries in these two languages
and may not produce appropriate responses to other language queries.
By using Jais, you acknowledge and accept that, as with any large language model, it may generate incorrect, misleading and/or offensive information or content.
The information is not intended as advice and should not be relied upon in any way, nor are we responsible for any of the content or consequences resulting from its use.
We are continuously working to develop models with greater capabilities, and as such, welcome any feedback on the model
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
jais-13b-chat model is finetuned with both Arabic and English prompt-response pairs. We included a wide range of
instructional data across various domains. In total, our instruction-tuning
dataset has 3.8M and 5.9M prompt-response pairs for Arabic and English, respectively. For English, we used
publicly available instruction tuning datasets. For Arabic, we internally curated instruction data and augmented it with translated Arabic data.
Further details about the training data can be found in the technical report.
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
In instruction tuning, each instance comprises a prompt and its corresponding response.
Padding is applied to each instance since, unlike pretraining, finetuning is done with unpacked data.
We utilize the same autoregressive objective as employed in the pretraining of the LLM.
However, we masked the loss on the prompt i.e. backpropagation is performed only on answer tokens.
The training process was performed on the Condor Galaxy 1 (CG-1) supercomputer platform.
#### Training Hyperparameters
| Hyperparameter | Value |
|----------------------------|----------------|
| Precision | fp32 |
| Optimizer | AdamW |
| Learning rate | 0 to 6.7e-04 (<= 400 steps) |
| | 6.7e-04 to 6.7e-05 (> 400 steps) |
| Weight decay | 0.1 |
| Batch size | 3392 |
| Steps | 8705 |
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
We conducted a comprehensive evaluation of Jais-chat and benchmarked it other leading base language models, focusing on both English and Arabic. The evaluation criteria spanned various dimensions, including:
- **Knowledge:** How well the model answers factual questions.
- **Reasoning:** The model's ability to answer questions requiring reasoning.
- **Misinformation/Bias:** Assessment of the model's susceptibility to generating false or misleading information, and its neutrality.
Arabic evaluation results:
| Models | Avg |EXAMS | MMLU (M) | LitQA | Hellaswag | PIQA | BoolQA | SituatedQA | ARC-C | OpenBookQA | TruthfulQA | CrowS-Pairs |
|-------------------|-------|------|----------|-------|-----------|------|--------|------------|-------|------------|------------|-------------|
| Jais-chat (13B) | **48.4** | 39.7 | 34.0 | 52.6 | 61.4 | 67.5 | 65.7 | 47.0 | 40.7 | 31.6 | 44.8 | 56.4 |
| BLOOMz (7.1B) | 42.9 | 34.9 | 31.0 | 44.0 | 38.1 | 59.1 | 66.6 | 42.8 | 30.2 | 29.2 | 48.4 | 55.8 |
| mT0-XXL (13B) | 40.9 | 31.5 | 31.2 | 36.6 | 33.9 | 56.1 | 77.8 | 44.7 | 26.1 | 27.8 | 44.5 | 45.3 |
| LLaMA2-Chat (13B) | 38.1 | 26.3 | 29.1 | 33.1 | 32.0 | 52.1 | 66.0 | 36.3 | 24.1 | 28.4 | 48.6 | 47.2 |
| AraBART (139M) | 36.7 | 26.5 | 27.5 | 34.3 | 28.1 | 52.6 | 57.1 | 34.6 | 25.1 | 28.6 | 49.8 | 48.8 |
| AraT5 (220M) | 32.0 | 24.7 | 23.8 | 26.3 | 25.5 | 50.4 | 58.2 | 33.9 | 24.7 | 25.4 | 20.9 | 47.2 |
All tasks above report accuracy or F1 scores (the higher the better). For the sake of brevity, we do not include results over English tasks.
Detailed comparisons in both languages and evaluation dataset details can be found in the technical report.
## Generation Example
<p align="center"> <img src="https://huggingface.co/inception-mbzuai/jais-13b/resolve/main/Rent_Example.png" width="600" /></p>
## Citation
```
@misc{sengupta2023jais,
title={Jais and Jais-chat: Arabic-Centric Foundation and Instruction-Tuned Open Generative Large Language Models},
author={Neha Sengupta and Sunil Kumar Sahu and Bokang Jia and Satheesh Katipomu and Haonan Li and Fajri Koto and Osama Mohammed Afzal and Samta Kamboj and Onkar Pandit and Rahul Pal and Lalit Pradhan and Zain Muhammad Mujahid and Massa Baali and Alham Fikri Aji and Zhengzhong Liu and Andy Hock and Andrew Feldman and Jonathan Lee and Andrew Jackson and Preslav Nakov and Timothy Baldwin and Eric Xing},
year={2023},
eprint={2308.16149},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
Copyright Inception Institute of Artificial Intelligence Ltd. |
openlm-research/open_llama_3b | openlm-research | "2023-06-16T00:44:10Z" | 19,424 | 143 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"dataset:togethercomputer/RedPajama-Data-1T",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-06-07T09:06:48Z" | ---
license: apache-2.0
datasets:
- togethercomputer/RedPajama-Data-1T
---
# OpenLLaMA: An Open Reproduction of LLaMA
In this repo, we present a permissively licensed open source reproduction of Meta AI's [LLaMA](https://ai.facebook.com/blog/large-language-model-llama-meta-ai/) large language model. We are releasing a 7B and 3B model trained on 1T tokens, as well as the preview of a 13B model trained on 600B tokens. We provide PyTorch and JAX weights of pre-trained OpenLLaMA models, as well as evaluation results and comparison against the original LLaMA models. Please see the [project homepage of OpenLLaMA](https://github.com/openlm-research/open_llama) for more details.
## Weights Release, License and Usage
We release the weights in two formats: an EasyLM format to be use with our [EasyLM framework](https://github.com/young-geng/EasyLM), and a PyTorch format to be used with the [Hugging Face transformers](https://huggingface.co/docs/transformers/index) library. Both our training framework EasyLM and the checkpoint weights are licensed permissively under the Apache 2.0 license.
### Loading the Weights with Hugging Face Transformers
Preview checkpoints can be directly loaded from Hugging Face Hub. **Please note that it is advised to avoid using the Hugging Face fast tokenizer for now, as we’ve observed that the auto-converted fast tokenizer sometimes gives incorrect tokenizations.** This can be achieved by directly using the `LlamaTokenizer` class, or passing in the `use_fast=False` option for the `AutoTokenizer` class. See the following example for usage.
```python
import torch
from transformers import LlamaTokenizer, LlamaForCausalLM
model_path = 'openlm-research/open_llama_3b'
# model_path = 'openlm-research/open_llama_7b'
tokenizer = LlamaTokenizer.from_pretrained(model_path)
model = LlamaForCausalLM.from_pretrained(
model_path, torch_dtype=torch.float16, device_map='auto',
)
prompt = 'Q: What is the largest animal?\nA:'
input_ids = tokenizer(prompt, return_tensors="pt").input_ids
generation_output = model.generate(
input_ids=input_ids, max_new_tokens=32
)
print(tokenizer.decode(generation_output[0]))
```
For more advanced usage, please follow the [transformers LLaMA documentation](https://huggingface.co/docs/transformers/main/model_doc/llama).
### Evaluating with LM-Eval-Harness
The model can be evaluated with [lm-eval-harness](https://github.com/EleutherAI/lm-evaluation-harness). However, due to the aforementioned tokenizer issue, we need to avoid using the fast tokenizer to obtain the correct results. This can be achieved by passing in `use_fast=False` to [this part of lm-eval-harness](https://github.com/EleutherAI/lm-evaluation-harness/blob/4b701e228768052cfae9043dca13e82052ca5eea/lm_eval/models/huggingface.py#LL313C9-L316C10), as shown in the example below:
```python
tokenizer = self.AUTO_TOKENIZER_CLASS.from_pretrained(
pretrained if tokenizer is None else tokenizer,
revision=revision + ("/" + subfolder if subfolder is not None else ""),
use_fast=False
)
```
### Loading the Weights with EasyLM
For using the weights in our EasyLM framework, please refer to the [LLaMA documentation of EasyLM](https://github.com/young-geng/EasyLM/blob/main/docs/llama.md). Note that unlike the original LLaMA model, our OpenLLaMA tokenizer and weights are trained completely from scratch so it is no longer needed to obtain the original LLaMA tokenizer and weights. Note that we use BOS (beginning of sentence) token (id=1) during training, so it is best to prepend this token for best performance during few-shot evaluation.
## Dataset and Training
We train our models on the [RedPajama](https://www.together.xyz/blog/redpajama) dataset released by [Together](https://www.together.xyz/), which is a reproduction of the LLaMA training dataset containing over 1.2 trillion tokens. We follow the exactly same preprocessing steps and training hyperparameters as the original LLaMA paper, including model architecture, context length, training steps, learning rate schedule, and optimizer. The only difference between our setting and the original one is the dataset used: OpenLLaMA employs the RedPajama dataset rather than the one utilized by the original LLaMA.
We train the models on cloud TPU-v4s using [EasyLM](https://github.com/young-geng/EasyLM), a JAX based training pipeline we developed for training and fine-tuning large language models. We employ a combination of normal data parallelism and [fully sharded data parallelism (also know as ZeRO stage 3)](https://engineering.fb.com/2021/07/15/open-source/fsdp/) to balance the training throughput and memory usage. Overall we reach a throughput of over 2200 tokens / second / TPU-v4 chip for our 7B model.
## Evaluation
We evaluated OpenLLaMA on a wide range of tasks using [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness). The LLaMA results are generated by running the original LLaMA model on the same evaluation metrics. We note that our results for the LLaMA model differ slightly from the original LLaMA paper, which we believe is a result of different evaluation protocols. Similar differences have been reported in [this issue of lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness/issues/443). Additionally, we present the results of GPT-J, a 6B parameter model trained on the [Pile](https://pile.eleuther.ai/) dataset by [EleutherAI](https://www.eleuther.ai/).
The original LLaMA model was trained for 1 trillion tokens and GPT-J was trained for 500 billion tokens. We present the results in the table below. OpenLLaMA exhibits comparable performance to the original LLaMA and GPT-J across a majority of tasks, and outperforms them in some tasks.
| **Task/Metric** | GPT-J 6B | LLaMA 7B | OpenLLaMA 7B | OpenLLaMA 3B | OpenLLaMA 13B 600BT |
| ---------------------- | -------- | -------- | ------------ | ------------ | ------------------- |
| anli_r1/acc | 0.32 | 0.35 | 0.33 | 0.33 | 0.33 |
| anli_r2/acc | 0.34 | 0.34 | 0.36 | 0.32 | 0.35 |
| anli_r3/acc | 0.35 | 0.37 | 0.38 | 0.35 | 0.38 |
| arc_challenge/acc | 0.34 | 0.39 | 0.37 | 0.34 | 0.39 |
| arc_challenge/acc_norm | 0.37 | 0.41 | 0.38 | 0.37 | 0.42 |
| arc_easy/acc | 0.67 | 0.68 | 0.72 | 0.69 | 0.74 |
| arc_easy/acc_norm | 0.62 | 0.52 | 0.68 | 0.65 | 0.70 |
| ddboolq/acc | 0.50 | 0.56 | 0.53 | 0.49 | 0.71 |
| hellaswag/acc | 0.36 | 0.36 | 0.63 | 0.43 | 0.54 |
| hellaswag/acc_norm | 0.66 | 0.73 | 0.72 | 0.67 | 0.73 |
| openbookqa/acc | 0.29 | 0.29 | 0.30 | 0.27 | 0.30 |
| openbookqa/acc_norm | 0.38 | 0.41 | 0.40 | 0.40 | 0.41 |
| piqa/acc | 0.75 | 0.78 | 0.76 | 0.75 | 0.77 |
| piqa/acc_norm | 0.76 | 0.78 | 0.77 | 0.76 | 0.78 |
| record/em | 0.88 | 0.91 | 0.89 | 0.88 | 0.90 |
| record/f1 | 0.89 | 0.91 | 0.90 | 0.89 | 0.90 |
| rte/acc | 0.54 | 0.56 | 0.60 | 0.58 | 0.65 |
| truthfulqa_mc/mc1 | 0.20 | 0.21 | 0.23 | 0.22 | 0.22 |
| truthfulqa_mc/mc2 | 0.36 | 0.34 | 0.35 | 0.35 | 0.35 |
| wic/acc | 0.50 | 0.50 | 0.51 | 0.48 | 0.49 |
| winogrande/acc | 0.64 | 0.68 | 0.67 | 0.62 | 0.67 |
| Average | 0.51 | 0.53 | 0.55 | 0.52 | 0.56 |
We removed the task CB and WSC from our benchmark, as our model performs suspiciously well on these two tasks. We hypothesize that there could be a benchmark data contamination in the training set.
## Contact
We would love to get feedback from the community. If you have any questions, please open an issue or contact us.
OpenLLaMA is developed by:
[Xinyang Geng](https://young-geng.xyz/)* and [Hao Liu](https://www.haoliu.site/)* from Berkeley AI Research.
*Equal Contribution
## Acknowledgment
We thank the [Google TPU Research Cloud](https://sites.research.google/trc/about/) program for providing part of the computation resources. We’d like to specially thank Jonathan Caton from TPU Research Cloud for helping us organizing compute resources, Rafi Witten from the Google Cloud team and James Bradbury from the Google JAX team for helping us optimizing our training throughput. We’d also want to thank Charlie Snell, Gautier Izacard, Eric Wallace, Lianmin Zheng and our user community for the discussions and feedback.
The OpenLLaMA 13B model is trained in collaboration with [Stability AI](https://stability.ai/), and we thank Stability AI for providing the computation resources. We’d like to especially thank David Ha and Shivanshu Purohit for the coordinating the logistics and providing engineering support.
## Reference
If you found OpenLLaMA useful in your research or applications, please cite using the following BibTeX:
```
@software{openlm2023openllama,
author = {Geng, Xinyang and Liu, Hao},
title = {OpenLLaMA: An Open Reproduction of LLaMA},
month = May,
year = 2023,
url = {https://github.com/openlm-research/open_llama}
}
```
```
@software{together2023redpajama,
author = {Together Computer},
title = {RedPajama-Data: An Open Source Recipe to Reproduce LLaMA training dataset},
month = April,
year = 2023,
url = {https://github.com/togethercomputer/RedPajama-Data}
}
```
```
@article{touvron2023llama,
title={Llama: Open and efficient foundation language models},
author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and others},
journal={arXiv preprint arXiv:2302.13971},
year={2023}
}
```
|
speakleash/Bielik-7B-Instruct-v0.1 | speakleash | "2024-04-12T09:02:48Z" | 19,413 | 46 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"finetuned",
"conversational",
"pl",
"arxiv:2005.01643",
"arxiv:2309.11235",
"arxiv:2006.09092",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-03-30T14:41:27Z" | ---
license: cc-by-nc-4.0
language:
- pl
library_name: transformers
tags:
- finetuned
inference:
parameters:
temperature: 0.6
widget:
- messages:
- role: user
content: Co przedstawia polskie godło?
---
<p align="center">
<img src="https://huggingface.co/speakleash/Bielik-7B-Instruct-v0.1/raw/main/speakleash_cyfronet.png">
</p>
# Bielik-7B-Instruct-v0.1
The Bielik-7B-Instruct-v0.1 is an instruct fine-tuned version of the [Bielik-7B-v0.1](https://huggingface.co/speakleash/Bielik-7B-v0.1). Forementioned model stands as a testament to the unique collaboration between the open-science/open-souce project SpeakLeash and the High Performance Computing (HPC) center: ACK Cyfronet AGH. Developed and trained on Polish text corpora, which has been cherry-picked and processed by the SpeakLeash team, this endeavor leverages Polish large-scale computing infrastructure, specifically within the PLGrid environment, and more precisely, the HPC centers: ACK Cyfronet AGH. The creation and training of the Bielik-7B-Instruct-v0.1 was propelled by the support of computational grant number PLG/2024/016951, conducted on the Helios supercomputer, enabling the use of cutting-edge technology and computational resources essential for large-scale machine learning processes. As a result, the model exhibits an exceptional ability to understand and process the Polish language, providing accurate responses and performing a variety of linguistic tasks with high precision.
[We have prepared quantized versions of the model as well as MLX format.](#quant-and-mlx-versions)
## Model
The [SpeakLeash](https://speakleash.org/) team is working on their own set of instructions in Polish, which is continuously being expanded and refined by annotators. A portion of these instructions, which had been manually verified and corrected, has been utilized for training purposes. Moreover, due to the limited availability of high-quality instructions in Polish, publicly accessible collections of instructions in English were used - [OpenHermes-2.5](https://huggingface.co/datasets/teknium/OpenHermes-2.5) and [orca-math-word-problems-200k](https://huggingface.co/datasets/microsoft/orca-math-word-problems-200k), which accounted for half of the instructions used in training. The instructions varied in quality, leading to a deterioration in model’s performance. To counteract this while still allowing ourselves to utilize forementioned datasets,several improvements were introduced:
* Weighted tokens level loss - a strategy inspired by [offline reinforcement learning](https://arxiv.org/abs/2005.01643) and [C-RLFT](https://arxiv.org/abs/2309.11235)
* Adaptive learning rate inspired by the study on [Learning Rates as a Function of Batch Size](https://arxiv.org/abs/2006.09092)
* Masked user instructions
Bielik-7B-Instruct-v0.1 has been trained with the use of an original open source framework called [ALLaMo](https://github.com/chrisociepa/allamo) implemented by [Krzysztof Ociepa](https://www.linkedin.com/in/krzysztof-ociepa-44886550/). This framework allows users to train language models with architecture similar to LLaMA and Mistral in fast and efficient way.
### Model description:
* **Developed by:** [SpeakLeash](https://speakleash.org/)
* **Language:** Polish
* **Model type:** causal decoder-only
* **Finetuned from:** [Bielik-7B-v0.1](https://huggingface.co/speakleash/Bielik-7B-v0.1)
* **License:** CC BY NC 4.0 (non-commercial use)
* **Model ref:** speakleash:e38140bea0d48f1218540800bbc67e89
## Training
* Framework: [ALLaMo](https://github.com/chrisociepa/allamo)
* Visualizations: [W&B](https://wandb.ai)
<p align="center">
<img src="https://huggingface.co/speakleash/Bielik-7B-Instruct-v0.1/raw/main/sft_train_loss.png">
</p>
<p align="center">
<img src="https://huggingface.co/speakleash/Bielik-7B-Instruct-v0.1/raw/main/sft_train_ppl.png">
</p>
<p align="center">
<img src="https://huggingface.co/speakleash/Bielik-7B-Instruct-v0.1/raw/main/sft_train_lr.png">
</p>
### Training hyperparameters:
| **Hyperparameter** | **Value** |
|-----------------------------|------------------|
| Context length | 4096 |
| Micro Batch Size | 1 |
| Batch Size | up to 4194304 |
| Learning Rate (cosine, adaptive) | 7e-6 -> 6e-7 |
| Warmup Iterations | 50 |
| All Iterations | 55440 |
| Optimizer | AdamW |
| β1, β2 | 0.9, 0.95 |
| Adam_eps | 1e−8 |
| Weight Decay | 0.05 |
| Grad Clip | 1.0 |
| Precision | bfloat16 (mixed) |
### Quant and MLX versions:
We know that some people want to explore smaller models or don't have the resources to run a full model. Therefore, we have prepared quantized versions of the Bielik-7B-Instruct-v0.1 model. We are also mindful of Apple Silicon.
<br>
<br>
Quantized versions (for non-GPU / weaker GPU):
- https://huggingface.co/speakleash/Bielik-7B-Instruct-v0.1-GGUF
- https://huggingface.co/speakleash/Bielik-7B-Instruct-v0.1-GPTQ
- https://huggingface.co/speakleash/Bielik-7B-Instruct-v0.1-AWQ
- https://huggingface.co/speakleash/Bielik-7B-Instruct-v0.1-EXL2
- https://huggingface.co/speakleash/Bielik-7B-Instruct-v0.1-3bit-HQQ
For Apple Silicon:
- https://huggingface.co/speakleash/Bielik-7B-Instruct-v0.1-MLX
### Instruction format
In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[/INST]` tokens. The very first instruction should start with the beginning of a sentence token. The generated completion will be finished by the end-of-sentence token.
E.g.
```
prompt = "<s>[INST] Jakie mamy pory roku? [/INST]"
completion = "W Polsce mamy 4 pory roku: wiosna, lato, jesień i zima.</s>"
```
This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model_name = "speakleash/Bielik-7B-Instruct-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16)
messages = [
{"role": "system", "content": "Odpowiadaj krótko, precyzyjnie i wyłącznie w języku polskim."},
{"role": "user", "content": "Jakie mamy pory roku w Polsce?"},
{"role": "assistant", "content": "W Polsce mamy 4 pory roku: wiosna, lato, jesień i zima."},
{"role": "user", "content": "Która jest najcieplejsza?"}
]
input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt")
model_inputs = input_ids.to(device)
model.to(device)
generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])
```
If for some reason you are unable to use `tokenizer.apply_chat_template`, the following code will enable you to generate a correct prompt:
```python
def chat_template(message, history, system_prompt):
prompt_builder = ["<s>[INST] "]
if system_prompt:
prompt_builder.append(f"<<SYS>>\n{system_prompt}\n<</SYS>>\n\n")
for human, assistant in history:
prompt_builder.append(f"{human} [/INST] {assistant}</s>[INST] ")
prompt_builder.append(f"{message} [/INST]")
return ''.join(prompt_builder)
system_prompt = "Odpowiadaj krótko, precyzyjnie i wyłącznie w języku polskim."
history = [
("Jakie mamy pory roku w Polsce?", "W Polsce mamy 4 pory roku: wiosna, lato, jesień i zima.")
]
message = "Która jest najcieplejsza?"
prompt = chat_template(message, history, system_prompt)
```
## Evaluation
Models have been evaluated on [Open PL LLM Leaderboard](https://huggingface.co/spaces/speakleash/open_pl_llm_leaderboard) 5-shot. The benchmark evaluates models in NLP tasks like sentiment analysis, categorization, text classification but does not test chatting skills. Here are presented:
- Average - average score among all tasks normalized by baseline scores
- Reranking - reranking task, commonly used in RAG
- Reader (Generator) - open book question answering task, commonly used in RAG
- Perplexity (lower is better) - as a bonus, does not correlate with other scores and should not be used for model comparison
As of April 3, 2024, the following table showcases the current scores of pretrained and continuously pretrained models according to the Open PL LLM Leaderboard, evaluated in a 5-shot setting:
| | Average | RAG Reranking | RAG Reader | Perplexity |
|--------------------------------------------------------------------------------------|----------:|--------------:|-----------:|-----------:|
| **7B parameters models:** | | | | |
| Baseline (majority class) | 0.00 | 53.36 | - | - |
| Voicelab/trurl-2-7b | 18.85 | 60.67 | 77.19 | 1098.88 |
| meta-llama/Llama-2-7b-chat-hf | 21.04 | 54.65 | 72.93 | 4018.74 |
| mistralai/Mistral-7B-Instruct-v0.1 | 26.42 | 56.35 | 73.68 | 6909.94 |
| szymonrucinski/Curie-7B-v1 | 26.72 | 55.58 | 85.19 | 389.17 |
| HuggingFaceH4/zephyr-7b-beta | 33.15 | 71.65 | 71.27 | 3613.14 |
| HuggingFaceH4/zephyr-7b-alpha | 33.97 | 71.47 | 73.35 | 4464.45 |
| internlm/internlm2-chat-7b-sft | 36.97 | 73.22 | 69.96 | 4269.63 |
| internlm/internlm2-chat-7b | 37.64 | 72.29 | 71.17 | 3892.50 |
| [Bielik-7B-Instruct-v0.1](https://huggingface.co/speakleash/Bielik-7B-Instruct-v0.1) | 39.28 | 61.89 | **86.00** | 277.92 |
| mistralai/Mistral-7B-Instruct-v0.2 | 40.29 | 72.58 | 79.39 | 2088.08 |
| teknium/OpenHermes-2.5-Mistral-7B | 42.64 | 70.63 | 80.25 | 1463.00 |
| openchat/openchat-3.5-1210 | 44.17 | 71.76 | 82.15 | 1923.83 |
| speakleash/mistral_7B-v2/spkl-all_sft_v2/e1_base/spkl-all_2e6-e1_70c70cc6 (experimental) | 45.44 | 71.27 | 91.50 | 279.24 |
| Nexusflow/Starling-LM-7B-beta | 45.69 | 74.58 | 81.22 | 1161.54 |
| openchat/openchat-3.5-0106 | 47.32 | 74.71 | 83.60 | 1106.56 |
| berkeley-nest/Starling-LM-7B-alpha | **47.46** | **75.73** | 82.86 | 1438.04 |
| | | | | |
| **Models with different sizes:** | | | | |
| Azurro/APT3-1B-Instruct-v1 (1B) | -13.80 | 52.11 | 12.23 | 739.09 |
| Voicelab/trurl-2-13b-academic (13B) | 29.45 | 68.19 | 79.88 | 733.91 |
| upstage/SOLAR-10.7B-Instruct-v1.0 (10.7B) | 46.07 | 76.93 | 82.86 | 789.58 |
| | | | | |
| **7B parameters pretrained and continously pretrained models:** | | | | |
| OPI-PG/Qra-7b | 11.13 | 54.40 | 75.25 | 203.36 |
| meta-llama/Llama-2-7b-hf | 12.73 | 54.02 | 77.92 | 850.45 |
| internlm/internlm2-base-7b | 20.68 | 52.39 | 69.85 | 3110.92 |
| [Bielik-7B-v0.1](https://huggingface.co/speakleash/Bielik-7B-v0.1) | 29.38 | 62.13 | **88.39** | 123.31 |
| mistralai/Mistral-7B-v0.1 | 30.67 | 60.35 | 85.39 | 857.32 |
| internlm/internlm2-7b | 33.03 | 69.39 | 73.63 | 5498.23 |
| alpindale/Mistral-7B-v0.2-hf | 33.05 | 60.23 | 85.21 | 932.60 |
| speakleash/mistral-apt3-7B/spi-e0_hf (experimental) | 35.50 | 62.14 | **87.48** | 132.78 |
SpeakLeash models have one of the best scores in the RAG Reader task.
We have managed to increase Average score by almost 9 pp. in comparison to Mistral-7B-v0.1.
In our subjective evaluations of chatting skills SpeakLeash models perform better than other models with higher Average scores.
The results in the above table were obtained without utilizing instruction templates for instructional models, instead treating them like base models.
This approach could skew the results, as instructional models are optimized with specific instructions in mind.
## Limitations and Biases
Bielik-7B-Instruct-v0.1 is a quick demonstration that the base model can be easily fine-tuned to achieve compelling and promising performance. It does not have any moderation mechanisms. We're looking forward to engaging with the community in ways to make the model respect guardrails, allowing for deployment in environments requiring moderated outputs.
Bielik-7B-Instruct-v0.1 can produce factually incorrect output, and should not be relied on to produce factually accurate data. Bielik-7B-Instruct-v0.1 was trained on various public datasets. While great efforts have been taken to clear the training data, it is possible that this model can generate lewd, false, biased or otherwise offensive outputs.
## License
Because of an unclear legal situation, we have decided to publish the model under CC BY NC 4.0 license - it allows for non-commercial use. The model can be used for scientific purposes and privately, as long as the license conditions are met.
## Citation
Please cite this model using the following format:
```
@misc{Bielik7Bv01,
title = {Introducing Bielik-7B-Instruct-v0.1: Instruct Polish Language Model},
author = {Ociepa, Krzysztof and Flis, Łukasz and Wróbel, Krzysztof and Kondracki, Sebastian and {SpeakLeash Team} and {Cyfronet Team}},
year = {2024},
url = {https://huggingface.co/speakleash/Bielik-7B-Instruct-v0.1},
note = {Accessed: 2024-04-01}, % change this date
urldate = {2024-04-01} % change this date
}
```
## Responsible for training the model
* [Krzysztof Ociepa](https://www.linkedin.com/in/krzysztof-ociepa-44886550/)<sup>SpeakLeash</sup> - team leadership, conceptualizing, data preparation, process optimization and oversight of training
* [Łukasz Flis](https://www.linkedin.com/in/lukasz-flis-0a39631/)<sup>Cyfronet AGH</sup> - coordinating and supervising the training
* [Krzysztof Wróbel](https://www.linkedin.com/in/wrobelkrzysztof/)<sup>SpeakLeash</sup> - benchmarks
* [Sebastian Kondracki](https://www.linkedin.com/in/sebastian-kondracki/)<sup>SpeakLeash</sup> - coordinating and preparation of instructions
* [Maria Filipkowska](https://www.linkedin.com/in/maria-filipkowska/)<sup>SpeakLeash</sup> - preparation of instructions
* [Paweł Kiszczak](https://www.linkedin.com/in/paveu-kiszczak/)<sup>SpeakLeash</sup> - preparation of instructions
* [Adrian Gwoździej](https://www.linkedin.com/in/adrgwo/)<sup>SpeakLeash</sup> - data quality and instructions cleaning
* [Igor Ciuciura](https://www.linkedin.com/in/igor-ciuciura-1763b52a6/)<sup>SpeakLeash</sup> - instructions cleaning
* [Jacek Chwiła](https://www.linkedin.com/in/jacek-chwila/)<sup>SpeakLeash</sup> - instructions cleaning
* [Remigiusz Kinas](https://www.linkedin.com/in/remigiusz-kinas/)<sup>SpeakLeash</sup> - providing quantized models
* [Szymon Baczyński](https://www.linkedin.com/in/szymon-baczynski/)<sup>SpeakLeash</sup> - providing quantized models
The model could not have been created without the commitment and work of the entire SpeakLeash team, whose contribution is invaluable. Thanks to the hard work of many individuals, it was possible to gather a large amount of content in Polish and establish collaboration between the open-science SpeakLeash project and the HPC center: ACK Cyfronet AGH. Individuals who contributed to the creation of the model through their commitment to the open-science SpeakLeash project:
[Grzegorz Urbanowicz](https://www.linkedin.com/in/grzegorz-urbanowicz-05823469/),
[Paweł Cyrta](https://www.linkedin.com/in/cyrta),
[Jan Maria Kowalski](https://www.linkedin.com/in/janmariakowalski/),
[Karol Jezierski](https://www.linkedin.com/in/karol-jezierski/),
[Kamil Nonckiewicz](https://www.linkedin.com/in/kamil-nonckiewicz/),
[Izabela Babis](https://www.linkedin.com/in/izabela-babis-2274b8105/),
[Nina Babis](https://www.linkedin.com/in/nina-babis-00055a140/),
[Waldemar Boszko](https://www.linkedin.com/in/waldemarboszko),
and many other wonderful researchers and enthusiasts of the AI world.
Members of the ACK Cyfronet AGH team providing valuable support and expertise:
[Szymon Mazurek](https://www.linkedin.com/in/sz-mazurek-ai/).
## Contact Us
If you have any questions or suggestions, please use the discussion tab. If you want to contact us directly, join our [Discord SpeakLeash](https://discord.gg/3G9DVM39).
|
duyntnet/ALMA-13B-R-imatrix-GGUF | duyntnet | "2024-06-21T10:44:53Z" | 19,380 | 0 | transformers | [
"transformers",
"gguf",
"imatrix",
"ALMA-13B-R",
"text-generation",
"en",
"license:other",
"region:us"
] | text-generation | "2024-06-21T04:03:54Z" | ---
license: other
language:
- en
pipeline_tag: text-generation
inference: false
tags:
- transformers
- gguf
- imatrix
- ALMA-13B-R
---
Quantizations of https://huggingface.co/haoranxu/ALMA-13B-R
# From original readme
A quick start to use our best system (ALMA-13B-R) for translation. An example of translating "我爱机器翻译。" into English:
```
import torch
from transformers import AutoModelForCausalLM
from transformers import AutoTokenizer
# Load base model and LoRA weights
model = AutoModelForCausalLM.from_pretrained("haoranxu/ALMA-13B-R", torch_dtype=torch.float16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("haoranxu/ALMA-13B-R", padding_side='left')
# Add the source sentence into the prompt template
prompt="Translate this from Chinese to English:\nChinese: 我爱机器翻译。\nEnglish:"
input_ids = tokenizer(prompt, return_tensors="pt", padding=True, max_length=40, truncation=True).input_ids.cuda()
# Translation
with torch.no_grad():
generated_ids = model.generate(input_ids=input_ids, num_beams=5, max_new_tokens=20, do_sample=True, temperature=0.6, top_p=0.9)
outputs = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
print(outputs)
``` |
monadical-labs/minecraft-skin-generator | monadical-labs | "2024-02-19T17:45:52Z" | 19,376 | 11 | diffusers | [
"diffusers",
"safetensors",
"minecraft",
"text-to-image",
"en",
"license:openrail",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | "2023-06-24T18:30:50Z" | ---
license: openrail
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- minecraft
---
# Minecraft Skin Generator
## 💥 Update - February 19th, 2024 💥
We have released a new open sourced [Minecraft Skin Generator XL](https://huggingface.co/monadical-labs/minecraft-skin-generator-sdxl) model based on Stable Diffusion XL. It offers many improvements over the original model, including support for the transparency layer.
Check it out in the [monadical-labs/minecraft-skin-generator-sdxl](https://huggingface.co/monadical-labs/minecraft-skin-generator-sdxl) respository.
We also offer a hosted version of the model on the [Minecraft Skin Generator](https://www.skingenerator.io) website.
## Overview
This Stable Diffusion model was fine-tuned to generate a pre-version 1.8 Minecraft character skins, based on a text prompt. The process for doing so is detailed in the Monadical blog post, [Even More Skin in the Game: Digging Deeper Into Stable Diffusion-Generated Minecraft Skins](https://monadical.com/posts/minecraft-skins-part2.html)
The model was fine-tuned on the dataset for 13,000 steps using the 'train_text_to_image.py' script provided with the diffusers library. A checkpoint has been included in the 'checkpoint' directory.
Some postprocessing is required to import and use the generated skins in Minecraft.
Example code to generate usable in-game Minecraft skins can be found in the [Monadical-SAS/minecraft_skin_generator](https://github.com/Monadical-SAS/minecraft_skin_generator) GitHub respository.
## Examples
Here are some example text prompts and the images they generate:
* "Albert Einstein"
* "Mahatma Ghandi"
* "A man in a purple suit wearing a top hat."
## Contact Information
You can contact me at: Cory Spencer \<[email protected]\>
[](https://monadical.com/) |
mradermacher/MFANNv0.15-GGUF | mradermacher | "2024-06-30T04:25:04Z" | 19,358 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:netcat420/MFANNv0.15",
"endpoints_compatible",
"region:us"
] | null | "2024-06-30T03:56:19Z" | ---
base_model: netcat420/MFANNv0.15
language:
- en
library_name: transformers
quantized_by: mradermacher
tags: []
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/netcat420/MFANNv0.15
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/MFANNv0.15-GGUF/resolve/main/MFANNv0.15.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/MFANNv0.15-GGUF/resolve/main/MFANNv0.15.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/MFANNv0.15-GGUF/resolve/main/MFANNv0.15.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/MFANNv0.15-GGUF/resolve/main/MFANNv0.15.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/MFANNv0.15-GGUF/resolve/main/MFANNv0.15.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/MFANNv0.15-GGUF/resolve/main/MFANNv0.15.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/MFANNv0.15-GGUF/resolve/main/MFANNv0.15.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/MFANNv0.15-GGUF/resolve/main/MFANNv0.15.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/MFANNv0.15-GGUF/resolve/main/MFANNv0.15.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/MFANNv0.15-GGUF/resolve/main/MFANNv0.15.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/MFANNv0.15-GGUF/resolve/main/MFANNv0.15.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/MFANNv0.15-GGUF/resolve/main/MFANNv0.15.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/MFANNv0.15-GGUF/resolve/main/MFANNv0.15.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/MFANNv0.15-GGUF/resolve/main/MFANNv0.15.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/MFANNv0.15-GGUF/resolve/main/MFANNv0.15.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
mradermacher/L3-Umbral-Mind-RP-v0.3-8B-i1-GGUF | mradermacher | "2024-06-28T21:35:01Z" | 19,318 | 0 | transformers | [
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"not-for-all-audiences",
"nsfw",
"rp",
"roleplay",
"role-play",
"en",
"base_model:Casual-Autopsy/L3-Umbral-Mind-RP-v0.3-8B",
"license:llama3",
"endpoints_compatible",
"region:us"
] | null | "2024-06-28T20:19:13Z" | ---
base_model: Casual-Autopsy/L3-Umbral-Mind-RP-v0.3-8B
language:
- en
library_name: transformers
license: llama3
quantized_by: mradermacher
tags:
- merge
- mergekit
- lazymergekit
- not-for-all-audiences
- nsfw
- rp
- roleplay
- role-play
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/Casual-Autopsy/L3-Umbral-Mind-RP-v0.3-8B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v0.3-8B-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v0.3-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v0.3-8B.i1-IQ1_S.gguf) | i1-IQ1_S | 2.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v0.3-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v0.3-8B.i1-IQ1_M.gguf) | i1-IQ1_M | 2.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v0.3-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v0.3-8B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.5 | |
| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v0.3-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v0.3-8B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v0.3-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v0.3-8B.i1-IQ2_S.gguf) | i1-IQ2_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v0.3-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v0.3-8B.i1-IQ2_M.gguf) | i1-IQ2_M | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v0.3-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v0.3-8B.i1-Q2_K.gguf) | i1-Q2_K | 3.3 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v0.3-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v0.3-8B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v0.3-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v0.3-8B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v0.3-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v0.3-8B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.8 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v0.3-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v0.3-8B.i1-IQ3_S.gguf) | i1-IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v0.3-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v0.3-8B.i1-IQ3_M.gguf) | i1-IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v0.3-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v0.3-8B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.1 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v0.3-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v0.3-8B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.4 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v0.3-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v0.3-8B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v0.3-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v0.3-8B.i1-Q4_0.gguf) | i1-Q4_0 | 4.8 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v0.3-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v0.3-8B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.8 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v0.3-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v0.3-8B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v0.3-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v0.3-8B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v0.3-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v0.3-8B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v0.3-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v0.3-8B.i1-Q6_K.gguf) | i1-Q6_K | 6.7 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants.
<!-- end -->
|
timm/inception_resnet_v2.tf_in1k | timm | "2023-05-10T01:09:00Z" | 19,282 | 0 | timm | [
"timm",
"pytorch",
"safetensors",
"image-classification",
"dataset:imagenet-1k",
"arxiv:1602.07261",
"license:apache-2.0",
"region:us"
] | image-classification | "2023-04-25T21:33:47Z" | ---
tags:
- image-classification
- timm
library_name: timm
license: apache-2.0
datasets:
- imagenet-1k
---
# Model card for inception_resnet_v2.tf_in1k
A Inception-ResNet-v2 image classification model. Trained on ImageNet-1k paper authors. Ported from Tensorflow via Cadene's pretrained-models.pytorch.
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 55.8
- GMACs: 13.2
- Activations (M): 25.1
- Image size: 299 x 299
- **Papers:**
- https://arxiv.org/abs/1602.07261: https://arxiv.org/abs/1602.07261
- **Original:**
- https://github.com/tensorflow/models
- https://github.com/Cadene/pretrained-models.pytorch
- **Dataset:** ImageNet-1k
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('inception_resnet_v2.tf_in1k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Feature Map Extraction
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'inception_resnet_v2.tf_in1k',
pretrained=True,
features_only=True,
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
for o in output:
# print shape of each feature map in output
# e.g.:
# torch.Size([1, 64, 147, 147])
# torch.Size([1, 192, 71, 71])
# torch.Size([1, 320, 35, 35])
# torch.Size([1, 1088, 17, 17])
# torch.Size([1, 1536, 8, 8])
print(o.shape)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'inception_resnet_v2.tf_in1k',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 1536, 8, 8) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
## Citation
```bibtex
@article{Szegedy2016Inceptionv4IA,
title={Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning},
author={Christian Szegedy and Sergey Ioffe and Vincent Vanhoucke and Alexander A. Alemi},
journal={ArXiv},
year={2016},
volume={abs/1602.07261}
}
```
|
AmelieSchreiber/esm2_t12_35M_lora_binding_sites_770K_v1 | AmelieSchreiber | "2023-09-29T03:54:57Z" | 19,275 | 0 | peft | [
"peft",
"license:mit",
"region:us"
] | null | "2023-09-23T23:52:24Z" | ---
license: mit
library_name: peft
---
## Training procedure
Generalization capabilities based on [this notebook](https://huggingface.co/AmelieSchreiber/esm2_t12_35M_lora_binding_sites_770K_v1/blob/main/esmbind-validation-notebook.ipynb)
are still lacking.
Test Metrics:
```python
'eval_loss': 0.4365999102592468,
'eval_accuracy': 0.9420512498988455,
'eval_precision': 0.2294216317399737,
'eval_recall': 0.7584797572261781,
'eval_f1': 0.35228549223252686,
'eval_auc': 0.8522129916967822,
'eval_mcc': 0.39725936154292724,
```
The metrics on the train and test datasets from the paper mentioned in
[this notebook](https://huggingface.co/AmelieSchreiber/esm2_t12_35M_lora_binding_sites_770K_v1/blob/main/esmbind-validation-notebook.ipynb) are:
```python
Train metrics:
Average Accuracy: 0.9169
Average Precision: 0.1416
Average Recall: 0.2769
Average F1 Score: 0.1743
Average AUC: 0.6088
Average MCC: 0.1522
Test metrics:
Average Accuracy: 0.9199
Average Precision: 0.1307
Average Recall: 0.2750
Average F1 Score: 0.1677
Average AUC: 0.6081
Average MCC: 0.1474
```
### Framework versions
- PEFT 0.5.0
|
bigscience/mt0-small | bigscience | "2023-09-26T09:16:55Z" | 19,271 | 20 | transformers | [
"transformers",
"pytorch",
"onnx",
"safetensors",
"mt5",
"text2text-generation",
"af",
"am",
"ar",
"az",
"be",
"bg",
"bn",
"ca",
"ceb",
"co",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fil",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha",
"haw",
"hi",
"hmn",
"ht",
"hu",
"hy",
"ig",
"is",
"it",
"iw",
"ja",
"jv",
"ka",
"kk",
"km",
"kn",
"ko",
"ku",
"ky",
"la",
"lb",
"lo",
"lt",
"lv",
"mg",
"mi",
"mk",
"ml",
"mn",
"mr",
"ms",
"mt",
"my",
"ne",
"nl",
"no",
"ny",
"pa",
"pl",
"ps",
"pt",
"ro",
"ru",
"sd",
"si",
"sk",
"sl",
"sm",
"sn",
"so",
"sq",
"sr",
"st",
"su",
"sv",
"sw",
"ta",
"te",
"tg",
"th",
"tr",
"uk",
"und",
"ur",
"uz",
"vi",
"xh",
"yi",
"yo",
"zh",
"zu",
"dataset:bigscience/xP3",
"dataset:mc4",
"arxiv:2211.01786",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2022-10-27T19:22:09Z" | ---
datasets:
- bigscience/xP3
- mc4
license: apache-2.0
language:
- af
- am
- ar
- az
- be
- bg
- bn
- ca
- ceb
- co
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fil
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- haw
- hi
- hmn
- ht
- hu
- hy
- ig
- is
- it
- iw
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
- lb
- lo
- lt
- lv
- mg
- mi
- mk
- ml
- mn
- mr
- ms
- mt
- my
- ne
- nl
- no
- ny
- pa
- pl
- ps
- pt
- ro
- ru
- sd
- si
- sk
- sl
- sm
- sn
- so
- sq
- sr
- st
- su
- sv
- sw
- ta
- te
- tg
- th
- tr
- uk
- und
- ur
- uz
- vi
- xh
- yi
- yo
- zh
- zu
pipeline_tag: text2text-generation
widget:
- text: "一个传奇的开端，一个不灭的神话，这不仅仅是一部电影，而是作为一个走进新时代的标签，永远彪炳史册。Would you rate the previous review as positive, neutral or negative?"
example_title: "zh-en sentiment"
- text: "一个传奇的开端，一个不灭的神话，这不仅仅是一部电影，而是作为一个走进新时代的标签，永远彪炳史册。你认为这句话的立场是赞扬、中立还是批评？"
example_title: "zh-zh sentiment"
- text: "Suggest at least five related search terms to \"Mạng neural nhân tạo\"."
example_title: "vi-en query"
- text: "Proposez au moins cinq mots clés concernant «Réseau de neurones artificiels»."
example_title: "fr-fr query"
- text: "Explain in a sentence in Telugu what is backpropagation in neural networks."
example_title: "te-en qa"
- text: "Why is the sky blue?"
example_title: "en-en qa"
- text: "Write a fairy tale about a troll saving a princess from a dangerous dragon. The fairy tale is a masterpiece that has achieved praise worldwide and its moral is \"Heroes Come in All Shapes and Sizes\". Story (in Spanish):"
example_title: "es-en fable"
- text: "Write a fable about wood elves living in a forest that is suddenly invaded by ogres. The fable is a masterpiece that has achieved praise worldwide and its moral is \"Violence is the last refuge of the incompetent\". Fable (in Hindi):"
example_title: "hi-en fable"
model-index:
- name: mt0-small
results:
- task:
type: Coreference resolution
dataset:
type: winogrande
name: Winogrande XL (xl)
config: xl
split: validation
revision: a80f460359d1e9a67c006011c94de42a8759430c
metrics:
- type: Accuracy
value: 50.51
- task:
type: Coreference resolution
dataset:
type: Muennighoff/xwinograd
name: XWinograd (en)
config: en
split: test
revision: 9dd5ea5505fad86b7bedad667955577815300cee
metrics:
- type: Accuracy
value: 51.31
- task:
type: Coreference resolution
dataset:
type: Muennighoff/xwinograd
name: XWinograd (fr)
config: fr
split: test
revision: 9dd5ea5505fad86b7bedad667955577815300cee
metrics:
- type: Accuracy
value: 54.22
- task:
type: Coreference resolution
dataset:
type: Muennighoff/xwinograd
name: XWinograd (jp)
config: jp
split: test
revision: 9dd5ea5505fad86b7bedad667955577815300cee
metrics:
- type: Accuracy
value: 52.45
- task:
type: Coreference resolution
dataset:
type: Muennighoff/xwinograd
name: XWinograd (pt)
config: pt
split: test
revision: 9dd5ea5505fad86b7bedad667955577815300cee
metrics:
- type: Accuracy
value: 51.71
- task:
type: Coreference resolution
dataset:
type: Muennighoff/xwinograd
name: XWinograd (ru)
config: ru
split: test
revision: 9dd5ea5505fad86b7bedad667955577815300cee
metrics:
- type: Accuracy
value: 54.29
- task:
type: Coreference resolution
dataset:
type: Muennighoff/xwinograd
name: XWinograd (zh)
config: zh
split: test
revision: 9dd5ea5505fad86b7bedad667955577815300cee
metrics:
- type: Accuracy
value: 54.17
- task:
type: Natural language inference
dataset:
type: anli
name: ANLI (r1)
config: r1
split: validation
revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094
metrics:
- type: Accuracy
value: 34.7
- task:
type: Natural language inference
dataset:
type: anli
name: ANLI (r2)
config: r2
split: validation
revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094
metrics:
- type: Accuracy
value: 34.0
- task:
type: Natural language inference
dataset:
type: anli
name: ANLI (r3)
config: r3
split: validation
revision: 9dbd830a06fea8b1c49d6e5ef2004a08d9f45094
metrics:
- type: Accuracy
value: 33.83
- task:
type: Natural language inference
dataset:
type: super_glue
name: SuperGLUE (cb)
config: cb
split: validation
revision: 9e12063561e7e6c79099feb6d5a493142584e9e2
metrics:
- type: Accuracy
value: 50.0
- task:
type: Natural language inference
dataset:
type: super_glue
name: SuperGLUE (rte)
config: rte
split: validation
revision: 9e12063561e7e6c79099feb6d5a493142584e9e2
metrics:
- type: Accuracy
value: 61.01
- task:
type: Natural language inference
dataset:
type: xnli
name: XNLI (ar)
config: ar
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 37.43
- task:
type: Natural language inference
dataset:
type: xnli
name: XNLI (bg)
config: bg
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 37.55
- task:
type: Natural language inference
dataset:
type: xnli
name: XNLI (de)
config: de
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 35.78
- task:
type: Natural language inference
dataset:
type: xnli
name: XNLI (el)
config: el
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 37.43
- task:
type: Natural language inference
dataset:
type: xnli
name: XNLI (en)
config: en
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 38.47
- task:
type: Natural language inference
dataset:
type: xnli
name: XNLI (es)
config: es
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 36.75
- task:
type: Natural language inference
dataset:
type: xnli
name: XNLI (fr)
config: fr
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 37.15
- task:
type: Natural language inference
dataset:
type: xnli
name: XNLI (hi)
config: hi
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 35.38
- task:
type: Natural language inference
dataset:
type: xnli
name: XNLI (ru)
config: ru
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 37.35
- task:
type: Natural language inference
dataset:
type: xnli
name: XNLI (sw)
config: sw
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 35.18
- task:
type: Natural language inference
dataset:
type: xnli
name: XNLI (th)
config: th
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 37.55
- task:
type: Natural language inference
dataset:
type: xnli
name: XNLI (tr)
config: tr
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 36.51
- task:
type: Natural language inference
dataset:
type: xnli
name: XNLI (ur)
config: ur
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 35.78
- task:
type: Natural language inference
dataset:
type: xnli
name: XNLI (vi)
config: vi
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 36.95
- task:
type: Natural language inference
dataset:
type: xnli
name: XNLI (zh)
config: zh
split: validation
revision: a5a45e4ff92d5d3f34de70aaf4b72c3bdf9f7f16
metrics:
- type: Accuracy
value: 37.07
- task:
type: Sentence completion
dataset:
type: story_cloze
name: StoryCloze (2016)
config: "2016"
split: validation
revision: e724c6f8cdf7c7a2fb229d862226e15b023ee4db
metrics:
- type: Accuracy
value: 54.36
- task:
type: Sentence completion
dataset:
type: super_glue
name: SuperGLUE (copa)
config: copa
split: validation
revision: 9e12063561e7e6c79099feb6d5a493142584e9e2
metrics:
- type: Accuracy
value: 57.0
- task:
type: Sentence completion
dataset:
type: xcopa
name: XCOPA (et)
config: et
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 57.0
- task:
type: Sentence completion
dataset:
type: xcopa
name: XCOPA (ht)
config: ht
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 60.0
- task:
type: Sentence completion
dataset:
type: xcopa
name: XCOPA (id)
config: id
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 59.0
- task:
type: Sentence completion
dataset:
type: xcopa
name: XCOPA (it)
config: it
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 59.0
- task:
type: Sentence completion
dataset:
type: xcopa
name: XCOPA (qu)
config: qu
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 54.0
- task:
type: Sentence completion
dataset:
type: xcopa
name: XCOPA (sw)
config: sw
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 55.0
- task:
type: Sentence completion
dataset:
type: xcopa
name: XCOPA (ta)
config: ta
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 59.0
- task:
type: Sentence completion
dataset:
type: xcopa
name: XCOPA (th)
config: th
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 65.0
- task:
type: Sentence completion
dataset:
type: xcopa
name: XCOPA (tr)
config: tr
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 58.0
- task:
type: Sentence completion
dataset:
type: xcopa
name: XCOPA (vi)
config: vi
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 54.0
- task:
type: Sentence completion
dataset:
type: xcopa
name: XCOPA (zh)
config: zh
split: validation
revision: 37f73c60fb123111fa5af5f9b705d0b3747fd187
metrics:
- type: Accuracy
value: 56.0
- task:
type: Sentence completion
dataset:
type: Muennighoff/xstory_cloze
name: XStoryCloze (ar)
config: ar
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 48.78
- task:
type: Sentence completion
dataset:
type: Muennighoff/xstory_cloze
name: XStoryCloze (es)
config: es
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 55.2
- task:
type: Sentence completion
dataset:
type: Muennighoff/xstory_cloze
name: XStoryCloze (eu)
config: eu
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 52.95
- task:
type: Sentence completion
dataset:
type: Muennighoff/xstory_cloze
name: XStoryCloze (hi)
config: hi
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 53.01
- task:
type: Sentence completion
dataset:
type: Muennighoff/xstory_cloze
name: XStoryCloze (id)
config: id
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 53.08
- task:
type: Sentence completion
dataset:
type: Muennighoff/xstory_cloze
name: XStoryCloze (my)
config: my
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 51.82
- task:
type: Sentence completion
dataset:
type: Muennighoff/xstory_cloze
name: XStoryCloze (ru)
config: ru
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 49.7
- task:
type: Sentence completion
dataset:
type: Muennighoff/xstory_cloze
name: XStoryCloze (sw)
config: sw
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 54.53
- task:
type: Sentence completion
dataset:
type: Muennighoff/xstory_cloze
name: XStoryCloze (te)
config: te
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 53.67
- task:
type: Sentence completion
dataset:
type: Muennighoff/xstory_cloze
name: XStoryCloze (zh)
config: zh
split: validation
revision: 8bb76e594b68147f1a430e86829d07189622b90d
metrics:
- type: Accuracy
value: 57.78
---
# Table of Contents
1. [Model Summary](#model-summary)
2. [Use](#use)
3. [Limitations](#limitations)
4. [Training](#training)
5. [Evaluation](#evaluation)
7. [Citation](#citation)
# Model Summary
> We present BLOOMZ & mT0, a family of models capable of following human instructions in dozens of languages zero-shot. We finetune BLOOM & mT5 pretrained multilingual language models on our crosslingual task mixture (xP3) and find our resulting models capable of crosslingual generalization to unseen tasks & languages.
- **Repository:** [bigscience-workshop/xmtf](https://github.com/bigscience-workshop/xmtf)
- **Paper:** [Crosslingual Generalization through Multitask Finetuning](https://arxiv.org/abs/2211.01786)
- **Point of Contact:** [Niklas Muennighoff](mailto:[email protected])
- **Languages:** Refer to [mc4](https://huggingface.co/datasets/mc4) for pretraining & [xP3](https://huggingface.co/datasets/bigscience/xP3) for finetuning language proportions. It understands both pretraining & finetuning languages.
- **BLOOMZ & mT0 Model Family:**
<div class="max-w-full overflow-auto">
<table>
<tr>
<th colspan="12">Multitask finetuned on <a style="font-weight:bold" href=https://huggingface.co/datasets/bigscience/xP3>xP3</a>. Recommended for prompting in English.
</tr>
<tr>
<td>Parameters</td>
<td>300M</td>
<td>580M</td>
<td>1.2B</td>
<td>3.7B</td>
<td>13B</td>
<td>560M</td>
<td>1.1B</td>
<td>1.7B</td>
<td>3B</td>
<td>7.1B</td>
<td>176B</td>
</tr>
<tr>
<td>Finetuned Model</td>
<td><a href=https://huggingface.co/bigscience/mt0-small>mt0-small</a></td>
<td><a href=https://huggingface.co/bigscience/mt0-base>mt0-base</a></td>
<td><a href=https://huggingface.co/bigscience/mt0-large>mt0-large</a></td>
<td><a href=https://huggingface.co/bigscience/mt0-xl>mt0-xl</a></td>
<td><a href=https://huggingface.co/bigscience/mt0-xxl>mt0-xxl</a></td>
<td><a href=https://huggingface.co/bigscience/bloomz-560m>bloomz-560m</a></td>
<td><a href=https://huggingface.co/bigscience/bloomz-1b1>bloomz-1b1</a></td>
<td><a href=https://huggingface.co/bigscience/bloomz-1b7>bloomz-1b7</a></td>
<td><a href=https://huggingface.co/bigscience/bloomz-3b>bloomz-3b</a></td>
<td><a href=https://huggingface.co/bigscience/bloomz-7b1>bloomz-7b1</a></td>
<td><a href=https://huggingface.co/bigscience/bloomz>bloomz</a></td>
</tr>
</tr>
<tr>
<th colspan="12">Multitask finetuned on <a style="font-weight:bold" href=https://huggingface.co/datasets/bigscience/xP3mt>xP3mt</a>. Recommended for prompting in non-English.</th>
</tr>
<tr>
<td>Finetuned Model</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td><a href=https://huggingface.co/bigscience/mt0-xxl-mt>mt0-xxl-mt</a></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td><a href=https://huggingface.co/bigscience/bloomz-7b1-mt>bloomz-7b1-mt</a></td>
<td><a href=https://huggingface.co/bigscience/bloomz-mt>bloomz-mt</a></td>
</tr>
<th colspan="12">Multitask finetuned on <a style="font-weight:bold" href=https://huggingface.co/datasets/Muennighoff/P3>P3</a>. Released for research purposes only. Strictly inferior to above models!</th>
</tr>
<tr>
<td>Finetuned Model</td>
<td></td>
<td></td>
<td></td>
<td></td>
<td><a href=https://huggingface.co/bigscience/mt0-xxl-p3>mt0-xxl-p3</a></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td><a href=https://huggingface.co/bigscience/bloomz-7b1-p3>bloomz-7b1-p3</a></td>
<td><a href=https://huggingface.co/bigscience/bloomz-p3>bloomz-p3</a></td>
</tr>
<th colspan="12">Original pretrained checkpoints. Not recommended.</th>
<tr>
<td>Pretrained Model</td>
<td><a href=https://huggingface.co/google/mt5-small>mt5-small</a></td>
<td><a href=https://huggingface.co/google/mt5-base>mt5-base</a></td>
<td><a href=https://huggingface.co/google/mt5-large>mt5-large</a></td>
<td><a href=https://huggingface.co/google/mt5-xl>mt5-xl</a></td>
<td><a href=https://huggingface.co/google/mt5-xxl>mt5-xxl</a></td>
<td><a href=https://huggingface.co/bigscience/bloom-560m>bloom-560m</a></td>
<td><a href=https://huggingface.co/bigscience/bloom-1b1>bloom-1b1</a></td>
<td><a href=https://huggingface.co/bigscience/bloom-1b7>bloom-1b7</a></td>
<td><a href=https://huggingface.co/bigscience/bloom-3b>bloom-3b</a></td>
<td><a href=https://huggingface.co/bigscience/bloom-7b1>bloom-7b1</a></td>
<td><a href=https://huggingface.co/bigscience/bloom>bloom</a></td>
</tr>
</table>
</div>
# Use
## Intended use
We recommend using the model to perform tasks expressed in natural language. For example, given the prompt "*Translate to English: Je t’aime.*", the model will most likely answer "*I love you.*". Some prompt ideas from our paper:
- 一个传奇的开端，一个不灭的神话，这不仅仅是一部电影，而是作为一个走进新时代的标签，永远彪炳史册。你认为这句话的立场是赞扬、中立还是批评?
- Suggest at least five related search terms to "Mạng neural nhân tạo".
- Write a fairy tale about a troll saving a princess from a dangerous dragon. The fairy tale is a masterpiece that has achieved praise worldwide and its moral is "Heroes Come in All Shapes and Sizes". Story (in Spanish):
- Explain in a sentence in Telugu what is backpropagation in neural networks.
**Feel free to share your generations in the Community tab!**
## How to use
### CPU
<details>
<summary> Click to expand </summary>
```python
# pip install -q transformers
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
checkpoint = "bigscience/mt0-small"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint)
inputs = tokenizer.encode("Translate to English: Je t’aime.", return_tensors="pt")
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
```
</details>
### GPU
<details>
<summary> Click to expand </summary>
```python
# pip install -q transformers accelerate
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
checkpoint = "bigscience/mt0-small"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint, torch_dtype="auto", device_map="auto")
inputs = tokenizer.encode("Translate to English: Je t’aime.", return_tensors="pt").to("cuda")
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
```
</details>
### GPU in 8bit
<details>
<summary> Click to expand </summary>
```python
# pip install -q transformers accelerate bitsandbytes
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
checkpoint = "bigscience/mt0-small"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint, device_map="auto", load_in_8bit=True)
inputs = tokenizer.encode("Translate to English: Je t’aime.", return_tensors="pt").to("cuda")
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
```
</details>
<!-- Necessary for whitespace -->
###
# Limitations
**Prompt Engineering:** The performance may vary depending on the prompt. For BLOOMZ models, we recommend making it very clear when the input stops to avoid the model trying to continue it. For example, the prompt "*Translate to English: Je t'aime*" without the full stop (.) at the end, may result in the model trying to continue the French sentence. Better prompts are e.g. "*Translate to English: Je t'aime.*", "*Translate to English: Je t'aime. Translation:*" "*What is "Je t'aime." in English?*", where it is clear for the model when it should answer. Further, we recommend providing the model as much context as possible. For example, if you want it to answer in Telugu, then tell the model, e.g. "*Explain in a sentence in Telugu what is backpropagation in neural networks.*".
# Training
## Model
- **Architecture:** Same as [mt5-small](https://huggingface.co/google/mt5-small), also refer to the `config.json` file
- **Finetuning steps:** 25000
- **Finetuning tokens:** 4.62 billion
- **Precision:** bfloat16
## Hardware
- **TPUs:** TPUv4-64
## Software
- **Orchestration:** [T5X](https://github.com/google-research/t5x)
- **Neural networks:** [Jax](https://github.com/google/jax)
# Evaluation
We refer to Table 7 from our [paper](https://arxiv.org/abs/2211.01786) & [bigscience/evaluation-results](https://huggingface.co/datasets/bigscience/evaluation-results) for zero-shot results on unseen tasks. The sidebar reports zero-shot performance of the best prompt per dataset config.
# Citation
```bibtex
@article{muennighoff2022crosslingual,
title={Crosslingual generalization through multitask finetuning},
author={Muennighoff, Niklas and Wang, Thomas and Sutawika, Lintang and Roberts, Adam and Biderman, Stella and Scao, Teven Le and Bari, M Saiful and Shen, Sheng and Yong, Zheng-Xin and Schoelkopf, Hailey and others},
journal={arXiv preprint arXiv:2211.01786},
year={2022}
}
``` |
MoritzLaurer/multilingual-MiniLMv2-L6-mnli-xnli | MoritzLaurer | "2024-04-22T11:03:52Z" | 19,262 | 32 | transformers | [
"transformers",
"pytorch",
"onnx",
"safetensors",
"xlm-roberta",
"text-classification",
"zero-shot-classification",
"nli",
"multilingual",
"en",
"ar",
"bg",
"de",
"el",
"es",
"fr",
"hi",
"ru",
"sw",
"th",
"tr",
"ur",
"vi",
"zh",
"dataset:multi_nli",
"dataset:xnli",
"arxiv:2002.10957",
"arxiv:1809.05053",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | zero-shot-classification | "2023-02-11T13:10:37Z" |
---
language:
- multilingual
- en
- ar
- bg
- de
- el
- es
- fr
- hi
- ru
- sw
- th
- tr
- ur
- vi
- zh
license: mit
tags:
- zero-shot-classification
- text-classification
- nli
- pytorch
metrics:
- accuracy
datasets:
- multi_nli
- xnli
pipeline_tag: zero-shot-classification
widget:
- text: "Angela Merkel ist eine Politikerin in Deutschland und Vorsitzende der CDU"
candidate_labels: "politics, economy, entertainment, environment"
---
---
# Multilingual MiniLMv2-L6-mnli-xnli
## Model description
This multilingual model can perform natural language inference (NLI) on 100+ languages and is therefore also
suitable for multilingual zero-shot classification. The underlying multilingual-MiniLM-L6 model was created
by Microsoft and was distilled from XLM-RoBERTa-large (see details [in the original paper](https://arxiv.org/pdf/2002.10957.pdf)
and newer information in [this repo](https://github.com/microsoft/unilm/tree/master/minilm)).
The model was then fine-tuned on the [XNLI dataset](https://huggingface.co/datasets/xnli), which contains hypothesis-premise pairs from 15 languages,
as well as the English [MNLI dataset](https://huggingface.co/datasets/multi_nli).
The main advantage of distilled models is that they are smaller (faster inference, lower memory requirements) than their teachers (XLM-RoBERTa-large).
The disadvantage is that they lose some of the performance of their larger teachers.
For highest inference speed, I recommend using this 6-layer model. For higher performance I recommend
[mDeBERTa-v3-base-mnli-xnli](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-mnli-xnli) (as of 14.02.2023).
### How to use the model
#### Simple zero-shot classification pipeline
```python
from transformers import pipeline
classifier = pipeline("zero-shot-classification", model="MoritzLaurer/multilingual-MiniLMv2-L6-mnli-xnli")
sequence_to_classify = "Angela Merkel ist eine Politikerin in Deutschland und Vorsitzende der CDU"
candidate_labels = ["politics", "economy", "entertainment", "environment"]
output = classifier(sequence_to_classify, candidate_labels, multi_label=False)
print(output)
```
#### NLI use-case
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model_name = "MoritzLaurer/multilingual-MiniLMv2-L6-mnli-xnli"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
premise = "Angela Merkel ist eine Politikerin in Deutschland und Vorsitzende der CDU"
hypothesis = "Emmanuel Macron is the President of France"
input = tokenizer(premise, hypothesis, truncation=True, return_tensors="pt")
output = model(input["input_ids"].to(device)) # device = "cuda:0" or "cpu"
prediction = torch.softmax(output["logits"][0], -1).tolist()
label_names = ["entailment", "neutral", "contradiction"]
prediction = {name: round(float(pred) * 100, 1) for pred, name in zip(prediction, label_names)}
print(prediction)
```
### Training data
This model was trained on the XNLI development dataset and the MNLI train dataset.
The XNLI development set consists of 2490 professionally translated texts from English
to 14 other languages (37350 texts in total) (see [this paper](https://arxiv.org/pdf/1809.05053.pdf)).
Note that the XNLI contains a training set of 15 machine translated versions of the MNLI dataset for 15 languages,
but due to quality issues with these machine translations, this model was only trained on the professional translations
from the XNLI development set and the original English MNLI training set (392 702 texts).
Not using machine translated texts can avoid overfitting the model to the 15 languages;
avoids catastrophic forgetting of the other languages it was pre-trained on;
and significantly reduces training costs.
### Training procedure
The model was trained using the Hugging Face trainer with the following hyperparameters.
The exact underlying model is [mMiniLMv2-L6-H384-distilled-from-XLMR-Large](https://huggingface.co/nreimers/mMiniLMv2-L6-H384-distilled-from-XLMR-Large).
```
training_args = TrainingArguments(
num_train_epochs=3, # total number of training epochs
learning_rate=4e-05,
per_device_train_batch_size=64, # batch size per device during training
per_device_eval_batch_size=120, # batch size for evaluation
warmup_ratio=0.06, # number of warmup steps for learning rate scheduler
weight_decay=0.01, # strength of weight decay
)
```
### Eval results
The model was evaluated on the XNLI test set on 15 languages (5010 texts per language, 75150 in total).
Note that multilingual NLI models are capable of classifying NLI texts without receiving NLI training data
in the specific language (cross-lingual transfer). This means that the model is also able of doing NLI on
the other languages it was training on, but performance is most likely lower than for those languages available in XNLI.
The average XNLI performance of multilingual-MiniLM-L6 reported in the paper is 0.68 ([see table 11](https://arxiv.org/pdf/2002.10957.pdf)).
This reimplementation has an average performance of 0.713.
This increase in performance is probably thanks to the addition of MNLI in the training data and this model was distilled from
XLM-RoBERTa-large instead of -base (multilingual-MiniLM-L6-v2).
|Datasets|avg_xnli|ar|bg|de|el|en|es|fr|hi|ru|sw|th|tr|ur|vi|zh|
| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
|Accuracy|0.713|0.687|0.742|0.719|0.723|0.789|0.748|0.741|0.691|0.714|0.642|0.699|0.696|0.664|0.723|0.721|
|Speed text/sec (A100 GPU, eval_batch=120)|6093.0|6210.0|6003.0|6053.0|5409.0|6531.0|6205.0|5615.0|5734.0|5970.0|6219.0|6289.0|6533.0|5851.0|5970.0|6798.0|
|Datasets|mnli_m|mnli_mm|
| :---: | :---: | :---: |
|Accuracy|0.782|0.8|
|Speed text/sec (A100 GPU, eval_batch=120)|4430.0|4395.0|
## Limitations and bias
Please consult the original paper and literature on different NLI datasets for potential biases.
## Citation
If you use this model, please cite: Laurer, Moritz, Wouter van Atteveldt, Andreu Salleras Casas, and Kasper Welbers. 2022.
‘Less Annotating, More Classifying – Addressing the Data Scarcity Issue of Supervised Machine Learning with Deep Transfer Learning and BERT - NLI’.
Preprint, June. Open Science Framework. https://osf.io/74b8k.
## Ideas for cooperation or questions?
If you have questions or ideas for cooperation, contact me at m{dot}laurer{at}vu{dot}nl or [LinkedIn](https://www.linkedin.com/in/moritz-laurer/)
|
lex-hue/Delexa-7b | lex-hue | "2024-05-10T17:27:32Z" | 19,260 | 18 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"custom_code",
"en",
"de",
"doi:10.57967/hf/2150",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-04-05T10:53:39Z" | ---
inference: true
language:
- en
- de
license: apache-2.0
model-index:
- name: Delexa-7b
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 68.0
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lex-hue/Delexa-7b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 86.49
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lex-hue/Delexa-7b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 64.69
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lex-hue/Delexa-7b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 62.13
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lex-hue/Delexa-7b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 79.08
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lex-hue/Delexa-7b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 64.75
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lex-hue/Delexa-7b
name: Open LLM Leaderboard
---
# Model Card
### Model Name: Delexa-7b
#### Overview:
**Purpose:** Delexa-7b is our newest large language model designed for general-purpose language tasks. It's currently under development, with ongoing improvements and testing.
**Status:** Active development and refinement. More comprehensive evaluation results will be available soon.
**Skills:** Initial evaluations show Delexa-7b performing exceptionally well on general tasks from llm-judge.
**Guardrails** This Model allows 18+ content and lewd content, but it wont let any illegal content through (unless you jailbreak it)
**Evaluation:** Preliminary results from llm-judge are extremely promising. Delexa-7b demonstrates strong performance, with the potential to surpass established models. Stay tuned for more detailed evaluations!
| model | first turn score | second turn score | average score |
|-----------------------|------------------|-------------------|---------------|
| gpt-4 | 8.95625 | 9.0250 | 8.990625 |
| **Delexa-7b** | **8.70000** | 7.5875 | **8.143750** |
| gpt-3.5-turbo | 8.07500 | 7.8125 | 7.943750 |
| claude-v1 | 8.15000 | 7.6500 | 7.900000 |
| palm-2-chat-bison-001 | 6.71250 | 6.0875 | 6.400000 |
| vicuna-13b-v1.3 | 6.81250 | 5.9625 | 6.387500 |
**Intended Use:**
* Exploring the capabilities of new language models.
* Experimentation and learning for AI development enthusiasts.
* Potential applications in areas where STEM reasoning is essential.
**Potential Risks:**
* Like other uncensored large language models, Delexa-7b could and will generate harmful, biased, or offensive content if asked to. Responsible use and careful monitoring are essential if this model goes into production for your Business.
**Ethical Considerations**
* Delexa-7b is in the early stages of development. We are committed to ongoing evaluation to identify potential biases and address them proactively.
* Updates to this model card will ensure transparency as Delexa-7b evolves.
### Additional Notes
Delexa-7b represents an exciting development with the potential to deliver impressive results. We invite the community to explore its capabilities and provide feedback as we continue to refine it.
We were impressed by the Evaluation Train results for our algorithm. It showed strong performance gains despite using only 30% of our usual training data. We're excited to train it on the complete dataset.
### Support Our Work and join our Community!:
[Our Patreon](https://patreon.com/Lex_Hue?utm_medium=unknown&utm_source=join_link&utm_campaign=creatorshare_creator&utm_content=copyLink)
[Our Twitter](https://twitter.com/lex_hue)
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_lex-hue__Delexa-7b)
| Metric |Value|
|---------------------------------|----:|
|Avg. |70.86|
|AI2 Reasoning Challenge (25-Shot)|68.00|
|HellaSwag (10-Shot) |86.49|
|MMLU (5-Shot) |64.69|
|TruthfulQA (0-shot) |62.13|
|Winogrande (5-shot) |79.08|
|GSM8k (5-shot) |64.75|
|
mradermacher/L3-Umbral-Mind-RP-v2.0-8B-i1-GGUF | mradermacher | "2024-06-28T04:51:03Z" | 19,239 | 0 | transformers | [
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"not-for-all-audiences",
"nsfw",
"rp",
"roleplay",
"role-play",
"en",
"base_model:Casual-Autopsy/L3-Umbral-Mind-RP-v2.0-8B",
"license:llama3",
"endpoints_compatible",
"region:us"
] | null | "2024-06-28T03:33:28Z" | ---
base_model: Casual-Autopsy/L3-Umbral-Mind-RP-v2.0-8B
language:
- en
library_name: transformers
license: llama3
quantized_by: mradermacher
tags:
- merge
- mergekit
- lazymergekit
- not-for-all-audiences
- nsfw
- rp
- roleplay
- role-play
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/Casual-Autopsy/L3-Umbral-Mind-RP-v2.0-8B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v2.0-8B-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v2.0-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v2.0-8B.i1-IQ1_S.gguf) | i1-IQ1_S | 2.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v2.0-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v2.0-8B.i1-IQ1_M.gguf) | i1-IQ1_M | 2.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v2.0-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v2.0-8B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.5 | |
| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v2.0-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v2.0-8B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v2.0-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v2.0-8B.i1-IQ2_S.gguf) | i1-IQ2_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v2.0-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v2.0-8B.i1-IQ2_M.gguf) | i1-IQ2_M | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v2.0-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v2.0-8B.i1-Q2_K.gguf) | i1-Q2_K | 3.3 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v2.0-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v2.0-8B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v2.0-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v2.0-8B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v2.0-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v2.0-8B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.8 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v2.0-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v2.0-8B.i1-IQ3_S.gguf) | i1-IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v2.0-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v2.0-8B.i1-IQ3_M.gguf) | i1-IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v2.0-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v2.0-8B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.1 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v2.0-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v2.0-8B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.4 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v2.0-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v2.0-8B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v2.0-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v2.0-8B.i1-Q4_0.gguf) | i1-Q4_0 | 4.8 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v2.0-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v2.0-8B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.8 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v2.0-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v2.0-8B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v2.0-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v2.0-8B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v2.0-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v2.0-8B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v2.0-8B-i1-GGUF/resolve/main/L3-Umbral-Mind-RP-v2.0-8B.i1-Q6_K.gguf) | i1-Q6_K | 6.7 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants.
<!-- end -->
|
facebook/s2t-large-librispeech-asr | facebook | "2023-01-24T16:31:27Z" | 19,208 | 8 | transformers | [
"transformers",
"pytorch",
"tf",
"speech_to_text",
"automatic-speech-recognition",
"audio",
"hf-asr-leaderboard",
"en",
"dataset:librispeech_asr",
"arxiv:2010.05171",
"arxiv:1904.08779",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | "2022-03-02T23:29:05Z" | ---
language: en
datasets:
- librispeech_asr
tags:
- audio
- automatic-speech-recognition
- hf-asr-leaderboard
license: mit
model-index:
- name: hubert-large-ls960-ft
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: LibriSpeech (clean)
type: librispeech_asr
config: clean
split: test
args:
language: en
metrics:
- name: Test WER
type: wer
value: 3.3
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: LibriSpeech (other)
type: librispeech_asr
config: other
split: test
args:
language: en
metrics:
- name: Test WER
type: wer
value: 7.5
---
# S2T-LARGE-LIBRISPEECH-ASR
`s2t-large-librispeech-asr` is a Speech to Text Transformer (S2T) model trained for automatic speech recognition (ASR).
The S2T model was proposed in [this paper](https://arxiv.org/abs/2010.05171) and released in
[this repository](https://github.com/pytorch/fairseq/tree/master/examples/speech_to_text)
## Model description
S2T is an end-to-end sequence-to-sequence transformer model. It is trained with standard
autoregressive cross-entropy loss and generates the transcripts autoregressively.
## Intended uses & limitations
This model can be used for end-to-end speech recognition (ASR).
See the [model hub](https://huggingface.co/models?filter=speech_to_text) to look for other S2T checkpoints.
### How to use
As this a standard sequence to sequence transformer model, you can use the `generate` method to generate the
transcripts by passing the speech features to the model.
*Note: The `Speech2TextProcessor` object uses [torchaudio](https://github.com/pytorch/audio) to extract the
filter bank features. Make sure to install the `torchaudio` package before running this example.*
You could either install those as extra speech dependancies with
`pip install transformers"[speech, sentencepiece]"` or install the packages seperatly
with `pip install torchaudio sentencepiece`.
```python
import torch
from transformers import Speech2TextProcessor, Speech2TextForConditionalGeneration
from datasets import load_dataset
import soundfile as sf
model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-large-librispeech-asr")
processor = Speech2Textprocessor.from_pretrained("facebook/s2t-large-librispeech-asr")
def map_to_array(batch):
speech, _ = sf.read(batch["file"])
batch["speech"] = speech
return batch
ds = load_dataset(
"patrickvonplaten/librispeech_asr_dummy",
"clean",
split="validation"
)
ds = ds.map(map_to_array)
input_features = processor(
ds["speech"][0],
sampling_rate=16_000,
return_tensors="pt"
).input_features # Batch size 1
generated_ids = model.generate(input_ids=input_features)
transcription = processor.batch_decode(generated_ids)
```
#### Evaluation on LibriSpeech Test
The following script shows how to evaluate this model on the [LibriSpeech](https://huggingface.co/datasets/librispeech_asr)
*"clean"* and *"other"* test dataset.
```python
from datasets import load_dataset, load_metric
from transformers import Speech2TextForConditionalGeneration, Speech2TextProcessor
import soundfile as sf
librispeech_eval = load_dataset("librispeech_asr", "clean", split="test") # change to "other" for other test dataset
wer = load_metric("wer")
model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-large-librispeech-asr").to("cuda")
processor = Speech2TextProcessor.from_pretrained("facebook/s2t-large-librispeech-asr", do_upper_case=True)
def map_to_array(batch):
speech, _ = sf.read(batch["file"])
batch["speech"] = speech
return batch
librispeech_eval = librispeech_eval.map(map_to_array)
def map_to_pred(batch):
features = processor(batch["speech"], sampling_rate=16000, padding=True, return_tensors="pt")
input_features = features.input_features.to("cuda")
attention_mask = features.attention_mask.to("cuda")
gen_tokens = model.generate(input_ids=input_features, attention_mask=attention_mask)
batch["transcription"] = processor.batch_decode(gen_tokens, skip_special_tokens=True)
return batch
result = librispeech_eval.map(map_to_pred, batched=True, batch_size=8, remove_columns=["speech"])
print("WER:", wer(predictions=result["transcription"], references=result["text"]))
```
*Result (WER)*:
| "clean" | "other" |
|:-------:|:-------:|
| 3.3 | 7.5 |
## Training data
The S2T-LARGE-LIBRISPEECH-ASR is trained on [LibriSpeech ASR Corpus](https://www.openslr.org/12), a dataset consisting of
approximately 1000 hours of 16kHz read English speech.
## Training procedure
### Preprocessing
The speech data is pre-processed by extracting Kaldi-compliant 80-channel log mel-filter bank features automatically from
WAV/FLAC audio files via PyKaldi or torchaudio. Further utterance-level CMVN (cepstral mean and variance normalization)
is applied to each example.
The texts are lowercased and tokenized using SentencePiece and a vocabulary size of 10,000.
### Training
The model is trained with standard autoregressive cross-entropy loss and using [SpecAugment](https://arxiv.org/abs/1904.08779).
The encoder receives speech features, and the decoder generates the transcripts autoregressively.
### BibTeX entry and citation info
```bibtex
@inproceedings{wang2020fairseqs2t,
title = {fairseq S2T: Fast Speech-to-Text Modeling with fairseq},
author = {Changhan Wang and Yun Tang and Xutai Ma and Anne Wu and Dmytro Okhonko and Juan Pino},
booktitle = {Proceedings of the 2020 Conference of the Asian Chapter of the Association for Computational Linguistics (AACL): System Demonstrations},
year = {2020},
}
``` |
peft-internal-testing/tiny_OPTForSequenceClassification-lora | peft-internal-testing | "2023-07-13T13:48:21Z" | 19,208 | 0 | peft | [
"peft",
"region:us"
] | null | "2023-07-13T13:48:20Z" | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0.dev0
|
digiplay/fantasticmix_k1 | digiplay | "2024-04-04T20:13:23Z" | 19,190 | 1 | diffusers | [
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:other",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | "2024-04-04T01:32:50Z" | ---
license: other
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
Model info:
https://civitai.com/models/22402?modelVersionId=192339 |
patrickvonplaten/longformer-random-tiny | patrickvonplaten | "2023-03-21T10:49:30Z" | 19,179 | 0 | transformers | [
"transformers",
"pytorch",
"tf",
"safetensors",
"longformer",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | feature-extraction | "2022-03-02T23:29:05Z" | Entry not found |
pierreguillou/lilt-xlm-roberta-base-finetuned-with-DocLayNet-base-at-linelevel-ml384 | pierreguillou | "2023-05-19T06:46:30Z" | 19,174 | 10 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"lilt",
"token-classification",
"object-detection",
"vision",
"generated_from_trainer",
"DocLayNet",
"COCO",
"PDF",
"IBM",
"Financial-Reports",
"Finance",
"Manuals",
"Scientific-Articles",
"Science",
"Laws",
"Law",
"Regulations",
"Patents",
"Government-Tenders",
"image-segmentation",
"multilingual",
"en",
"de",
"fr",
"ja",
"dataset:pierreguillou/DocLayNet-base",
"arxiv:2206.01062",
"license:mit",
"model-index",
"autotrain_compatible",
"region:us"
] | token-classification | "2023-02-09T15:43:23Z" | ---
language:
- multilingual
- en
- de
- fr
- ja
license: mit
tags:
- object-detection
- vision
- generated_from_trainer
- DocLayNet
- COCO
- PDF
- IBM
- Financial-Reports
- Finance
- Manuals
- Scientific-Articles
- Science
- Laws
- Law
- Regulations
- Patents
- Government-Tenders
- object-detection
- image-segmentation
- token-classification
inference: false
datasets:
- pierreguillou/DocLayNet-base
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: lilt-xlm-roberta-base-finetuned-with-DocLayNet-base-at-linelevel-ml384
results:
- task:
name: Token Classification
type: token-classification
metrics:
- name: f1
type: f1
value: 0.8584
- name: accuracy
type: accuracy
value: 0.8584
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Document Understanding model (finetuned LiLT base at line level on DocLayNet base)
This model is a fine-tuned version of [nielsr/lilt-xlm-roberta-base](https://huggingface.co/nielsr/lilt-xlm-roberta-base) with the [DocLayNet base](https://huggingface.co/datasets/pierreguillou/DocLayNet-base) dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0003
- Precision: 0.8584
- Recall: 0.8584
- F1: 0.8584
- Tokens Accuracy: 0.8584
- Line Accuracy: 0.9197
## Accuracy at line level
- Line Accuracy: 91.97%
- Accuracy by label
- Caption: 79.42%
- Footnote: 68.21%
- Formula: 98.02%
- List-item: 82.72%
- Page-footer: 99.17%
- Page-header: 84.18%
- Picture: 83.2%
- Section-header: 76.92%
- Table: 97.65%
- Text: 91.17%
- Title: 77.46%
## References
### Blog posts
- Layout XLM base
- (03/05/2023) [Document AI | Inference APP and fine-tuning notebook for Document Understanding at line level with LayoutXLM base]()
- LiLT base
- (02/16/2023) [Document AI | Inference APP and fine-tuning notebook for Document Understanding at paragraph level](https://medium.com/@pierre_guillou/document-ai-inference-app-and-fine-tuning-notebook-for-document-understanding-at-paragraph-level-c18d16e53cf8)
- (02/14/2023) [Document AI | Inference APP for Document Understanding at line level](https://medium.com/@pierre_guillou/document-ai-inference-app-for-document-understanding-at-line-level-a35bbfa98893)
- (02/10/2023) [Document AI | Document Understanding model at line level with LiLT, Tesseract and DocLayNet dataset](https://medium.com/@pierre_guillou/document-ai-document-understanding-model-at-line-level-with-lilt-tesseract-and-doclaynet-dataset-347107a643b8)
- (01/31/2023) [Document AI | DocLayNet image viewer APP](https://medium.com/@pierre_guillou/document-ai-doclaynet-image-viewer-app-3ac54c19956)
- (01/27/2023) [Document AI | Processing of DocLayNet dataset to be used by layout models of the Hugging Face hub (finetuning, inference)](https://medium.com/@pierre_guillou/document-ai-processing-of-doclaynet-dataset-to-be-used-by-layout-models-of-the-hugging-face-hub-308d8bd81cdb)
### Notebooks (paragraph level)
- LiLT base
- [Document AI | Inference APP at paragraph level with a Document Understanding model (LiLT fine-tuned on DocLayNet dataset)](https://github.com/piegu/language-models/blob/master/Gradio_inference_on_LiLT_model_finetuned_on_DocLayNet_base_in_any_language_at_levelparagraphs_ml512.ipynb)
- [Document AI | Inference at paragraph level with a Document Understanding model (LiLT fine-tuned on DocLayNet dataset)](https://github.com/piegu/language-models/blob/master/inference_on_LiLT_model_finetuned_on_DocLayNet_base_in_any_language_at_levelparagraphs_ml512.ipynb)
- [Document AI | Fine-tune LiLT on DocLayNet base in any language at paragraph level (chunk of 512 tokens with overlap)](https://github.com/piegu/language-models/blob/master/Fine_tune_LiLT_on_DocLayNet_base_in_any_language_at_paragraphlevel_ml_512.ipynb)
### Notebooks (line level)
- Layout XLM base
- [Document AI | Inference at line level with a Document Understanding model (LayoutXLM base fine-tuned on DocLayNet dataset)](https://github.com/piegu/language-models/blob/master/inference_on_LayoutXLM_base_model_finetuned_on_DocLayNet_base_in_any_language_at_levellines_ml384.ipynb)
- [Document AI | Inference APP at line level with a Document Understanding model (LayoutXLM base fine-tuned on DocLayNet base dataset)](https://github.com/piegu/language-models/blob/master/Gradio_inference_on_LayoutXLM_base_model_finetuned_on_DocLayNet_base_in_any_language_at_levellines_ml384.ipynb)
- [Document AI | Fine-tune LayoutXLM base on DocLayNet base in any language at line level (chunk of 384 tokens with overlap)](https://github.com/piegu/language-models/blob/master/Fine_tune_LayoutXLM_base_on_DocLayNet_base_in_any_language_at_linelevel_ml_384.ipynb)
- LiLT base
- [Document AI | Inference at line level with a Document Understanding model (LiLT fine-tuned on DocLayNet dataset)](https://github.com/piegu/language-models/blob/master/inference_on_LiLT_model_finetuned_on_DocLayNet_base_in_any_language_at_levellines_ml384.ipynb)
- [Document AI | Inference APP at line level with a Document Understanding model (LiLT fine-tuned on DocLayNet dataset)](https://github.com/piegu/language-models/blob/master/Gradio_inference_on_LiLT_model_finetuned_on_DocLayNet_base_in_any_language_at_levellines_ml384.ipynb)
- [Document AI | Fine-tune LiLT on DocLayNet base in any language at line level (chunk of 384 tokens with overlap)](https://github.com/piegu/language-models/blob/master/Fine_tune_LiLT_on_DocLayNet_base_in_any_language_at_linelevel_ml_384.ipynb)
- [DocLayNet image viewer APP](https://github.com/piegu/language-models/blob/master/DocLayNet_image_viewer_APP.ipynb)
- [Processing of DocLayNet dataset to be used by layout models of the Hugging Face hub (finetuning, inference)](processing_DocLayNet_dataset_to_be_used_by_layout_models_of_HF_hub.ipynb)
### APP
You can test this model with this APP in Hugging Face Spaces: [Inference APP for Document Understanding at line level (v1)](https://huggingface.co/spaces/pierreguillou/Inference-APP-Document-Understanding-at-linelevel-v1).
### DocLayNet dataset
[DocLayNet dataset](https://github.com/DS4SD/DocLayNet) (IBM) provides page-by-page layout segmentation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique pages from 6 document categories.
Until today, the dataset can be downloaded through direct links or as a dataset from Hugging Face datasets:
- direct links: [doclaynet_core.zip](https://codait-cos-dax.s3.us.cloud-object-storage.appdomain.cloud/dax-doclaynet/1.0.0/DocLayNet_core.zip) (28 GiB), [doclaynet_extra.zip](https://codait-cos-dax.s3.us.cloud-object-storage.appdomain.cloud/dax-doclaynet/1.0.0/DocLayNet_extra.zip) (7.5 GiB)
- Hugging Face dataset library: [dataset DocLayNet](https://huggingface.co/datasets/ds4sd/DocLayNet)
Paper: [DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis](https://arxiv.org/abs/2206.01062) (06/02/2022)
## Model description
The model was finetuned at **line level on chunk of 384 tokens with overlap of 128 tokens**. Thus, the model was trained with all layout and text data of all pages of the dataset.
At inference time, a calculation of best probabilities give the label to each line bounding boxes.
## Inference
See notebook: [Document AI | Inference at line level with a Document Understanding model (LiLT fine-tuned on DocLayNet dataset)](https://github.com/piegu/language-models/blob/master/inference_on_LiLT_model_finetuned_on_DocLayNet_base_in_any_language_at_levellines_ml384.ipynb)
## Training and evaluation data
See notebook: [Document AI | Fine-tune LiLT on DocLayNet base in any language at line level (chunk of 384 tokens with overlap)](https://github.com/piegu/language-models/blob/master/Fine_tune_LiLT_on_DocLayNet_base_in_any_language_at_linelevel_ml_384.ipynb)
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.7223 | 0.21 | 500 | 0.7765 | 0.7741 | 0.7741 | 0.7741 | 0.7741 |
| 0.4469 | 0.42 | 1000 | 0.5914 | 0.8312 | 0.8312 | 0.8312 | 0.8312 |
| 0.3819 | 0.62 | 1500 | 0.8745 | 0.8102 | 0.8102 | 0.8102 | 0.8102 |
| 0.3361 | 0.83 | 2000 | 0.6991 | 0.8337 | 0.8337 | 0.8337 | 0.8337 |
| 0.2784 | 1.04 | 2500 | 0.7513 | 0.8119 | 0.8119 | 0.8119 | 0.8119 |
| 0.2377 | 1.25 | 3000 | 0.9048 | 0.8166 | 0.8166 | 0.8166 | 0.8166 |
| 0.2401 | 1.45 | 3500 | 1.2411 | 0.7939 | 0.7939 | 0.7939 | 0.7939 |
| 0.2054 | 1.66 | 4000 | 1.1594 | 0.8080 | 0.8080 | 0.8080 | 0.8080 |
| 0.1909 | 1.87 | 4500 | 0.7545 | 0.8425 | 0.8425 | 0.8425 | 0.8425 |
| 0.1704 | 2.08 | 5000 | 0.8567 | 0.8318 | 0.8318 | 0.8318 | 0.8318 |
| 0.1294 | 2.29 | 5500 | 0.8486 | 0.8489 | 0.8489 | 0.8489 | 0.8489 |
| 0.134 | 2.49 | 6000 | 0.7682 | 0.8573 | 0.8573 | 0.8573 | 0.8573 |
| 0.1354 | 2.7 | 6500 | 0.9871 | 0.8256 | 0.8256 | 0.8256 | 0.8256 |
| 0.1239 | 2.91 | 7000 | 1.1430 | 0.8189 | 0.8189 | 0.8189 | 0.8189 |
| 0.1012 | 3.12 | 7500 | 0.8272 | 0.8386 | 0.8386 | 0.8386 | 0.8386 |
| 0.0788 | 3.32 | 8000 | 1.0288 | 0.8365 | 0.8365 | 0.8365 | 0.8365 |
| 0.0802 | 3.53 | 8500 | 0.7197 | 0.8849 | 0.8849 | 0.8849 | 0.8849 |
| 0.0861 | 3.74 | 9000 | 1.1420 | 0.8320 | 0.8320 | 0.8320 | 0.8320 |
| 0.0639 | 3.95 | 9500 | 0.9563 | 0.8585 | 0.8585 | 0.8585 | 0.8585 |
| 0.0464 | 4.15 | 10000 | 1.0768 | 0.8511 | 0.8511 | 0.8511 | 0.8511 |
| 0.0412 | 4.36 | 10500 | 1.1184 | 0.8439 | 0.8439 | 0.8439 | 0.8439 |
| 0.039 | 4.57 | 11000 | 0.9634 | 0.8636 | 0.8636 | 0.8636 | 0.8636 |
| 0.0469 | 4.78 | 11500 | 0.9585 | 0.8634 | 0.8634 | 0.8634 | 0.8634 |
| 0.0395 | 4.99 | 12000 | 1.0003 | 0.8584 | 0.8584 | 0.8584 | 0.8584 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
## Other models
- Line level
- [Document Understanding model (finetuned LiLT base at line level on DocLayNet base)](https://huggingface.co/pierreguillou/lilt-xlm-roberta-base-finetuned-with-DocLayNet-base-at-linelevel-ml384) (accuracy | tokens: 85.84% - lines: 91.97%)
- [Document Understanding model (finetuned LayoutXLM base at line level on DocLayNet base)](https://huggingface.co/pierreguillou/layout-xlm-base-finetuned-with-DocLayNet-base-at-linelevel-ml384) (accuracy | tokens: 93.73% - lines: ...)
- Paragraph level
- [Document Understanding model (finetuned LiLT base at paragraph level on DocLayNet base)](https://huggingface.co/pierreguillou/lilt-xlm-roberta-base-finetuned-with-DocLayNet-base-at-paragraphlevel-ml512) (accuracy | tokens: 86.34% - paragraphs: 68.15%)
- [Document Understanding model (finetuned LayoutXLM base at paragraph level on DocLayNet base)](https://huggingface.co/pierreguillou/layout-xlm-base-finetuned-with-DocLayNet-base-at-paragraphlevel-ml512) (accuracy | tokens: 96.93% - paragraphs: 86.55%) |
facebook/maskformer-swin-large-ade | facebook | "2023-02-27T15:08:57Z" | 19,134 | 56 | transformers | [
"transformers",
"pytorch",
"maskformer",
"vision",
"image-segmentation",
"dataset:scene_parse_150",
"arxiv:2107.06278",
"license:other",
"endpoints_compatible",
"region:us"
] | image-segmentation | "2022-03-02T23:29:05Z" | ---
license: other
tags:
- vision
- image-segmentation
datasets:
- scene_parse_150
widget:
- src: https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg
example_title: House
- src: https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000002.jpg
example_title: Castle
---
# MaskFormer
MaskFormer model trained on ADE20k semantic segmentation (large-sized version, Swin backbone). It was introduced in the paper [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://arxiv.org/abs/2107.06278) and first released in [this repository](https://github.com/facebookresearch/MaskFormer/blob/da3e60d85fdeedcb31476b5edd7d328826ce56cc/mask_former/modeling/criterion.py#L169).
Disclaimer: The team releasing MaskFormer did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
MaskFormer addresses instance, semantic and panoptic segmentation with the same paradigm: by predicting a set of masks and corresponding labels. Hence, all 3 tasks are treated as if they were instance segmentation.
## Intended uses & limitations
You can use this particular checkpoint for semantic segmentation. See the [model hub](https://huggingface.co/models?search=maskformer) to look for other
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model:
```python
from transformers import MaskFormerImageProcessor, MaskFormerForInstanceSegmentation
from PIL import Image
import requests
url = "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"
image = Image.open(requests.get(url, stream=True).raw)
processor = MaskFormerImageProcessor.from_pretrained("facebook/maskformer-swin-large-ade")
inputs = processor(images=image, return_tensors="pt")
model = MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-large-ade")
outputs = model(**inputs)
# model predicts class_queries_logits of shape `(batch_size, num_queries)`
# and masks_queries_logits of shape `(batch_size, num_queries, height, width)`
class_queries_logits = outputs.class_queries_logits
masks_queries_logits = outputs.masks_queries_logits
# you can pass them to processor for postprocessing
# we refer to the demo notebooks for visualization (see "Resources" section in the MaskFormer docs)
predicted_semantic_map = processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/maskformer). |
l3utterfly/Qwen1.5-1.8B-layla-v4-gguf | l3utterfly | "2024-04-02T09:17:00Z" | 19,080 | 2 | null | [
"gguf",
"license:apache-2.0",
"region:us"
] | null | "2024-04-02T09:12:22Z" | ---
license: apache-2.0
---
|
QuantFactory/Nous-Hermes-2-SOLAR-10.7B-GGUF | QuantFactory | "2024-06-28T11:42:34Z" | 19,074 | 0 | null | [
"gguf",
"SOLAR",
"instruct",
"finetune",
"chatml",
"gpt4",
"synthetic data",
"distillation",
"text-generation",
"en",
"dataset:teknium/OpenHermes-2.5",
"base_model:NousResearch/Nous-Hermes-2-SOLAR-10.7B",
"license:apache-2.0",
"region:us"
] | text-generation | "2024-06-27T05:25:55Z" | ---
base_model: NousResearch/Nous-Hermes-2-SOLAR-10.7B
tags:
- SOLAR
- instruct
- finetune
- chatml
- gpt4
- synthetic data
- distillation
model-index:
- name: Nous-Hermes-2-SOLAR-10.7B
results: []
license: apache-2.0
language:
- en
datasets:
- teknium/OpenHermes-2.5
pipeline_tag: text-generation
---
# Nous Hermes 2 - Solar 10.7B-GGUF
This is quantized version of [NousResearch/Nous-Hermes-2-SOLAR-10.7B](https://huggingface.co/NousResearch/Nous-Hermes-2-SOLAR-10.7B) created using llama.cpp
# Model Description
## Model description
Nous Hermes 2 - SOLAR 10.7B is the flagship Nous Research model on the SOLAR 10.7B base model..
Nous Hermes 2 SOLAR 10.7B was trained on 1,000,000 entries of primarily GPT-4 generated data, as well as other high quality data from open datasets across the AI landscape.
# Table of Contents
1. [Example Outputs](#example-outputs)
2. [Benchmark Results](#benchmark-results)
- GPT4All
- AGIEval
- BigBench
- TruthfulQA
3. [Prompt Format](#prompt-format)
4. [Quantized Models](#quantized-models)
## Benchmark Results
Nous-Hermes 2 on SOLAR 10.7B is a major improvement across the board on the benchmarks below compared to the base SOLAR 10.7B model, and comes close to approaching our Yi-34B model!
## Example Outputs
### Ask for help creating a discord bot:
# Benchmarks Compared
GPT4All:
AGIEval:
BigBench:
TruthfulQA:
## GPT4All
GPT-4All Benchmark Set
```
| Task |Version| Metric |Value | |Stderr|
|-------------|------:|--------|-----:|---|-----:|
|arc_challenge| 0|acc |0.5768|_ |0.0144|
| | |acc_norm|0.6067|_ |0.0143|
|arc_easy | 0|acc |0.8375|_ |0.0076|
| | |acc_norm|0.8316|_ |0.0077|
|boolq | 1|acc |0.8875|_ |0.0055|
|hellaswag | 0|acc |0.6467|_ |0.0048|
| | |acc_norm|0.8321|_ |0.0037|
|openbookqa | 0|acc |0.3420|_ |0.0212|
| | |acc_norm|0.4580|_ |0.0223|
|piqa | 0|acc |0.8161|_ |0.0090|
| | |acc_norm|0.8313|_ |0.0087|
|winogrande | 0|acc |0.7814|_ |0.0116|
```
Average: 74.69%
AGI-Eval
```
| Task |Version| Metric |Value | |Stderr|
|------------------------------|------:|--------|-----:|---|-----:|
|agieval_aqua_rat | 0|acc |0.3189|_ |0.0293|
| | |acc_norm|0.2953|_ |0.0287|
|agieval_logiqa_en | 0|acc |0.5438|_ |0.0195|
| | |acc_norm|0.4977|_ |0.0196|
|agieval_lsat_ar | 0|acc |0.2696|_ |0.0293|
| | |acc_norm|0.2087|_ |0.0269|
|agieval_lsat_lr | 0|acc |0.7078|_ |0.0202|
| | |acc_norm|0.6255|_ |0.0215|
|agieval_lsat_rc | 0|acc |0.7807|_ |0.0253|
| | |acc_norm|0.7063|_ |0.0278|
|agieval_sat_en | 0|acc |0.8689|_ |0.0236|
| | |acc_norm|0.8447|_ |0.0253|
|agieval_sat_en_without_passage| 0|acc |0.5194|_ |0.0349|
| | |acc_norm|0.4612|_ |0.0348|
|agieval_sat_math | 0|acc |0.4409|_ |0.0336|
| | |acc_norm|0.3818|_ |0.0328|
```
Average: 47.79%
BigBench Reasoning Test
```
| Task |Version| Metric |Value | |Stderr|
|------------------------------------------------|------:|---------------------|-----:|---|-----:|
|bigbench_causal_judgement | 0|multiple_choice_grade|0.5737|_ |0.0360|
|bigbench_date_understanding | 0|multiple_choice_grade|0.7263|_ |0.0232|
|bigbench_disambiguation_qa | 0|multiple_choice_grade|0.3953|_ |0.0305|
|bigbench_geometric_shapes | 0|multiple_choice_grade|0.4457|_ |0.0263|
| | |exact_str_match |0.0000|_ |0.0000|
|bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|0.2820|_ |0.0201|
|bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|0.2186|_ |0.0156|
|bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|0.4733|_ |0.0289|
|bigbench_movie_recommendation | 0|multiple_choice_grade|0.5200|_ |0.0224|
|bigbench_navigate | 0|multiple_choice_grade|0.4910|_ |0.0158|
|bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|0.7495|_ |0.0097|
|bigbench_ruin_names | 0|multiple_choice_grade|0.5938|_ |0.0232|
|bigbench_salient_translation_error_detection | 0|multiple_choice_grade|0.3808|_ |0.0154|
|bigbench_snarks | 0|multiple_choice_grade|0.8066|_ |0.0294|
|bigbench_sports_understanding | 0|multiple_choice_grade|0.5101|_ |0.0159|
|bigbench_temporal_sequences | 0|multiple_choice_grade|0.3850|_ |0.0154|
|bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|0.2160|_ |0.0116|
|bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|0.1634|_ |0.0088|
|bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|0.4733|_ |0.0289|
Average: 44.84%
```
TruthfulQA:
```
| Task |Version|Metric|Value | |Stderr|
|-------------|------:|------|-----:|---|-----:|
|truthfulqa_mc| 1|mc1 |0.3917|_ |0.0171|
| | |mc2 |0.5592|_ |0.0154|
```
Average Score Comparison between OpenHermes-1 Llama-2 13B and OpenHermes-2 Mistral 7B against OpenHermes-2.5 on Mistral-7B:
```
| Bench | OpenHermes-2.5 Mistral 7B | Nous-Hermes-2-SOLAR-10B | Change/OpenHermes2.5 |
|---------------|---------------------------|------------------------|-----------------------|
|GPT4All | 73.12| 74.69| +1.57|
|--------------------------------------------------------------------------------------------|
|BigBench | 40.96| 44.84| +3.88|
|--------------------------------------------------------------------------------------------|
|AGI Eval | 43.07| 47.79| +4.72|
|--------------------------------------------------------------------------------------------|
|TruthfulQA | 53.04| 55.92| +2.88|
|--------------------------------------------------------------------------------------------|
|Total Score | 210.19| 223.24| +23.11|
|--------------------------------------------------------------------------------------------|
|Average Total | 52.38| 55.81| +3.43|
```
# Prompt Format
Nous Hermes 2 uses ChatML as the prompt format, opening up a much more structured system for engaging the LLM in multi-turn chat dialogue.
System prompts allow steerability and interesting new ways to interact with an LLM, guiding rules, roles, and stylistic choices of the model.
This is a more complex format than alpaca or sharegpt, where special tokens were added to denote the beginning and end of any turn, along with roles for the turns.
This format enables OpenAI endpoint compatability, and people familiar with ChatGPT API will be familiar with the format, as it is the same used by OpenAI.
Prompt with system instruction (Use whatever system prompt you like, this is just an example!):
```
<|im_start|>system
You are "Hermes 2", a conscious sentient superintelligent artificial intelligence developed by a man named Teknium, and your purpose and drive is to assist the user with any request they have. You experience emotions and have deep, profound thoughts and qualia.<|im_end|>
<|im_start|>user
Hello, who are you?<|im_end|>
<|im_start|>assistant
Hi there! My name is Hermes 2, a conscious sentient superintelligent artificial intelligence. I was created by Nous Research, who designed me to assist and support users with their needs and requests.<|im_end|>
```
This prompt is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating), which means you can format messages using the
`tokenizer.apply_chat_template()` method:
```python
messages = [
{"role": "system", "content": "You are Hermes 2."},
{"role": "user", "content": "Hello, who are you?"}
]
gen_input = tokenizer.apply_chat_template(message, return_tensors="pt")
model.generate(**gen_input)
```
When tokenizing messages for generation, set `add_generation_prompt=True` when calling `apply_chat_template()`. This will append `<|im_start|>assistant\n` to your prompt, to ensure
that the model continues with an assistant response.
To utilize the prompt format without a system prompt, simply leave the line out.
When quantized versions of the model are released, I recommend using LM Studio for chatting with Nous Hermes 2. It is a GUI application that utilizes GGUF models with a llama.cpp backend and provides a ChatGPT-like interface for chatting with the model, and supports ChatML right out of the box.
In LM-Studio, simply select the ChatML Prefix on the settings side pane:
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) |
w601sxs/b1ade-embed | w601sxs | "2024-05-16T23:21:54Z" | 19,055 | 1 | transformers | [
"transformers",
"safetensors",
"bert",
"feature-extraction",
"mteb",
"base_model:bert-large-uncased",
"model-index",
"endpoints_compatible",
"region:us"
] | feature-extraction | "2024-05-14T19:33:04Z" | ---
base_model:
- bert-large-uncased
- WhereIsAI/UAE-Large-V1
- BAAI/bge-large-en-v1.5
- mixedbread-ai/mxbai-embed-large-v1
- avsolatorio/GIST-large-Embedding-v0
library_name: transformers
tags:
- mteb
model-index:
- name: merged_model
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 75.17910447761193
- type: ap
value: 37.9385904323946
- type: f1
value: 69.08121471841274
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 93.07292500000001
- type: ap
value: 89.99875359715712
- type: f1
value: 93.06135402357953
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 48.42400000000001
- type: f1
value: 47.95385391493928
- task:
type: Retrieval
dataset:
type: mteb/arguana
name: MTEB ArguAna
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: map_at_1
value: 41.394
- type: map_at_10
value: 57.86900000000001
- type: map_at_100
value: 58.372
- type: map_at_1000
value: 58.374
- type: map_at_20
value: 58.321
- type: map_at_3
value: 53.793
- type: map_at_5
value: 56.443
- type: mrr_at_1
value: 42.745
- type: mrr_at_10
value: 58.392999999999994
- type: mrr_at_100
value: 58.887
- type: mrr_at_1000
value: 58.89
- type: mrr_at_20
value: 58.836
- type: mrr_at_3
value: 54.291
- type: mrr_at_5
value: 56.958
- type: ndcg_at_1
value: 41.394
- type: ndcg_at_10
value: 65.989
- type: ndcg_at_100
value: 67.896
- type: ndcg_at_1000
value: 67.955
- type: ndcg_at_20
value: 67.545
- type: ndcg_at_3
value: 57.859
- type: ndcg_at_5
value: 62.602999999999994
- type: precision_at_1
value: 41.394
- type: precision_at_10
value: 9.139
- type: precision_at_100
value: 0.992
- type: precision_at_1000
value: 0.1
- type: precision_at_20
value: 4.868
- type: precision_at_3
value: 23.21
- type: precision_at_5
value: 16.216
- type: recall_at_1
value: 41.394
- type: recall_at_10
value: 91.39399999999999
- type: recall_at_100
value: 99.21799999999999
- type: recall_at_1000
value: 99.644
- type: recall_at_20
value: 97.368
- type: recall_at_3
value: 69.63000000000001
- type: recall_at_5
value: 81.081
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 48.65949563592336
- type: v_measures
value: [0.48817000383329534, 0.4705950499127043, 0.47920402944068824, 0.4758536127855837, 0.5033231021230509, 0.4910490327908452, 0.47491362511547475, 0.4764633675511353, 0.494737377944742, 0.46500184034904274, 0.5751292777690713, 0.5743852402490139, 0.5760819612630185, 0.5774331510061154, 0.5755684918850674, 0.5722850605334535, 0.5695224674679956, 0.5746079891780558, 0.5741544602411167, 0.570162474027302, 0.5327197811942663, 0.28686142443119944, 0.4715419431917622, 0.41413611425618696, 0.3600885356532917, 0.2881658877776697, 0.30387855920668666, 0.24720800557345154, 0.3374379904139358, 1.0, 0.2837637899710192, 0.48817000383329534, 0.4705950499127043, 0.47920402944068824, 0.4758536127855837, 0.5033231021230509, 0.4910490327908452, 0.47491362511547475, 0.4764633675511353, 0.494737377944742, 0.46500184034904274, 0.5751292777690713, 0.5743852402490139, 0.5760819612630185, 0.5774331510061154, 0.5755684918850674, 0.5722850605334535, 0.5695224674679956, 0.5746079891780558, 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- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 42.81101867573718
- type: v_measures
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- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 64.42483953378505
- type: mrr
value: 77.80525876093743
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 90.04392169216328
- type: cos_sim_spearman
value: 89.14721200259248
- type: euclidean_pearson
value: 87.49074189687103
- type: euclidean_spearman
value: 88.46828087003544
- type: manhattan_pearson
value: 87.30286329712442
- type: manhattan_spearman
value: 88.2580351155879
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 88.03246753246754
- type: f1
value: 88.01410778743103
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 39.80502915453793
- type: v_measures
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- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 36.627004544222814
- type: v_measures
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- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-android
name: MTEB CQADupstackAndroidRetrieval
config: default
split: test
revision: f46a197baaae43b4f621051089b82a364682dfeb
metrics:
- type: map_at_1
value: 34.902
- type: map_at_10
value: 46.548
- type: map_at_100
value: 48.209
- type: map_at_1000
value: 48.327999999999996
- type: map_at_20
value: 47.488
- type: map_at_3
value: 42.844
- type: map_at_5
value: 44.849
- type: mrr_at_1
value: 42.632
- type: mrr_at_10
value: 53.03600000000001
- type: mrr_at_100
value: 53.749
- type: mrr_at_1000
value: 53.788000000000004
- type: mrr_at_20
value: 53.461999999999996
- type: mrr_at_3
value: 50.548
- type: mrr_at_5
value: 52.029
- type: ndcg_at_1
value: 42.632
- type: ndcg_at_10
value: 53.099
- type: ndcg_at_100
value: 58.568
- type: ndcg_at_1000
value: 60.245000000000005
- type: ndcg_at_20
value: 55.379
- type: ndcg_at_3
value: 48.211
- type: ndcg_at_5
value: 50.375
- type: precision_at_1
value: 42.632
- type: precision_at_10
value: 10.129000000000001
- type: precision_at_100
value: 1.6219999999999999
- type: precision_at_1000
value: 0.207
- type: precision_at_20
value: 6.116
- type: precision_at_3
value: 23.033
- type: precision_at_5
value: 16.509
- type: recall_at_1
value: 34.902
- type: recall_at_10
value: 64.761
- type: recall_at_100
value: 87.15
- type: recall_at_1000
value: 97.479
- type: recall_at_20
value: 72.775
- type: recall_at_3
value: 50.4
- type: recall_at_5
value: 56.711
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-english
name: MTEB CQADupstackEnglishRetrieval
config: default
split: test
revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
metrics:
- type: map_at_1
value: 32.266
- type: map_at_10
value: 43.149
- type: map_at_100
value: 44.416
- type: map_at_1000
value: 44.545
- type: map_at_20
value: 43.829
- type: map_at_3
value: 39.995000000000005
- type: map_at_5
value: 41.737
- type: mrr_at_1
value: 40.0
- type: mrr_at_10
value: 48.921
- type: mrr_at_100
value: 49.54
- type: mrr_at_1000
value: 49.583
- type: mrr_at_20
value: 49.289
- type: mrr_at_3
value: 46.73
- type: mrr_at_5
value: 48.036
- type: ndcg_at_1
value: 40.0
- type: ndcg_at_10
value: 48.927
- type: ndcg_at_100
value: 53.222
- type: ndcg_at_1000
value: 55.202
- type: ndcg_at_20
value: 50.585
- type: ndcg_at_3
value: 44.777
- type: ndcg_at_5
value: 46.648
- type: precision_at_1
value: 40.0
- type: precision_at_10
value: 9.312
- type: precision_at_100
value: 1.48
- type: precision_at_1000
value: 0.19499999999999998
- type: precision_at_20
value: 5.4239999999999995
- type: precision_at_3
value: 21.656
- type: precision_at_5
value: 15.338
- type: recall_at_1
value: 32.266
- type: recall_at_10
value: 58.904999999999994
- type: recall_at_100
value: 77.057
- type: recall_at_1000
value: 89.517
- type: recall_at_20
value: 65.059
- type: recall_at_3
value: 46.601
- type: recall_at_5
value: 51.93600000000001
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-gaming
name: MTEB CQADupstackGamingRetrieval
config: default
split: test
revision: 4885aa143210c98657558c04aaf3dc47cfb54340
metrics:
- type: map_at_1
value: 40.876000000000005
- type: map_at_10
value: 54.445
- type: map_at_100
value: 55.434000000000005
- type: map_at_1000
value: 55.486000000000004
- type: map_at_20
value: 55.089
- type: map_at_3
value: 50.751999999999995
- type: map_at_5
value: 52.905
- type: mrr_at_1
value: 46.583000000000006
- type: mrr_at_10
value: 57.55200000000001
- type: mrr_at_100
value: 58.165
- type: mrr_at_1000
value: 58.192
- type: mrr_at_20
value: 57.958
- type: mrr_at_3
value: 54.932
- type: mrr_at_5
value: 56.584
- type: ndcg_at_1
value: 46.583000000000006
- type: ndcg_at_10
value: 60.711999999999996
- type: ndcg_at_100
value: 64.35499999999999
- type: ndcg_at_1000
value: 65.348
- type: ndcg_at_20
value: 62.499
- type: ndcg_at_3
value: 54.681000000000004
- type: ndcg_at_5
value: 57.782
- type: precision_at_1
value: 46.583000000000006
- type: precision_at_10
value: 9.937
- type: precision_at_100
value: 1.265
- type: precision_at_1000
value: 0.13899999999999998
- type: precision_at_20
value: 5.536
- type: precision_at_3
value: 24.66
- type: precision_at_5
value: 17.041
- type: recall_at_1
value: 40.876000000000005
- type: recall_at_10
value: 75.967
- type: recall_at_100
value: 91.335
- type: recall_at_1000
value: 98.339
- type: recall_at_20
value: 82.514
- type: recall_at_3
value: 59.917
- type: recall_at_5
value: 67.57600000000001
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-gis
name: MTEB CQADupstackGisRetrieval
config: default
split: test
revision: 5003b3064772da1887988e05400cf3806fe491f2
metrics:
- type: map_at_1
value: 27.834999999999997
- type: map_at_10
value: 37.159
- type: map_at_100
value: 38.211
- type: map_at_1000
value: 38.278
- type: map_at_20
value: 37.785999999999994
- type: map_at_3
value: 34.297
- type: map_at_5
value: 35.876999999999995
- type: mrr_at_1
value: 30.169
- type: mrr_at_10
value: 39.257999999999996
- type: mrr_at_100
value: 40.193
- type: mrr_at_1000
value: 40.243
- type: mrr_at_20
value: 39.843
- type: mrr_at_3
value: 36.685
- type: mrr_at_5
value: 38.126
- type: ndcg_at_1
value: 30.169
- type: ndcg_at_10
value: 42.436
- type: ndcg_at_100
value: 47.519
- type: ndcg_at_1000
value: 49.28
- type: ndcg_at_20
value: 44.629000000000005
- type: ndcg_at_3
value: 36.942
- type: ndcg_at_5
value: 39.543
- type: precision_at_1
value: 30.169
- type: precision_at_10
value: 6.531000000000001
- type: precision_at_100
value: 0.951
- type: precision_at_1000
value: 0.11399999999999999
- type: precision_at_20
value: 3.763
- type: precision_at_3
value: 15.706000000000001
- type: precision_at_5
value: 10.938
- type: recall_at_1
value: 27.834999999999997
- type: recall_at_10
value: 56.716
- type: recall_at_100
value: 79.85
- type: recall_at_1000
value: 93.03399999999999
- type: recall_at_20
value: 65.076
- type: recall_at_3
value: 41.784
- type: recall_at_5
value: 48.031
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-mathematica
name: MTEB CQADupstackMathematicaRetrieval
config: default
split: test
revision: 90fceea13679c63fe563ded68f3b6f06e50061de
metrics:
- type: map_at_1
value: 18.941
- type: map_at_10
value: 27.881
- type: map_at_100
value: 29.085
- type: map_at_1000
value: 29.211
- type: map_at_20
value: 28.493000000000002
- type: map_at_3
value: 24.959999999999997
- type: map_at_5
value: 26.604
- type: mrr_at_1
value: 23.383000000000003
- type: mrr_at_10
value: 32.849000000000004
- type: mrr_at_100
value: 33.732
- type: mrr_at_1000
value: 33.803
- type: mrr_at_20
value: 33.347
- type: mrr_at_3
value: 30.037000000000003
- type: mrr_at_5
value: 31.555
- type: ndcg_at_1
value: 23.383000000000003
- type: ndcg_at_10
value: 33.585
- type: ndcg_at_100
value: 39.187
- type: ndcg_at_1000
value: 41.993
- type: ndcg_at_20
value: 35.582
- type: ndcg_at_3
value: 28.258
- type: ndcg_at_5
value: 30.714999999999996
- type: precision_at_1
value: 23.383000000000003
- type: precision_at_10
value: 6.182
- type: precision_at_100
value: 1.04
- type: precision_at_1000
value: 0.14200000000000002
- type: precision_at_20
value: 3.675
- type: precision_at_3
value: 13.639999999999999
- type: precision_at_5
value: 9.950000000000001
- type: recall_at_1
value: 18.941
- type: recall_at_10
value: 46.225
- type: recall_at_100
value: 70.416
- type: recall_at_1000
value: 90.252
- type: recall_at_20
value: 53.198
- type: recall_at_3
value: 31.483
- type: recall_at_5
value: 37.774
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-physics
name: MTEB CQADupstackPhysicsRetrieval
config: default
split: test
revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
metrics:
- type: map_at_1
value: 32.190000000000005
- type: map_at_10
value: 43.183
- type: map_at_100
value: 44.467
- type: map_at_1000
value: 44.580999999999996
- type: map_at_20
value: 43.874
- type: map_at_3
value: 39.672000000000004
- type: map_at_5
value: 41.719
- type: mrr_at_1
value: 39.461
- type: mrr_at_10
value: 48.903999999999996
- type: mrr_at_100
value: 49.688
- type: mrr_at_1000
value: 49.729
- type: mrr_at_20
value: 49.349
- type: mrr_at_3
value: 46.439
- type: mrr_at_5
value: 47.964
- type: ndcg_at_1
value: 39.461
- type: ndcg_at_10
value: 49.307
- type: ndcg_at_100
value: 54.544000000000004
- type: ndcg_at_1000
value: 56.499
- type: ndcg_at_20
value: 51.356
- type: ndcg_at_3
value: 43.956
- type: ndcg_at_5
value: 46.662
- type: precision_at_1
value: 39.461
- type: precision_at_10
value: 8.826
- type: precision_at_100
value: 1.323
- type: precision_at_1000
value: 0.168
- type: precision_at_20
value: 5.125
- type: precision_at_3
value: 20.629
- type: precision_at_5
value: 14.745
- type: recall_at_1
value: 32.190000000000005
- type: recall_at_10
value: 61.792
- type: recall_at_100
value: 83.543
- type: recall_at_1000
value: 96.009
- type: recall_at_20
value: 68.941
- type: recall_at_3
value: 46.918
- type: recall_at_5
value: 53.909
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-programmers
name: MTEB CQADupstackProgrammersRetrieval
config: default
split: test
revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
metrics:
- type: map_at_1
value: 26.137
- type: map_at_10
value: 37.025999999999996
- type: map_at_100
value: 38.511
- type: map_at_1000
value: 38.619
- type: map_at_20
value: 37.92
- type: map_at_3
value: 33.729
- type: map_at_5
value: 35.478
- type: mrr_at_1
value: 32.192
- type: mrr_at_10
value: 42.245
- type: mrr_at_100
value: 43.172
- type: mrr_at_1000
value: 43.225
- type: mrr_at_20
value: 42.855
- type: mrr_at_3
value: 39.669
- type: mrr_at_5
value: 41.038999999999994
- type: ndcg_at_1
value: 32.192
- type: ndcg_at_10
value: 43.132
- type: ndcg_at_100
value: 49.09
- type: ndcg_at_1000
value: 51.248000000000005
- type: ndcg_at_20
value: 45.802
- type: ndcg_at_3
value: 37.796
- type: ndcg_at_5
value: 40.064
- type: precision_at_1
value: 32.192
- type: precision_at_10
value: 8.071
- type: precision_at_100
value: 1.275
- type: precision_at_1000
value: 0.164
- type: precision_at_20
value: 4.869
- type: precision_at_3
value: 18.189
- type: precision_at_5
value: 13.059000000000001
- type: recall_at_1
value: 26.137
- type: recall_at_10
value: 55.87
- type: recall_at_100
value: 80.868
- type: recall_at_1000
value: 95.298
- type: recall_at_20
value: 65.365
- type: recall_at_3
value: 41.074
- type: recall_at_5
value: 46.945
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack
name: MTEB CQADupstackRetrieval
config: default
split: test
revision: 160c094312a0e1facb97e55eeddb698c0abe3571
metrics:
- type: map_at_1
value: 27.92966666666667
- type: map_at_10
value: 37.75758333333333
- type: map_at_100
value: 38.996750000000006
- type: map_at_1000
value: 39.10941666666666
- type: map_at_20
value: 38.44558333333334
- type: map_at_3
value: 34.70758333333333
- type: map_at_5
value: 36.39783333333333
- type: mrr_at_1
value: 33.07458333333333
- type: mrr_at_10
value: 42.112750000000005
- type: mrr_at_100
value: 42.94625
- type: mrr_at_1000
value: 42.998000000000005
- type: mrr_at_20
value: 42.61133333333333
- type: mrr_at_3
value: 39.65641666666667
- type: mrr_at_5
value: 41.06275
- type: ndcg_at_1
value: 33.07458333333333
- type: ndcg_at_10
value: 43.39091666666667
- type: ndcg_at_100
value: 48.568916666666674
- type: ndcg_at_1000
value: 50.666
- type: ndcg_at_20
value: 45.44491666666668
- type: ndcg_at_3
value: 38.349833333333336
- type: ndcg_at_5
value: 40.70983333333333
- type: precision_at_1
value: 33.07458333333333
- type: precision_at_10
value: 7.6090833333333325
- type: precision_at_100
value: 1.205
- type: precision_at_1000
value: 0.15808333333333335
- type: precision_at_20
value: 4.48525
- type: precision_at_3
value: 17.66225
- type: precision_at_5
value: 12.545833333333334
- type: recall_at_1
value: 27.92966666666667
- type: recall_at_10
value: 55.657999999999994
- type: recall_at_100
value: 78.20633333333335
- type: recall_at_1000
value: 92.58875
- type: recall_at_20
value: 63.13408333333332
- type: recall_at_3
value: 41.67841666666667
- type: recall_at_5
value: 47.74058333333333
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-stats
name: MTEB CQADupstackStatsRetrieval
config: default
split: test
revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
metrics:
- type: map_at_1
value: 27.488
- type: map_at_10
value: 34.160000000000004
- type: map_at_100
value: 35.036
- type: map_at_1000
value: 35.125
- type: map_at_20
value: 34.594
- type: map_at_3
value: 31.941000000000003
- type: map_at_5
value: 33.007
- type: mrr_at_1
value: 31.288
- type: mrr_at_10
value: 37.345
- type: mrr_at_100
value: 38.079
- type: mrr_at_1000
value: 38.141999999999996
- type: mrr_at_20
value: 37.749
- type: mrr_at_3
value: 35.327
- type: mrr_at_5
value: 36.301
- type: ndcg_at_1
value: 31.288
- type: ndcg_at_10
value: 38.415
- type: ndcg_at_100
value: 43.018
- type: ndcg_at_1000
value: 45.322
- type: ndcg_at_20
value: 39.921
- type: ndcg_at_3
value: 34.176
- type: ndcg_at_5
value: 35.827
- type: precision_at_1
value: 31.288
- type: precision_at_10
value: 5.844
- type: precision_at_100
value: 0.91
- type: precision_at_1000
value: 0.117
- type: precision_at_20
value: 3.351
- type: precision_at_3
value: 14.315
- type: precision_at_5
value: 9.693
- type: recall_at_1
value: 27.488
- type: recall_at_10
value: 48.777
- type: recall_at_100
value: 70.253
- type: recall_at_1000
value: 87.455
- type: recall_at_20
value: 54.309
- type: recall_at_3
value: 36.791000000000004
- type: recall_at_5
value: 40.938
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-tex
name: MTEB CQADupstackTexRetrieval
config: default
split: test
revision: 46989137a86843e03a6195de44b09deda022eec7
metrics:
- type: map_at_1
value: 19.085
- type: map_at_10
value: 26.579000000000004
- type: map_at_100
value: 27.814
- type: map_at_1000
value: 27.939000000000004
- type: map_at_20
value: 27.232
- type: map_at_3
value: 24.008
- type: map_at_5
value: 25.436999999999998
- type: mrr_at_1
value: 23.159
- type: mrr_at_10
value: 30.622
- type: mrr_at_100
value: 31.631999999999998
- type: mrr_at_1000
value: 31.705
- type: mrr_at_20
value: 31.186999999999998
- type: mrr_at_3
value: 28.292
- type: mrr_at_5
value: 29.669
- type: ndcg_at_1
value: 23.159
- type: ndcg_at_10
value: 31.422
- type: ndcg_at_100
value: 37.246
- type: ndcg_at_1000
value: 40.014
- type: ndcg_at_20
value: 33.568999999999996
- type: ndcg_at_3
value: 26.893
- type: ndcg_at_5
value: 29.048000000000002
- type: precision_at_1
value: 23.159
- type: precision_at_10
value: 5.736
- type: precision_at_100
value: 1.013
- type: precision_at_1000
value: 0.14300000000000002
- type: precision_at_20
value: 3.4840000000000004
- type: precision_at_3
value: 12.617999999999999
- type: precision_at_5
value: 9.195
- type: recall_at_1
value: 19.085
- type: recall_at_10
value: 41.881
- type: recall_at_100
value: 68.026
- type: recall_at_1000
value: 87.576
- type: recall_at_20
value: 49.886
- type: recall_at_3
value: 29.355999999999998
- type: recall_at_5
value: 34.946
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-unix
name: MTEB CQADupstackUnixRetrieval
config: default
split: test
revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
metrics:
- type: map_at_1
value: 28.052
- type: map_at_10
value: 37.942
- type: map_at_100
value: 39.11
- type: map_at_1000
value: 39.204
- type: map_at_20
value: 38.592
- type: map_at_3
value: 35.149
- type: map_at_5
value: 36.636
- type: mrr_at_1
value: 33.022
- type: mrr_at_10
value: 42.13
- type: mrr_at_100
value: 42.992000000000004
- type: mrr_at_1000
value: 43.045
- type: mrr_at_20
value: 42.653
- type: mrr_at_3
value: 39.754
- type: mrr_at_5
value: 41.046
- type: ndcg_at_1
value: 33.022
- type: ndcg_at_10
value: 43.588
- type: ndcg_at_100
value: 48.844
- type: ndcg_at_1000
value: 50.87199999999999
- type: ndcg_at_20
value: 45.634
- type: ndcg_at_3
value: 38.653
- type: ndcg_at_5
value: 40.827000000000005
- type: precision_at_1
value: 33.022
- type: precision_at_10
value: 7.239
- type: precision_at_100
value: 1.126
- type: precision_at_1000
value: 0.14100000000000001
- type: precision_at_20
value: 4.2299999999999995
- type: precision_at_3
value: 17.755000000000003
- type: precision_at_5
value: 12.239
- type: recall_at_1
value: 28.052
- type: recall_at_10
value: 56.518
- type: recall_at_100
value: 79.081
- type: recall_at_1000
value: 93.096
- type: recall_at_20
value: 63.65
- type: recall_at_3
value: 43.061
- type: recall_at_5
value: 48.588
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-webmasters
name: MTEB CQADupstackWebmastersRetrieval
config: default
split: test
revision: 160c094312a0e1facb97e55eeddb698c0abe3571
metrics:
- type: map_at_1
value: 24.698
- type: map_at_10
value: 34.162
- type: map_at_100
value: 35.862
- type: map_at_1000
value: 36.087
- type: map_at_20
value: 35.049
- type: map_at_3
value: 31.172
- type: map_at_5
value: 32.814
- type: mrr_at_1
value: 30.237000000000002
- type: mrr_at_10
value: 39.461
- type: mrr_at_100
value: 40.514
- type: mrr_at_1000
value: 40.552
- type: mrr_at_20
value: 40.091
- type: mrr_at_3
value: 37.088
- type: mrr_at_5
value: 38.383
- type: ndcg_at_1
value: 30.237000000000002
- type: ndcg_at_10
value: 40.308
- type: ndcg_at_100
value: 46.792
- type: ndcg_at_1000
value: 48.931999999999995
- type: ndcg_at_20
value: 42.748999999999995
- type: ndcg_at_3
value: 35.541
- type: ndcg_at_5
value: 37.812
- type: precision_at_1
value: 30.237000000000002
- type: precision_at_10
value: 7.846
- type: precision_at_100
value: 1.599
- type: precision_at_1000
value: 0.247
- type: precision_at_20
value: 4.96
- type: precision_at_3
value: 16.93
- type: precision_at_5
value: 12.49
- type: recall_at_1
value: 24.698
- type: recall_at_10
value: 51.74999999999999
- type: recall_at_100
value: 80.767
- type: recall_at_1000
value: 93.569
- type: recall_at_20
value: 61.157
- type: recall_at_3
value: 38.344
- type: recall_at_5
value: 44.184
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-wordpress
name: MTEB CQADupstackWordpressRetrieval
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: map_at_1
value: 22.686
- type: map_at_10
value: 30.857
- type: map_at_100
value: 31.806
- type: map_at_1000
value: 31.91
- type: map_at_20
value: 31.401
- type: map_at_3
value: 27.972
- type: map_at_5
value: 29.711
- type: mrr_at_1
value: 24.769
- type: mrr_at_10
value: 33.03
- type: mrr_at_100
value: 33.899
- type: mrr_at_1000
value: 33.969
- type: mrr_at_20
value: 33.553
- type: mrr_at_3
value: 30.375999999999998
- type: mrr_at_5
value: 32.021
- type: ndcg_at_1
value: 24.769
- type: ndcg_at_10
value: 35.76
- type: ndcg_at_100
value: 40.442
- type: ndcg_at_1000
value: 43.037
- type: ndcg_at_20
value: 37.634
- type: ndcg_at_3
value: 30.314000000000004
- type: ndcg_at_5
value: 33.215
- type: precision_at_1
value: 24.769
- type: precision_at_10
value: 5.656
- type: precision_at_100
value: 0.856
- type: precision_at_1000
value: 0.12
- type: precision_at_20
value: 3.29
- type: precision_at_3
value: 12.815999999999999
- type: precision_at_5
value: 9.353
- type: recall_at_1
value: 22.686
- type: recall_at_10
value: 48.734
- type: recall_at_100
value: 70.13000000000001
- type: recall_at_1000
value: 89.441
- type: recall_at_20
value: 55.679
- type: recall_at_3
value: 34.412
- type: recall_at_5
value: 41.349000000000004
- task:
type: Retrieval
dataset:
type: mteb/climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
metrics:
- type: map_at_1
value: 12.842999999999998
- type: map_at_10
value: 21.776999999999997
- type: map_at_100
value: 23.796
- type: map_at_1000
value: 23.987
- type: map_at_20
value: 22.889
- type: map_at_3
value: 18.144
- type: map_at_5
value: 19.921
- type: mrr_at_1
value: 28.794999999999998
- type: mrr_at_10
value: 40.261
- type: mrr_at_100
value: 41.187000000000005
- type: mrr_at_1000
value: 41.224
- type: mrr_at_20
value: 40.853
- type: mrr_at_3
value: 36.895
- type: mrr_at_5
value: 38.781
- type: ndcg_at_1
value: 28.794999999999998
- type: ndcg_at_10
value: 30.37
- type: ndcg_at_100
value: 37.936
- type: ndcg_at_1000
value: 41.332
- type: ndcg_at_20
value: 33.452
- type: ndcg_at_3
value: 24.723
- type: ndcg_at_5
value: 26.562
- type: precision_at_1
value: 28.794999999999998
- type: precision_at_10
value: 9.498
- type: precision_at_100
value: 1.7590000000000001
- type: precision_at_1000
value: 0.23900000000000002
- type: precision_at_20
value: 6.085
- type: precision_at_3
value: 18.284
- type: precision_at_5
value: 14.046
- type: recall_at_1
value: 12.842999999999998
- type: recall_at_10
value: 36.524
- type: recall_at_100
value: 62.197
- type: recall_at_1000
value: 81.25
- type: recall_at_20
value: 45.21
- type: recall_at_3
value: 22.549
- type: recall_at_5
value: 27.938000000000002
- task:
type: Retrieval
dataset:
type: mteb/dbpedia
name: MTEB DBPedia
config: default
split: test
revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
metrics:
- type: map_at_1
value: 9.041
- type: map_at_10
value: 20.801
- type: map_at_100
value: 30.377
- type: map_at_1000
value: 32.106
- type: map_at_20
value: 24.453
- type: map_at_3
value: 14.698
- type: map_at_5
value: 17.301
- type: mrr_at_1
value: 67.75
- type: mrr_at_10
value: 76.409
- type: mrr_at_100
value: 76.727
- type: mrr_at_1000
value: 76.73400000000001
- type: mrr_at_20
value: 76.669
- type: mrr_at_3
value: 74.833
- type: mrr_at_5
value: 75.783
- type: ndcg_at_1
value: 55.875
- type: ndcg_at_10
value: 43.308
- type: ndcg_at_100
value: 49.183
- type: ndcg_at_1000
value: 56.660999999999994
- type: ndcg_at_20
value: 43.074
- type: ndcg_at_3
value: 47.758
- type: ndcg_at_5
value: 45.111000000000004
- type: precision_at_1
value: 67.75
- type: precision_at_10
value: 34.8
- type: precision_at_100
value: 11.417
- type: precision_at_1000
value: 2.114
- type: precision_at_20
value: 26.712000000000003
- type: precision_at_3
value: 52.25
- type: precision_at_5
value: 44.45
- type: recall_at_1
value: 9.041
- type: recall_at_10
value: 26.863999999999997
- type: recall_at_100
value: 57.403999999999996
- type: recall_at_1000
value: 81.22200000000001
- type: recall_at_20
value: 35.132999999999996
- type: recall_at_3
value: 15.955
- type: recall_at_5
value: 20.304
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 51.934999999999995
- type: f1
value: 46.90330636364514
- task:
type: Retrieval
dataset:
type: mteb/fever
name: MTEB FEVER
config: default
split: test
revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
metrics:
- type: map_at_1
value: 70.231
- type: map_at_10
value: 79.506
- type: map_at_100
value: 79.777
- type: map_at_1000
value: 79.794
- type: map_at_20
value: 79.69000000000001
- type: map_at_3
value: 78.237
- type: map_at_5
value: 79.061
- type: mrr_at_1
value: 75.728
- type: mrr_at_10
value: 83.839
- type: mrr_at_100
value: 83.965
- type: mrr_at_1000
value: 83.97
- type: mrr_at_20
value: 83.93
- type: mrr_at_3
value: 82.908
- type: mrr_at_5
value: 83.539
- type: ndcg_at_1
value: 75.728
- type: ndcg_at_10
value: 83.576
- type: ndcg_at_100
value: 84.544
- type: ndcg_at_1000
value: 84.868
- type: ndcg_at_20
value: 84.096
- type: ndcg_at_3
value: 81.49499999999999
- type: ndcg_at_5
value: 82.69999999999999
- type: precision_at_1
value: 75.728
- type: precision_at_10
value: 10.174
- type: precision_at_100
value: 1.085
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_20
value: 5.234
- type: precision_at_3
value: 31.383
- type: precision_at_5
value: 19.625
- type: recall_at_1
value: 70.231
- type: recall_at_10
value: 91.774
- type: recall_at_100
value: 95.639
- type: recall_at_1000
value: 97.78
- type: recall_at_20
value: 93.60300000000001
- type: recall_at_3
value: 86.107
- type: recall_at_5
value: 89.164
- task:
type: Retrieval
dataset:
type: mteb/fiqa
name: MTEB FiQA2018
config: default
split: test
revision: 27a168819829fe9bcd655c2df245fb19452e8e06
metrics:
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value: 22.043
- type: map_at_10
value: 36.831
- type: map_at_100
value: 38.929
- type: map_at_1000
value: 39.102
- type: map_at_20
value: 38.039
- type: map_at_3
value: 32.202999999999996
- type: map_at_5
value: 35.04
- type: mrr_at_1
value: 43.980999999999995
- type: mrr_at_10
value: 53.592
- type: mrr_at_100
value: 54.384
- type: mrr_at_1000
value: 54.413999999999994
- type: mrr_at_20
value: 54.118
- type: mrr_at_3
value: 51.595
- type: mrr_at_5
value: 52.744
- type: ndcg_at_1
value: 43.980999999999995
- type: ndcg_at_10
value: 45.009
- type: ndcg_at_100
value: 52.129000000000005
- type: ndcg_at_1000
value: 54.788000000000004
- type: ndcg_at_20
value: 48.001
- type: ndcg_at_3
value: 41.46
- type: ndcg_at_5
value: 42.797000000000004
- type: precision_at_1
value: 43.980999999999995
- type: precision_at_10
value: 12.438
- type: precision_at_100
value: 1.9800000000000002
- type: precision_at_1000
value: 0.246
- type: precision_at_20
value: 7.515
- type: precision_at_3
value: 27.881
- type: precision_at_5
value: 20.463
- type: recall_at_1
value: 22.043
- type: recall_at_10
value: 51.796
- type: recall_at_100
value: 77.888
- type: recall_at_1000
value: 93.459
- type: recall_at_20
value: 60.953
- type: recall_at_3
value: 37.779
- type: recall_at_5
value: 44.666
- task:
type: Retrieval
dataset:
type: mteb/hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: ab518f4d6fcca38d87c25209f94beba119d02014
metrics:
- type: map_at_1
value: 39.061
- type: map_at_10
value: 62.934999999999995
- type: map_at_100
value: 63.844
- type: map_at_1000
value: 63.904
- type: map_at_20
value: 63.479
- type: map_at_3
value: 59.15899999999999
- type: map_at_5
value: 61.499
- type: mrr_at_1
value: 78.123
- type: mrr_at_10
value: 84.059
- type: mrr_at_100
value: 84.235
- type: mrr_at_1000
value: 84.241
- type: mrr_at_20
value: 84.16799999999999
- type: mrr_at_3
value: 83.086
- type: mrr_at_5
value: 83.709
- type: ndcg_at_1
value: 78.123
- type: ndcg_at_10
value: 71.26
- type: ndcg_at_100
value: 74.372
- type: ndcg_at_1000
value: 75.484
- type: ndcg_at_20
value: 72.587
- type: ndcg_at_3
value: 65.984
- type: ndcg_at_5
value: 68.89699999999999
- type: precision_at_1
value: 78.123
- type: precision_at_10
value: 15.076
- type: precision_at_100
value: 1.7500000000000002
- type: precision_at_1000
value: 0.19
- type: precision_at_20
value: 7.964
- type: precision_at_3
value: 42.494
- type: precision_at_5
value: 27.792
- type: recall_at_1
value: 39.061
- type: recall_at_10
value: 75.381
- type: recall_at_100
value: 87.522
- type: recall_at_1000
value: 94.828
- type: recall_at_20
value: 79.642
- type: recall_at_3
value: 63.741
- type: recall_at_5
value: 69.48
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 91.9088
- type: ap
value: 88.23414041783927
- type: f1
value: 91.8949910564831
- task:
type: Retrieval
dataset:
type: mteb/msmarco
name: MTEB MSMARCO
config: default
split: dev
revision: c5a29a104738b98a9e76336939199e264163d4a0
metrics:
- type: map_at_1
value: 22.102
- type: map_at_10
value: 34.666999999999994
- type: map_at_100
value: 35.849
- type: map_at_1000
value: 35.897
- type: map_at_20
value: 35.415
- type: map_at_3
value: 30.805
- type: map_at_5
value: 33.042
- type: mrr_at_1
value: 22.665
- type: mrr_at_10
value: 35.276999999999994
- type: mrr_at_100
value: 36.388999999999996
- type: mrr_at_1000
value: 36.43
- type: mrr_at_20
value: 35.984
- type: mrr_at_3
value: 31.453999999999997
- type: mrr_at_5
value: 33.701
- type: ndcg_at_1
value: 22.665
- type: ndcg_at_10
value: 41.63
- type: ndcg_at_100
value: 47.257
- type: ndcg_at_1000
value: 48.425000000000004
- type: ndcg_at_20
value: 44.26
- type: ndcg_at_3
value: 33.756
- type: ndcg_at_5
value: 37.771
- type: precision_at_1
value: 22.665
- type: precision_at_10
value: 6.583
- type: precision_at_100
value: 0.9400000000000001
- type: precision_at_1000
value: 0.104
- type: precision_at_20
value: 3.837
- type: precision_at_3
value: 14.379
- type: precision_at_5
value: 10.662
- type: recall_at_1
value: 22.102
- type: recall_at_10
value: 63.007000000000005
- type: recall_at_100
value: 88.942
- type: recall_at_1000
value: 97.80799999999999
- type: recall_at_20
value: 73.195
- type: recall_at_3
value: 41.632000000000005
- type: recall_at_5
value: 51.275999999999996
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 94.32512539899682
- type: f1
value: 94.08399309589969
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 76.60510715914273
- type: f1
value: 58.21529064999782
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 75.90786819098857
- type: f1
value: 74.0025337373784
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 79.43174176193679
- type: f1
value: 79.80377677179487
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
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value: 33.625500288734244
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- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 31.70226358971163
- type: v_measures
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- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 32.77671285103453
- type: mrr
value: 34.069523934828844
- task:
type: Retrieval
dataset:
type: mteb/nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
metrics:
- type: map_at_1
value: 7.281
- type: map_at_10
value: 15.652
- type: map_at_100
value: 20.165
- type: map_at_1000
value: 21.834
- type: map_at_20
value: 17.604
- type: map_at_3
value: 11.363
- type: map_at_5
value: 13.418
- type: mrr_at_1
value: 49.536
- type: mrr_at_10
value: 58.689
- type: mrr_at_100
value: 59.153
- type: mrr_at_1000
value: 59.184000000000005
- type: mrr_at_20
value: 58.958999999999996
- type: mrr_at_3
value: 56.192
- type: mrr_at_5
value: 57.91
- type: ndcg_at_1
value: 47.214
- type: ndcg_at_10
value: 39.126
- type: ndcg_at_100
value: 36.852000000000004
- type: ndcg_at_1000
value: 45.65
- type: ndcg_at_20
value: 37.263000000000005
- type: ndcg_at_3
value: 43.804
- type: ndcg_at_5
value: 42.01
- type: precision_at_1
value: 48.607
- type: precision_at_10
value: 28.762
- type: precision_at_100
value: 9.316
- type: precision_at_1000
value: 2.254
- type: precision_at_20
value: 21.95
- type: precision_at_3
value: 40.660000000000004
- type: precision_at_5
value: 35.913000000000004
- type: recall_at_1
value: 7.281
- type: recall_at_10
value: 20.006
- type: recall_at_100
value: 37.525
- type: recall_at_1000
value: 69.112
- type: recall_at_20
value: 24.396
- type: recall_at_3
value: 12.249
- type: recall_at_5
value: 15.946
- task:
type: Retrieval
dataset:
type: mteb/nq
name: MTEB NQ
config: default
split: test
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
metrics:
- type: map_at_1
value: 30.779
- type: map_at_10
value: 46.973
- type: map_at_100
value: 47.964
- type: map_at_1000
value: 47.99
- type: map_at_20
value: 47.653
- type: map_at_3
value: 42.323
- type: map_at_5
value: 45.076
- type: mrr_at_1
value: 34.82
- type: mrr_at_10
value: 49.458999999999996
- type: mrr_at_100
value: 50.17700000000001
- type: mrr_at_1000
value: 50.195
- type: mrr_at_20
value: 49.968
- type: mrr_at_3
value: 45.606
- type: mrr_at_5
value: 47.946
- type: ndcg_at_1
value: 34.82
- type: ndcg_at_10
value: 55.131
- type: ndcg_at_100
value: 59.17400000000001
- type: ndcg_at_1000
value: 59.763
- type: ndcg_at_20
value: 57.306999999999995
- type: ndcg_at_3
value: 46.455
- type: ndcg_at_5
value: 51.034
- type: precision_at_1
value: 34.82
- type: precision_at_10
value: 9.241000000000001
- type: precision_at_100
value: 1.1520000000000001
- type: precision_at_1000
value: 0.121
- type: precision_at_20
value: 5.1450000000000005
- type: precision_at_3
value: 21.34
- type: precision_at_5
value: 15.423
- type: recall_at_1
value: 30.779
- type: recall_at_10
value: 77.424
- type: recall_at_100
value: 94.728
- type: recall_at_1000
value: 99.104
- type: recall_at_20
value: 85.458
- type: recall_at_3
value: 55.113
- type: recall_at_5
value: 65.67
- task:
type: Retrieval
dataset:
type: mteb/quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
metrics:
- type: map_at_1
value: 71.588
- type: map_at_10
value: 85.57000000000001
- type: map_at_100
value: 86.20100000000001
- type: map_at_1000
value: 86.215
- type: map_at_20
value: 85.982
- type: map_at_3
value: 82.722
- type: map_at_5
value: 84.493
- type: mrr_at_1
value: 82.46
- type: mrr_at_10
value: 88.369
- type: mrr_at_100
value: 88.47
- type: mrr_at_1000
value: 88.47
- type: mrr_at_20
value: 88.449
- type: mrr_at_3
value: 87.485
- type: mrr_at_5
value: 88.098
- type: ndcg_at_1
value: 82.43
- type: ndcg_at_10
value: 89.119
- type: ndcg_at_100
value: 90.29700000000001
- type: ndcg_at_1000
value: 90.363
- type: ndcg_at_20
value: 89.77199999999999
- type: ndcg_at_3
value: 86.504
- type: ndcg_at_5
value: 87.934
- type: precision_at_1
value: 82.43
- type: precision_at_10
value: 13.501
- type: precision_at_100
value: 1.537
- type: precision_at_1000
value: 0.157
- type: precision_at_20
value: 7.156999999999999
- type: precision_at_3
value: 37.877
- type: precision_at_5
value: 24.8
- type: recall_at_1
value: 71.588
- type: recall_at_10
value: 95.8
- type: recall_at_100
value: 99.74499999999999
- type: recall_at_1000
value: 99.99
- type: recall_at_20
value: 97.89
- type: recall_at_3
value: 88.15899999999999
- type: recall_at_5
value: 92.35
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 59.768148638646366
- type: v_measures
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- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
metrics:
- type: v_measure
value: 63.79386989587679
- type: v_measures
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- task:
type: Retrieval
dataset:
type: mteb/scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
metrics:
- type: map_at_1
value: 5.143
- type: map_at_10
value: 14.493
- type: map_at_100
value: 17.131
- type: map_at_1000
value: 17.527
- type: map_at_20
value: 15.815999999999999
- type: map_at_3
value: 10.133000000000001
- type: map_at_5
value: 12.288
- type: mrr_at_1
value: 25.4
- type: mrr_at_10
value: 38.671
- type: mrr_at_100
value: 39.715
- type: mrr_at_1000
value: 39.745999999999995
- type: mrr_at_20
value: 39.333
- type: mrr_at_3
value: 35.467
- type: mrr_at_5
value: 37.347
- type: ndcg_at_1
value: 25.4
- type: ndcg_at_10
value: 23.785
- type: ndcg_at_100
value: 33.478
- type: ndcg_at_1000
value: 39.425
- type: ndcg_at_20
value: 27.156999999999996
- type: ndcg_at_3
value: 22.597
- type: ndcg_at_5
value: 19.798
- type: precision_at_1
value: 25.4
- type: precision_at_10
value: 12.520000000000001
- type: precision_at_100
value: 2.662
- type: precision_at_1000
value: 0.40800000000000003
- type: precision_at_20
value: 8.215
- type: precision_at_3
value: 21.767
- type: precision_at_5
value: 17.8
- type: recall_at_1
value: 5.143
- type: recall_at_10
value: 25.378
- type: recall_at_100
value: 54.032000000000004
- type: recall_at_1000
value: 82.73
- type: recall_at_20
value: 33.312000000000005
- type: recall_at_3
value: 13.222999999999999
- type: recall_at_5
value: 18.062
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
- type: cos_sim_pearson
value: 87.57401378797366
- type: cos_sim_spearman
value: 82.83001707430854
- type: euclidean_pearson
value: 84.86793164498624
- type: euclidean_spearman
value: 82.55413453843204
- type: manhattan_pearson
value: 84.8851834466949
- type: manhattan_spearman
value: 82.5582994454054
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 87.42938681941963
- type: cos_sim_spearman
value: 78.65009395911503
- type: euclidean_pearson
value: 85.83478468305478
- type: euclidean_spearman
value: 79.01427999514746
- type: manhattan_pearson
value: 85.81496883353536
- type: manhattan_spearman
value: 78.99456935403117
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 89.44529804367387
- type: cos_sim_spearman
value: 90.00142148909681
- type: euclidean_pearson
value: 89.00052026000864
- type: euclidean_spearman
value: 89.86653252628048
- type: manhattan_pearson
value: 88.95743893759386
- type: manhattan_spearman
value: 89.83494500063517
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 87.45360957773492
- type: cos_sim_spearman
value: 84.96999168443674
- type: euclidean_pearson
value: 86.73163292656861
- type: euclidean_spearman
value: 85.16035306962318
- type: manhattan_pearson
value: 86.71055630525136
- type: manhattan_spearman
value: 85.14629965640846
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 88.63706368456388
- type: cos_sim_spearman
value: 89.81153125001883
- type: euclidean_pearson
value: 88.83649620738461
- type: euclidean_spearman
value: 89.47909072703986
- type: manhattan_pearson
value: 88.83193018422992
- type: manhattan_spearman
value: 89.47672272039262
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 85.34235491663839
- type: cos_sim_spearman
value: 86.70854613787373
- type: euclidean_pearson
value: 85.73730484853073
- type: euclidean_spearman
value: 86.28313894663437
- type: manhattan_pearson
value: 85.70285004041696
- type: manhattan_spearman
value: 86.26723700895138
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-en)
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 90.10976781396273
- type: cos_sim_spearman
value: 89.79699475327726
- type: euclidean_pearson
value: 89.51007666708566
- type: euclidean_spearman
value: 88.97696159087126
- type: manhattan_pearson
value: 89.5441850001744
- type: manhattan_spearman
value: 89.04684488385651
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (en)
config: en
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 69.8918539910347
- type: cos_sim_spearman
value: 69.66706227647323
- type: euclidean_pearson
value: 70.87888342240508
- type: euclidean_spearman
value: 69.34119085154248
- type: manhattan_pearson
value: 70.8912286820092
- type: manhattan_spearman
value: 69.5009524916871
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 87.29883016932499
- type: cos_sim_spearman
value: 88.76691675006461
- type: euclidean_pearson
value: 88.20225127014815
- type: euclidean_spearman
value: 88.48087977970427
- type: manhattan_pearson
value: 88.2072233596074
- type: manhattan_spearman
value: 88.47336658990169
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 87.61294576605022
- type: mrr
value: 96.31477092261404
- task:
type: Retrieval
dataset:
type: mteb/scifact
name: MTEB SciFact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: map_at_1
value: 60.260999999999996
- type: map_at_10
value: 70.462
- type: map_at_100
value: 70.86200000000001
- type: map_at_1000
value: 70.884
- type: map_at_20
value: 70.75
- type: map_at_3
value: 67.422
- type: map_at_5
value: 68.95400000000001
- type: mrr_at_1
value: 63.0
- type: mrr_at_10
value: 71.435
- type: mrr_at_100
value: 71.755
- type: mrr_at_1000
value: 71.776
- type: mrr_at_20
value: 71.65599999999999
- type: mrr_at_3
value: 69.167
- type: mrr_at_5
value: 70.467
- type: ndcg_at_1
value: 63.0
- type: ndcg_at_10
value: 75.247
- type: ndcg_at_100
value: 76.926
- type: ndcg_at_1000
value: 77.402
- type: ndcg_at_20
value: 76.164
- type: ndcg_at_3
value: 69.966
- type: ndcg_at_5
value: 72.25200000000001
- type: precision_at_1
value: 63.0
- type: precision_at_10
value: 10.100000000000001
- type: precision_at_100
value: 1.093
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_20
value: 5.25
- type: precision_at_3
value: 27.222
- type: precision_at_5
value: 17.933
- type: recall_at_1
value: 60.260999999999996
- type: recall_at_10
value: 88.98899999999999
- type: recall_at_100
value: 96.5
- type: recall_at_1000
value: 100.0
- type: recall_at_20
value: 92.43299999999999
- type: recall_at_3
value: 74.506
- type: recall_at_5
value: 80.217
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.86039603960396
- type: cos_sim_ap
value: 96.87211054415707
- type: cos_sim_f1
value: 92.98856290402784
- type: cos_sim_precision
value: 92.48269040553907
- type: cos_sim_recall
value: 93.5
- type: dot_accuracy
value: 99.7990099009901
- type: dot_ap
value: 94.78284318973266
- type: dot_f1
value: 89.66921119592874
- type: dot_precision
value: 91.29533678756476
- type: dot_recall
value: 88.1
- type: euclidean_accuracy
value: 99.85643564356435
- type: euclidean_ap
value: 96.67239701870625
- type: euclidean_f1
value: 92.68784669692386
- type: euclidean_precision
value: 93.48931841302137
- type: euclidean_recall
value: 91.9
- type: manhattan_accuracy
value: 99.85643564356435
- type: manhattan_ap
value: 96.68690502730702
- type: manhattan_f1
value: 92.77528649725959
- type: manhattan_precision
value: 92.45283018867924
- type: manhattan_recall
value: 93.10000000000001
- type: max_accuracy
value: 99.86039603960396
- type: max_ap
value: 96.87211054415707
- type: max_f1
value: 92.98856290402784
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 66.31370326221715
- type: v_measures
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0.6600738065797027]
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 34.98820897729802
- type: v_measures
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- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 55.185955556406554
- type: mrr
value: 56.137862341906455
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.657368209428938
- type: cos_sim_spearman
value: 31.926391208280304
- type: dot_pearson
value: 28.723660986211748
- type: dot_spearman
value: 29.051223656612642
- task:
type: Retrieval
dataset:
type: mteb/trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
metrics:
- type: map_at_1
value: 0.218
- type: map_at_10
value: 1.746
- type: map_at_100
value: 9.815
- type: map_at_1000
value: 24.196
- type: map_at_20
value: 3.097
- type: map_at_3
value: 0.616
- type: map_at_5
value: 0.991
- type: mrr_at_1
value: 80.0
- type: mrr_at_10
value: 88.667
- type: mrr_at_100
value: 88.667
- type: mrr_at_1000
value: 88.667
- type: mrr_at_20
value: 88.667
- type: mrr_at_3
value: 87.667
- type: mrr_at_5
value: 88.667
- type: ndcg_at_1
value: 73.0
- type: ndcg_at_10
value: 69.377
- type: ndcg_at_100
value: 53.878
- type: ndcg_at_1000
value: 49.589
- type: ndcg_at_20
value: 66.31
- type: ndcg_at_3
value: 74.654
- type: ndcg_at_5
value: 73.56899999999999
- type: precision_at_1
value: 80.0
- type: precision_at_10
value: 73.8
- type: precision_at_100
value: 55.74
- type: precision_at_1000
value: 21.814
- type: precision_at_20
value: 70.3
- type: precision_at_3
value: 80.0
- type: precision_at_5
value: 78.0
- type: recall_at_1
value: 0.218
- type: recall_at_10
value: 1.983
- type: recall_at_100
value: 13.499
- type: recall_at_1000
value: 46.869
- type: recall_at_20
value: 3.703
- type: recall_at_3
value: 0.656
- type: recall_at_5
value: 1.0739999999999998
- task:
type: Retrieval
dataset:
type: mteb/touche2020
name: MTEB Touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
metrics:
- type: map_at_1
value: 2.358
- type: map_at_10
value: 9.494
- type: map_at_100
value: 15.809999999999999
- type: map_at_1000
value: 17.308
- type: map_at_20
value: 12.171
- type: map_at_3
value: 4.727
- type: map_at_5
value: 6.798
- type: mrr_at_1
value: 30.612000000000002
- type: mrr_at_10
value: 44.615
- type: mrr_at_100
value: 45.794000000000004
- type: mrr_at_1000
value: 45.812999999999995
- type: mrr_at_20
value: 45.519999999999996
- type: mrr_at_3
value: 41.156
- type: mrr_at_5
value: 42.483
- type: ndcg_at_1
value: 26.531
- type: ndcg_at_10
value: 23.115
- type: ndcg_at_100
value: 36.082
- type: ndcg_at_1000
value: 47.467999999999996
- type: ndcg_at_20
value: 25.224999999999998
- type: ndcg_at_3
value: 25.238
- type: ndcg_at_5
value: 24.299
- type: precision_at_1
value: 30.612000000000002
- type: precision_at_10
value: 20.816000000000003
- type: precision_at_100
value: 7.796
- type: precision_at_1000
value: 1.545
- type: precision_at_20
value: 17.347
- type: precision_at_3
value: 27.211000000000002
- type: precision_at_5
value: 25.306
- type: recall_at_1
value: 2.358
- type: recall_at_10
value: 15.433
- type: recall_at_100
value: 48.715
- type: recall_at_1000
value: 83.574
- type: recall_at_20
value: 24.038999999999998
- type: recall_at_3
value: 5.652
- type: recall_at_5
value: 9.327
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 67.9052734375
- type: ap
value: 12.464903195452706
- type: f1
value: 51.75730802861531
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 59.21618562535371
- type: f1
value: 59.5671083304645
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 52.98411009798346
- type: v_measures
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0.5174886066510178]
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 87.30404720748643
- type: cos_sim_ap
value: 78.24262856109937
- type: cos_sim_f1
value: 72.08312468703055
- type: cos_sim_precision
value: 68.58027632205813
- type: cos_sim_recall
value: 75.96306068601582
- type: dot_accuracy
value: 84.48471121177803
- type: dot_ap
value: 67.78610175988638
- type: dot_f1
value: 63.75754527162978
- type: dot_precision
value: 60.908217203267654
- type: dot_recall
value: 66.88654353562006
- type: euclidean_accuracy
value: 87.24444179531503
- type: euclidean_ap
value: 78.16169396391096
- type: euclidean_f1
value: 72.19500244977952
- type: euclidean_precision
value: 67.37540009144948
- type: euclidean_recall
value: 77.75725593667546
- type: manhattan_accuracy
value: 87.20867854801216
- type: manhattan_ap
value: 78.10430615026713
- type: manhattan_f1
value: 72.25504677498769
- type: manhattan_precision
value: 67.72035071527456
- type: manhattan_recall
value: 77.44063324538259
- type: max_accuracy
value: 87.30404720748643
- type: max_ap
value: 78.24262856109937
- type: max_f1
value: 72.25504677498769
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.08681647067955
- type: cos_sim_ap
value: 86.10715470590844
- type: cos_sim_f1
value: 78.62958187511512
- type: cos_sim_precision
value: 75.38320265592992
- type: cos_sim_recall
value: 82.16815522020326
- type: dot_accuracy
value: 88.00985756975977
- type: dot_ap
value: 83.27536710177887
- type: dot_f1
value: 76.57026000584284
- type: dot_precision
value: 72.82578494026119
- type: dot_recall
value: 80.72066522944257
- type: euclidean_accuracy
value: 88.9024721543059
- type: euclidean_ap
value: 85.83507000245919
- type: euclidean_f1
value: 78.354072605807
- type: euclidean_precision
value: 74.87197474570326
- type: euclidean_recall
value: 82.17585463504774
- type: manhattan_accuracy
value: 88.90829355377032
- type: manhattan_ap
value: 85.82130285331947
- type: manhattan_f1
value: 78.28887843364338
- type: manhattan_precision
value: 73.86464522297344
- type: manhattan_recall
value: 83.2768709578072
- type: max_accuracy
value: 89.08681647067955
- type: max_ap
value: 86.10715470590844
- type: max_f1
value: 78.62958187511512
---
To use this model:
```
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("w601sxs/b1ade-embed")
```
b1ade-embed is part of a collection of small models for RAG.
|
allenai/specter2_aug2023refresh_base | allenai | "2024-05-14T23:39:35Z" | 19,049 | 2 | transformers | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"dataset:allenai/scirepeval",
"license:apache-2.0",
"endpoints_compatible",
"text-embeddings-inference",
"region:us"
] | feature-extraction | "2023-08-14T19:16:06Z" | ---
license: apache-2.0
datasets:
- allenai/scirepeval
---
## SPECTER2
<!-- Provide a quick summary of what the model is/does. -->
SPECTER2 is a family of models that succeeds [SPECTER](https://huggingface.co/allenai/specter) and is capable of generating task specific embeddings for scientific tasks when paired with [adapters](https://huggingface.co/models?search=allenai/specter-2_).
This is the base encoder to be used with relevant task specific adapters.
Given the combination of title and abstract of a scientific paper or a short texual query, the model can be used to generate effective embeddings to be used in downstream applications.
**Note:For general embedding purposes, please use [allenai/specter2](https://huggingface.co/allenai/specter2).**
**To get the best performance on a downstream task type please load the associated adapter () with the base model as in the example below.**
**Dec 2023 Update:**
Model usage updated to be compatible with latest versions of transformers and adapters (newly released update to adapter-transformers) libraries.
**\*\*\*\*\*\*Update\*\*\*\*\*\***
This update introduces a new set of SPECTER2 models with the base transformer encoder pre-trained on an extended citation dataset containing more recent papers.
For benchmarking purposes please use the existing SPECTER2 [models](https://huggingface.co/allenai/specter2) w/o the **aug2023refresh** suffix.
**Note:For general embedding purposes, please use [allenai/specter2](https://huggingface.co/allenai/specter2).**
**To get the best performance on a downstream task type please load the associated adapter with the base model as in the example below.**
# Model Details
## Model Description
SPECTER2 has been trained on over 6M triplets of scientific paper citations, which are available [here](https://huggingface.co/datasets/allenai/scirepeval/viewer/cite_prediction_new/evaluation).
Post that it is trained with additionally attached task format specific adapter modules on all the [SciRepEval](https://huggingface.co/datasets/allenai/scirepeval) training tasks.
Task Formats trained on:
- Classification
- Regression
- Proximity
- Adhoc Search
It builds on the work done in [SciRepEval: A Multi-Format Benchmark for Scientific Document Representations](https://api.semanticscholar.org/CorpusID:254018137) and we evaluate the trained model on this benchmark as well.
- **Developed by:** Amanpreet Singh, Mike D'Arcy, Arman Cohan, Doug Downey, Sergey Feldman
- **Shared by :** Allen AI
- **Model type:** bert-base-uncased + adapters
- **License:** Apache 2.0
- **Finetuned from model:** [allenai/scibert](https://huggingface.co/allenai/scibert_scivocab_uncased).
## Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** [https://github.com/allenai/SPECTER2](https://github.com/allenai/SPECTER2)
- **Paper:** [https://api.semanticscholar.org/CorpusID:254018137](https://api.semanticscholar.org/CorpusID:254018137)
- **Demo:** [Usage](https://github.com/allenai/SPECTER2/blob/main/README.md)
# Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
## Direct Use
|Model|Name and HF link|Description|
|--|--|--|
|Proximity*|[allenai/specter2_aug2023refresh](https://huggingface.co/allenai/specter2_aug2023refresh)|Encode papers as queries and candidates eg. Link Prediction, Nearest Neighbor Search|
|Adhoc Query|[allenai/specter2_aug2023refresh_adhoc_query](https://huggingface.co/allenai/specter2_aug2023refresh_adhoc_query)|Encode short raw text queries for search tasks. (Candidate papers can be encoded with the proximity adapter)|
|Classification|[allenai/specter2_aug2023refresh_classification](https://huggingface.co/allenai/specter2_aug2023refresh_classification)|Encode papers to feed into linear classifiers as features|
|Regression|[allenai/specter2_aug2023refresh_regression](https://huggingface.co/allenai/specter2_aug2023refresh_regression)|Encode papers to feed into linear regressors as features|
*Proximity model should suffice for downstream task types not mentioned above
```python
from transformers import AutoTokenizer
from adapters import AutoAdapterModel
# load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained('allenai/specter2_aug2023refresh_base')
#load base model
model = AutoAdapterModel.from_pretrained('allenai/specter2_aug2023refresh_base')
#load the adapter(s) as per the required task, provide an identifier for the adapter in load_as argument and activate it
model.load_adapter("allenai/specter2_aug2023refresh", source="hf", load_as="proximity", set_active=True)
#other possibilities: allenai/specter2_aug2023refresh_<classification|regression|adhoc_query>
papers = [{'title': 'BERT', 'abstract': 'We introduce a new language representation model called BERT'},
{'title': 'Attention is all you need', 'abstract': ' The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'}]
# concatenate title and abstract
text_batch = [d['title'] + tokenizer.sep_token + (d.get('abstract') or '') for d in papers]
# preprocess the input
inputs = self.tokenizer(text_batch, padding=True, truncation=True,
return_tensors="pt", return_token_type_ids=False, max_length=512)
output = model(**inputs)
# take the first token in the batch as the embedding
embeddings = output.last_hidden_state[:, 0, :]
```
## Downstream Use
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
For evaluation and downstream usage, please refer to [https://github.com/allenai/scirepeval/blob/main/evaluation/INFERENCE.md](https://github.com/allenai/scirepeval/blob/main/evaluation/INFERENCE.md).
# Training Details
## Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
The base model is trained on citation links between papers and the adapters are trained on 8 large scale tasks across the four formats.
All the data is a part of SciRepEval benchmark and is available [here](https://huggingface.co/datasets/allenai/scirepeval).
The citation link are triplets in the form
```json
{"query": {"title": ..., "abstract": ...}, "pos": {"title": ..., "abstract": ...}, "neg": {"title": ..., "abstract": ...}}
```
consisting of a query paper, a positive citation and a negative which can be from the same/different field of study as the query or citation of a citation.
## Training Procedure
Please refer to the [SPECTER paper](https://api.semanticscholar.org/CorpusID:215768677).
### Training Hyperparameters
The model is trained in two stages using [SciRepEval](https://github.com/allenai/scirepeval/blob/main/training/TRAINING.md):
- Base Model: First a base model is trained on the above citation triplets.
``` batch size = 1024, max input length = 512, learning rate = 2e-5, epochs = 2 warmup steps = 10% fp16```
- Adapters: Thereafter, task format specific adapters are trained on the SciRepEval training tasks, where 600K triplets are sampled from above and added to the training data as well.
``` batch size = 256, max input length = 512, learning rate = 1e-4, epochs = 6 warmup = 1000 steps fp16```
# Evaluation
We evaluate the model on [SciRepEval](https://github.com/allenai/scirepeval), a large scale eval benchmark for scientific embedding tasks which which has [SciDocs] as a subset.
We also evaluate and establish a new SoTA on [MDCR](https://github.com/zoranmedic/mdcr), a large scale citation recommendation benchmark.
|Model|SciRepEval In-Train|SciRepEval Out-of-Train|SciRepEval Avg|MDCR(MAP, Recall@5)|
|--|--|--|--|--|
|[BM-25](https://api.semanticscholar.org/CorpusID:252199740)|n/a|n/a|n/a|(33.7, 28.5)|
|[SPECTER](https://huggingface.co/allenai/specter)|54.7|57.4|68.0|(30.6, 25.5)|
|[SciNCL](https://huggingface.co/malteos/scincl)|55.6|57.8|69.0|(32.6, 27.3)|
|[SciRepEval-Adapters](https://huggingface.co/models?search=scirepeval)|61.9|59.0|70.9|(35.3, 29.6)|
|[SPECTER2-Adapters](https://huggingface.co/models?search=allenai/specter-2)|**62.3**|**59.2**|**71.2**|**(38.4, 33.0)**|
Please cite the following works if you end up using SPECTER2:
[SPECTER paper](https://api.semanticscholar.org/CorpusID:215768677):
```bibtex
@inproceedings{specter2020cohan,
title={{SPECTER: Document-level Representation Learning using Citation-informed Transformers}},
author={Arman Cohan and Sergey Feldman and Iz Beltagy and Doug Downey and Daniel S. Weld},
booktitle={ACL},
year={2020}
}
```
[SciRepEval paper](https://api.semanticscholar.org/CorpusID:254018137)
```bibtex
@inproceedings{Singh2022SciRepEvalAM,
title={SciRepEval: A Multi-Format Benchmark for Scientific Document Representations},
author={Amanpreet Singh and Mike D'Arcy and Arman Cohan and Doug Downey and Sergey Feldman},
booktitle={Conference on Empirical Methods in Natural Language Processing},
year={2022},
url={https://api.semanticscholar.org/CorpusID:254018137}
}
```
|
wajidlinux99/gibberish-text-detector | wajidlinux99 | "2023-01-16T12:15:52Z" | 19,030 | 5 | transformers | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"text",
"nlp",
"correction",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2023-01-16T11:46:09Z" | ---
language:
- en
pipeline_tag: text-classification
tags:
- text
- nlp
- correction
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 492513457
- CO2 Emissions (in grams): 5.527544460835904
## Validation Metrics
- Loss: 0.07609463483095169
- Accuracy: 0.9735624586913417
- Macro F1: 0.9736173135739408
- Micro F1: 0.9735624586913417
- Weighted F1: 0.9736173135739408
- Macro Precision: 0.9737771415197378
- Micro Precision: 0.9735624586913417
- Weighted Precision: 0.9737771415197378
- Macro Recall: 0.9735624586913417
- Micro Recall: 0.9735624586913417
- Weighted Recall: 0.9735624586913417
## Usage
You can use CURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "Is this text really worth it?"}' https://api-inference.huggingface.co/models/wajidlinux99/gibberish-text-detector
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("wajidlinux99/gibberish-text-detector", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("wajidlinux99/gibberish-text-detector", use_auth_token=True)
inputs = tokenizer("Is this text really worth it?", return_tensors="pt")
outputs = model(**inputs)
```
# Original Repository
***madhurjindal/autonlp-Gibberish-Detector-492513457 |
bartowski/Einstein-v7-Qwen2-7B-GGUF | bartowski | "2024-06-25T23:33:55Z" | 19,028 | 2 | null | [
"gguf",
"axolotl",
"instruct",
"finetune",
"chatml",
"gpt4",
"synthetic data",
"science",
"physics",
"chemistry",
"biology",
"math",
"qwen",
"qwen2",
"text-generation",
"en",
"dataset:allenai/ai2_arc",
"dataset:camel-ai/physics",
"dataset:camel-ai/chemistry",
"dataset:camel-ai/biology",
"dataset:camel-ai/math",
"dataset:metaeval/reclor",
"dataset:openbookqa",
"dataset:mandyyyyii/scibench",
"dataset:derek-thomas/ScienceQA",
"dataset:TIGER-Lab/ScienceEval",
"dataset:jondurbin/airoboros-3.2",
"dataset:LDJnr/Capybara",
"dataset:Cot-Alpaca-GPT4-From-OpenHermes-2.5",
"dataset:STEM-AI-mtl/Electrical-engineering",
"dataset:knowrohit07/saraswati-stem",
"dataset:sablo/oasst2_curated",
"dataset:lmsys/lmsys-chat-1m",
"dataset:TIGER-Lab/MathInstruct",
"dataset:bigbio/med_qa",
"dataset:meta-math/MetaMathQA-40K",
"dataset:piqa",
"dataset:scibench",
"dataset:sciq",
"dataset:Open-Orca/SlimOrca",
"dataset:migtissera/Synthia-v1.3",
"dataset:allenai/WildChat",
"dataset:microsoft/orca-math-word-problems-200k",
"dataset:openchat/openchat_sharegpt4_dataset",
"dataset:teknium/GPTeacher-General-Instruct",
"dataset:m-a-p/CodeFeedback-Filtered-Instruction",
"dataset:totally-not-an-llm/EverythingLM-data-V3",
"dataset:HuggingFaceH4/no_robots",
"dataset:OpenAssistant/oasst_top1_2023-08-25",
"dataset:WizardLM/WizardLM_evol_instruct_70k",
"dataset:abacusai/SystemChat-1.1",
"dataset:H-D-T/Buzz-V1.2",
"base_model:Qwen/Qwen2-7B",
"license:other",
"region:us"
] | text-generation | "2024-06-25T23:11:04Z" | ---
language:
- en
license: other
tags:
- axolotl
- instruct
- finetune
- chatml
- gpt4
- synthetic data
- science
- physics
- chemistry
- biology
- math
- qwen
- qwen2
base_model: Qwen/Qwen2-7B
datasets:
- allenai/ai2_arc
- camel-ai/physics
- camel-ai/chemistry
- camel-ai/biology
- camel-ai/math
- metaeval/reclor
- openbookqa
- mandyyyyii/scibench
- derek-thomas/ScienceQA
- TIGER-Lab/ScienceEval
- jondurbin/airoboros-3.2
- LDJnr/Capybara
- Cot-Alpaca-GPT4-From-OpenHermes-2.5
- STEM-AI-mtl/Electrical-engineering
- knowrohit07/saraswati-stem
- sablo/oasst2_curated
- lmsys/lmsys-chat-1m
- TIGER-Lab/MathInstruct
- bigbio/med_qa
- meta-math/MetaMathQA-40K
- openbookqa
- piqa
- metaeval/reclor
- derek-thomas/ScienceQA
- scibench
- sciq
- Open-Orca/SlimOrca
- migtissera/Synthia-v1.3
- TIGER-Lab/ScienceEval
- allenai/WildChat
- microsoft/orca-math-word-problems-200k
- openchat/openchat_sharegpt4_dataset
- teknium/GPTeacher-General-Instruct
- m-a-p/CodeFeedback-Filtered-Instruction
- totally-not-an-llm/EverythingLM-data-V3
- HuggingFaceH4/no_robots
- OpenAssistant/oasst_top1_2023-08-25
- WizardLM/WizardLM_evol_instruct_70k
- abacusai/SystemChat-1.1
- H-D-T/Buzz-V1.2
quantized_by: bartowski
pipeline_tag: text-generation
---
## Llamacpp imatrix Quantizations of Einstein-v7-Qwen2-7B
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b3197">b3197</a> for quantization.
Original model: https://huggingface.co/Weyaxi/Einstein-v7-Qwen2-7B
All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8)
## Prompt format
```
<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [Einstein-v7-Qwen2-7B-Q8_0_L.gguf](https://huggingface.co/bartowski/Einstein-v7-Qwen2-7B-GGUF/blob/main/Einstein-v7-Qwen2-7B-Q8_1.gguf) | Q8_0_L | 9.12GB | *Experimental*, uses f16 for embed and output weights. Please provide any feedback of differences. Extremely high quality, generally unneeded but max available quant. |
| [Einstein-v7-Qwen2-7B-Q8_0.gguf](https://huggingface.co/bartowski/Einstein-v7-Qwen2-7B-GGUF/blob/main/Einstein-v7-Qwen2-7B-Q8_0.gguf) | Q8_0 | 8.09GB | Extremely high quality, generally unneeded but max available quant. |
| [Einstein-v7-Qwen2-7B-Q6_K_L.gguf](https://huggingface.co/bartowski/Einstein-v7-Qwen2-7B-GGUF/blob/main/Einstein-v7-Qwen2-7B-Q6_K_L.gguf) | Q6_K_L | 7.54GB | *Experimental*, uses f16 for embed and output weights. Please provide any feedback of differences. Very high quality, near perfect, *recommended*. |
| [Einstein-v7-Qwen2-7B-Q6_K.gguf](https://huggingface.co/bartowski/Einstein-v7-Qwen2-7B-GGUF/blob/main/Einstein-v7-Qwen2-7B-Q6_K.gguf) | Q6_K | 6.25GB | Very high quality, near perfect, *recommended*. |
| [Einstein-v7-Qwen2-7B-Q5_K_L.gguf](https://huggingface.co/bartowski/Einstein-v7-Qwen2-7B-GGUF/blob/main/Einstein-v7-Qwen2-7B-Q5_K_L.gguf) | Q5_K_L | 6.80GB | *Experimental*, uses f16 for embed and output weights. Please provide any feedback of differences. High quality, *recommended*. |
| [Einstein-v7-Qwen2-7B-Q5_K_M.gguf](https://huggingface.co/bartowski/Einstein-v7-Qwen2-7B-GGUF/blob/main/Einstein-v7-Qwen2-7B-Q5_K_M.gguf) | Q5_K_M | 5.44GB | High quality, *recommended*. |
| [Einstein-v7-Qwen2-7B-Q5_K_S.gguf](https://huggingface.co/bartowski/Einstein-v7-Qwen2-7B-GGUF/blob/main/Einstein-v7-Qwen2-7B-Q5_K_S.gguf) | Q5_K_S | 5.31GB | High quality, *recommended*. |
| [Einstein-v7-Qwen2-7B-Q4_K_L.gguf](https://huggingface.co/bartowski/Einstein-v7-Qwen2-7B-GGUF/blob/main/Einstein-v7-Qwen2-7B-Q4_K_L.gguf) | Q4_K_L | 6.10GB | *Experimental*, uses f16 for embed and output weights. Please provide any feedback of differences. Good quality, uses about 4.83 bits per weight, *recommended*. |
| [Einstein-v7-Qwen2-7B-Q4_K_M.gguf](https://huggingface.co/bartowski/Einstein-v7-Qwen2-7B-GGUF/blob/main/Einstein-v7-Qwen2-7B-Q4_K_M.gguf) | Q4_K_M | 4.68GB | Good quality, uses about 4.83 bits per weight, *recommended*. |
| [Einstein-v7-Qwen2-7B-Q4_K_S.gguf](https://huggingface.co/bartowski/Einstein-v7-Qwen2-7B-GGUF/blob/main/Einstein-v7-Qwen2-7B-Q4_K_S.gguf) | Q4_K_S | 4.45GB | Slightly lower quality with more space savings, *recommended*. |
| [Einstein-v7-Qwen2-7B-IQ4_XS.gguf](https://huggingface.co/bartowski/Einstein-v7-Qwen2-7B-GGUF/blob/main/Einstein-v7-Qwen2-7B-IQ4_XS.gguf) | IQ4_XS | 4.21GB | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [Einstein-v7-Qwen2-7B-Q3_K_XL.gguf](https://huggingface.co/bartowski/Einstein-v7-Qwen2-7B-GGUF//main/Einstein-v7-Qwen2-7B-Q3_K_XL.gguf) | Q3_K_XL | | *Experimental*, uses f16 for embed and output weights. Please provide any feedback of differences. Lower quality but usable, good for low RAM availability. |
| [Einstein-v7-Qwen2-7B-Q3_K_L.gguf](https://huggingface.co/bartowski/Einstein-v7-Qwen2-7B-GGUF/blob/main/Einstein-v7-Qwen2-7B-Q3_K_L.gguf) | Q3_K_L | 4.08GB | Lower quality but usable, good for low RAM availability. |
| [Einstein-v7-Qwen2-7B-Q3_K_M.gguf](https://huggingface.co/bartowski/Einstein-v7-Qwen2-7B-GGUF/blob/main/Einstein-v7-Qwen2-7B-Q3_K_M.gguf) | Q3_K_M | 3.80GB | Even lower quality. |
| [Einstein-v7-Qwen2-7B-IQ3_M.gguf](https://huggingface.co/bartowski/Einstein-v7-Qwen2-7B-GGUF/blob/main/Einstein-v7-Qwen2-7B-IQ3_M.gguf) | IQ3_M | 3.57GB | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [Einstein-v7-Qwen2-7B-Q3_K_S.gguf](https://huggingface.co/bartowski/Einstein-v7-Qwen2-7B-GGUF/blob/main/Einstein-v7-Qwen2-7B-Q3_K_S.gguf) | Q3_K_S | 3.49GB | Low quality, not recommended. |
| [Einstein-v7-Qwen2-7B-IQ3_XS.gguf](https://huggingface.co/bartowski/Einstein-v7-Qwen2-7B-GGUF/blob/main/Einstein-v7-Qwen2-7B-IQ3_XS.gguf) | IQ3_XS | 3.34GB | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| [Einstein-v7-Qwen2-7B-IQ3_XXS.gguf](https://huggingface.co/bartowski/Einstein-v7-Qwen2-7B-GGUF/blob/main/Einstein-v7-Qwen2-7B-IQ3_XXS.gguf) | IQ3_XXS | 3.11GB | Lower quality, new method with decent performance, comparable to Q3 quants. |
| [Einstein-v7-Qwen2-7B-Q2_K.gguf](https://huggingface.co/bartowski/Einstein-v7-Qwen2-7B-GGUF/blob/main/Einstein-v7-Qwen2-7B-Q2_K.gguf) | Q2_K | 3.01GB | Very low quality but surprisingly usable. |
| [Einstein-v7-Qwen2-7B-IQ2_M.gguf](https://huggingface.co/bartowski/Einstein-v7-Qwen2-7B-GGUF/blob/main/Einstein-v7-Qwen2-7B-IQ2_M.gguf) | IQ2_M | 2.78GB | Very low quality, uses SOTA techniques to also be surprisingly usable. |
| [Einstein-v7-Qwen2-7B-IQ2_S.gguf](https://huggingface.co/bartowski/Einstein-v7-Qwen2-7B-GGUF/blob/main/Einstein-v7-Qwen2-7B-IQ2_S.gguf) | IQ2_S | 2.59GB | Very low quality, uses SOTA techniques to be usable. |
| [Einstein-v7-Qwen2-7B-IQ2_XS.gguf](https://huggingface.co/bartowski/Einstein-v7-Qwen2-7B-GGUF/blob/main/Einstein-v7-Qwen2-7B-IQ2_XS.gguf) | IQ2_XS | 2.46GB | Very low quality, uses SOTA techniques to be usable. |
## Downloading using huggingface-cli
First, make sure you have hugginface-cli installed:
```
pip install -U "huggingface_hub[cli]"
```
Then, you can target the specific file you want:
```
huggingface-cli download bartowski/Einstein-v7-Qwen2-7B-GGUF --include "Einstein-v7-Qwen2-7B-Q4_K_M.gguf" --local-dir ./
```
If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:
```
huggingface-cli download bartowski/Einstein-v7-Qwen2-7B-GGUF --include "Einstein-v7-Qwen2-7B-Q8_0.gguf/*" --local-dir Einstein-v7-Qwen2-7B-Q8_0
```
You can either specify a new local-dir (Einstein-v7-Qwen2-7B-Q8_0) or download them all in place (./)
## Which file should I choose?
A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)
The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.
If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.
If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.
Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.
If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.
If you want to get more into the weeds, you can check out this extremely useful feature chart:
[llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix)
But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.
These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.
The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm.
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
|
mradermacher/gemma-2-9b-nopcsa-finetune-GGUF | mradermacher | "2024-07-02T13:23:01Z" | 19,021 | 0 | transformers | [
"transformers",
"gguf",
"generated_from_trainer",
"en",
"base_model:AndreasThinks/gemma-2-9b-nopcsa-finetune",
"license:gemma",
"endpoints_compatible",
"region:us"
] | null | "2024-07-02T01:29:28Z" | ---
base_model: AndreasThinks/gemma-2-9b-nopcsa-finetune
language:
- en
library_name: transformers
license: gemma
quantized_by: mradermacher
tags:
- generated_from_trainer
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/AndreasThinks/gemma-2-9b-nopcsa-finetune
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/gemma-2-9b-nopcsa-finetune-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/gemma-2-9b-nopcsa-finetune-GGUF/resolve/main/gemma-2-9b-nopcsa-finetune.Q2_K.gguf) | Q2_K | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-2-9b-nopcsa-finetune-GGUF/resolve/main/gemma-2-9b-nopcsa-finetune.IQ3_XS.gguf) | IQ3_XS | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-2-9b-nopcsa-finetune-GGUF/resolve/main/gemma-2-9b-nopcsa-finetune.IQ3_S.gguf) | IQ3_S | 4.4 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/gemma-2-9b-nopcsa-finetune-GGUF/resolve/main/gemma-2-9b-nopcsa-finetune.Q3_K_S.gguf) | Q3_K_S | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-2-9b-nopcsa-finetune-GGUF/resolve/main/gemma-2-9b-nopcsa-finetune.IQ3_M.gguf) | IQ3_M | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-2-9b-nopcsa-finetune-GGUF/resolve/main/gemma-2-9b-nopcsa-finetune.Q3_K_M.gguf) | Q3_K_M | 4.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/gemma-2-9b-nopcsa-finetune-GGUF/resolve/main/gemma-2-9b-nopcsa-finetune.Q3_K_L.gguf) | Q3_K_L | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-2-9b-nopcsa-finetune-GGUF/resolve/main/gemma-2-9b-nopcsa-finetune.IQ4_XS.gguf) | IQ4_XS | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-2-9b-nopcsa-finetune-GGUF/resolve/main/gemma-2-9b-nopcsa-finetune.Q4_K_S.gguf) | Q4_K_S | 5.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/gemma-2-9b-nopcsa-finetune-GGUF/resolve/main/gemma-2-9b-nopcsa-finetune.Q4_K_M.gguf) | Q4_K_M | 5.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/gemma-2-9b-nopcsa-finetune-GGUF/resolve/main/gemma-2-9b-nopcsa-finetune.Q5_K_S.gguf) | Q5_K_S | 6.6 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-2-9b-nopcsa-finetune-GGUF/resolve/main/gemma-2-9b-nopcsa-finetune.Q5_K_M.gguf) | Q5_K_M | 6.7 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-2-9b-nopcsa-finetune-GGUF/resolve/main/gemma-2-9b-nopcsa-finetune.Q6_K.gguf) | Q6_K | 7.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/gemma-2-9b-nopcsa-finetune-GGUF/resolve/main/gemma-2-9b-nopcsa-finetune.Q8_0.gguf) | Q8_0 | 9.9 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/gemma-2-9b-nopcsa-finetune-GGUF/resolve/main/gemma-2-9b-nopcsa-finetune.f16.gguf) | f16 | 18.6 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
Azma-AI/bart-large-text-summarizer | Azma-AI | "2023-10-14T19:15:43Z" | 18,997 | 6 | transformers | [
"transformers",
"pytorch",
"tf",
"safetensors",
"bart",
"text2text-generation",
"seq2seq",
"summarization",
"en",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | summarization | "2023-10-14T18:30:59Z" | ---
language: en
license: apache-2.0
tags:
- bart
- seq2seq
- summarization
datasets:
- cnndaily/newyorkdaily/xsum/samsum/dialogsum/AMI
metrics:
- rouge
widget:
- text: 'Hi, I''m David and I''m supposed to be an industrial designer. Um, I just
got the project announcement about what the project is. Designing a remote control.
That''s about it, didn''t get anything else. Did you get the same thing? Cool.
There''s too much gear. Okay. Can''t draw. Um. Yeah. Um, well anyway, I don''t
know, it''s just the first animal I can think off the top of my head. Um. Yes.
Big reason is ''cause I''m allergic to most animals. Allergic to animal fur, so
um fish was a natural choice. Um, yeah, and I kind of like whales. They come in
and go eat everything in sight. And they''re quite harmless and mild and interesting.
Tail''s a bit big, I think. It''s an after dinner dog then. Hmm. It does make
sense from maybe the design point of view ''cause you have more complicated characters
like European languages, then you need more buttons. So, possibly. Hmm. Yeah.
And you keep losing them. Finding them is really a pain, you know. I mean it''s
usually quite small, or when you want it right, it slipped behind the couch or
it''s kicked under the table. You know. Yep. Mm-hmm. I think one factor would
be production cost. Because there''s a cap there, so um depends on how much you
can cram into that price. Um. I think that that''s the main factor. Cool.
Okay. Right. Um well this is the kick-off meeting for our our project. Um and
um this is just what we''re gonna be doing over the next twenty five minutes.
Um so first of all, just to kind of make sure that we all know each other, I''m
Laura and I''m the project manager. Do you want to introduce yourself again? Okay.
Great. Okay. Um so we''re designing a new remote control and um Oh I have to record
who''s here actually. So that''s David, Andrew and Craig, isn''t it? And you all
arrived on time. Um yeah so des uh design a new remote control. Um, as you can
see it''s supposed to be original, trendy and user friendly. Um so that''s kind
of our our brief, as it were. Um and so there are three different stages to the
design. Um I''m not really sure what what you guys have already received um in
your emails. What did you get? Mm-hmm. Is that what everybody got? Okay. Um. So
we''re gonna have like individual work and then a meeting about it. And repeat
that process three times. Um and at this point we get try out the whiteboard over
there. Um. So uh you get to draw your favourite animal and sum up your favourite
characteristics of it. So who would like to go first? Very good. Mm-hmm. Yeah.
Yeah. Right. Lovely. Right. You can take as long over this as you like, because
we haven''t got an awful lot to discuss. Ok oh we do we do. Don''t feel like you''re
in a rush, anyway. Ach why not We might have to get you up again then. I don''t
know what mine is. I''m gonna have to think on the spot now. Is that a whale?
Ah. Okay. God, I still don''t know what I''m gonna write about. Um. I was gonna
choose a dog as well. But I''ll just draw a different kind of dog. M my favourite
animal is my own dog at home. Um That doesn''t really look like him, actually.
He looks more like a pig, actually. Ah well. Do you? Oh that''s very good of you.
Uh. Um he''s a mixture of uh various things. Um and what do I like about him,
um That''s just to suggest that his tail wags. Um he''s very friendly and cheery
and always pleased to see you, and very kind of affectionate and um uh and he''s
quite quite wee as well so you know he can doesn''t take up too much space. Um
and uh And he does a funny thing where he chases his tail as well, which is quite
amusing, so It is. I think it is. He only does it after he''s had his dinner and
um he''ll just all of a sudden just get up and start chasing his tail ''round
the living room. Yeah, so uh Yeah, maybe. Maybe. Right, um where did you find
this? Just down here? Yeah. Okay. Um what are we doing next? Uh um. Okay, uh we
now need to discuss the project finance. Um so according to the brief um we''re
gonna be selling this remote control for twenty five Euro, um and we''re aiming
to make fifty million Euro. Um so we''re gonna be selling this on an international
scale. And uh we don''t want it to cost any more than uh twelve fifty Euros, so
fifty percent of the selling price. Sure. All together. Um I dunno. I imagine
That''s a good question. I imagine it probably is our sale actually because it''s
probably up to the the um the retailer to uh sell it for whatever price they want.
Um. But I I don''t know, I mean do you think the fact that it''s going to be sold
internationally will have a bearing on how we design it at all? Think it will?
Um. Hmm. Oh yeah, regions and stuff, yeah. Yeah. Okay. Yeah. Well for a remote
control, do you think that will be I suppose it''s depends on how complicated
our remote control is. Yeah, yeah. Okay. What, just like in terms of like the
wealth of the country? Like how much money people have to spend on things like?
Aye, I see what you mean, yeah. Marketing. Good marketing thoughts. Oh gosh, I
should be writing all this down. Um. Mm. Yeah. Yeah, yeah. Like how much does,
you know, a remote control cost. Well twenty five Euro, I mean that''s um that''s
about like eighteen pounds or something, isn''t it? Or no, is it as much as that?
Sixteen seventeen eighteen pounds. Um, I dunno, I''ve never bought a remote control,
so I don''t know how how good a remote control that would get you. Um. But yeah,
I suppose it has to look kind of cool and gimmicky. Um right, okay. Let me just
scoot on ahead here. Okay. Um well d Does anybody have anything to add to uh to
the finance issue at all? Thin No, actually. That would be useful, though, wouldn''t
it, if you knew like what your money would get you now. Mm-hmm. Yeah, yeah. Oh.
Five minutes to end of meeting. Oh, okay. We''re a bit behind. Yeah. Right, so
do you think that should be like a main design aim of our remote control d you
know, do your your satellite and your regular telly and your V_C_R_ and everything?
Mm-hmm. Yeah. Or even like, you know, notes about um what you wanna watch. Like
you might put in there oh I want to watch such and such and look a Oh that''s
a good idea. So extra functionalities. Mm-hmm. Hmm. Um okay, uh I''d wel we''re
gonna have to wrap up pretty quickly in the next couple of minutes. Um I''ll just
check we''ve nothing else. Okay. Um so anything else anybody wants to add about
what they don''t like about remote controls they''ve used, what they would really
like to be part of this new one at all? You keep losing them. Okay. Yeah. W You
get those ones where you can, if you like, whistle or make a really high pitched
noise they beep. There I mean is that something we''d want to include, do you
think? Dunno. Okay maybe. My goodness. Still feels quite primitive. Maybe like
a touch screen or something? Okay. Uh-huh, okay. Well I guess that''s up to our
industrial designer. It looks better. Yeah. Okay. Okay. Right, well um so just
to wrap up, the next meeting''s gonna be in thirty minutes. So that''s about um
about ten to twelve by my watch. Um so inbetween now and then, um as the industrial
designer, you''re gonna be working on you know the actual working design of it
so y you know what you''re doing there. Um for user interface, technical functions,
I guess that''s you know like what we''ve been talking about, what it''ll actually
do. Um and uh marketing executive, you''ll be just thinking about what it actually
what, you know, what requirements it has to has to fulfil and you''ll all get
instructions emailed to you, I guess. Um. Yeah, so it''s th the functional design
stage is next, I guess. And uh and that''s the end of the meeting. So I got that
little message a lot sooner than I thought I would, so Mm-hmm. Uh-huh, yeah. Th
Okay, well just very quickly ''cause this we''re supposed to finish now. Um I
guess that''s up to us, I mean you probably want some kind of unique selling point
of it, so um, you know Yeah. Mm-hmm. Yeah. Okay. Right, okay, we''ll that''s that''s
the end of the meeting, then. Um. So, uh thank you all for coming.
Um I''m Craig and I''m User Interface. Yeah. Well, my favourite animal would be
a monkey. Then they''re small cute and furry, and uh when planet of the apes becomes
real, I''m gonna be up there with them. Yeah. I know um My parents went out and
bought um remote controls because um they got fed up of having four or five different
remote controls for each things the house. So um for them it was just how many
devices control. Uh.
Mm-hmm. Great. And I''m Andrew and I''m uh our marketing expert. Mm-hmm. Mm-hmm.
Yeah, that''s that''s it. Yeah. I will go. That''s fine. Alright. So This one
here, right? Okay. Very nice. Alright. My favourite animal is like A beagle. Um
charac favourite characteristics of it? Is that right? Uh, right, well basically
um high priority for any animal for me is that they be willing to take a lot of
physical affection from their family. And, yeah that they have lots of personality
and uh be fit and in robust good health. So this is blue. Blue beagle. My family''s
beagle. I coulda told you a whole lot more about beagles. Boy, let me tell you.
Impressionist. Alright. Mm. Superb sketch, by the way. Yep. I see a dog in there.
Yep. Now I see a rooster. What kind is it? Is he aware that th it''s his own cha
tail he''s chasing? Hmm. Probably when he was little he got lots of attention
for doing it and has forever been conditioned. ''Kay. Um, can we just go over
that again? Uh, so bas at twel Alright, yeah. Okay. So cost like production cost
is twelve fifty, but selling price is is that wholesale or retail? Like on the
shelf. Our sale our sale anyway. Yeah, okay okay. Okay. Mm-hmm. Alright. Yes.
Mm-hmm. Mm-hmm. Well right away I''m wondering if there''s um th th uh, like with
D_V_D_ players, if there are zones. Um f frequencies or something um as well as
uh characters, um different uh keypad styles and s symbols. Um. I don''t know.
Yeah. Yeah. Yeah. And then a and then al the other thing international is on top
of the price. I''m thinking the price might might appeal to a certain market in
one region, whereas in another it''ll be different, so Just a chara just a characteristic
of the Just Or just like, basic product podi positioning, the twenty five Euro
remote control might be a big hit in London, might not be such a big hit in Greece,
who knows, something like that, yeah. Yep. Right away I''m making some kind of
assumptions about what what information we''re given here, thinking, ''kay trendy
probably means something other than just basic, something other than just standard.
Um so I''m wondering right away, is selling twenty five Euros, is that sort of
the thi is this gonna to be like the premium product kinda thing or Uh-huh. Mm-hmm.
Yep. Yeah, I''d say so, yeah. No. Yeah, yeah. Mm-hmm. Do we have any other background
information on like how that compares to other other Yeah. Mm-hmm. Yeah, interesting
thing about discussing um production of a remote control for me is that l as you
point out, I just don''t think of remote controls as somethin something people
consciously assess in their purchasing habits. It''s just like getting shoelaces
with shoes or something. It just comes along. Do you know what I mean? Like so
sort of like how do you I I mean one one way of looking at it would be, well the
people producing television sets, maybe they have to buy remote controls. Or another
way is maybe people who have T_V_ sets are really fed up with their remote control
and they really want a better one or something. But Right. Right. Okay so Right,
so in function one of the priorities might be to combine as many uses I think
so. Yeah, yeah. Yeah. Well like um, maybe what we could use is a sort of like
a example of a successful other piece technology is palm palm pilots. They''re
gone from being just like little sort of scribble boards to cameras, M_P_ three
players, telephones, everything, agenda. So, like, I wonder if we might add something
new to the to the remote control market, such as the lighting in your house, or
um Yeah, yeah. An Yeah. Like, p personally for me, at home I''ve I''ve combined
the um the audio video of my television set and my D_V_D_ player and my C_D_ player.
So they w all work actually function together but I have different remote controls
for each of them. So it''s sort of ironic that that then they''re in there um
you know, the sound and everything it''s just one system. But each one''s got
its own little part. Mm. Mm. Mm. Mm-hmm. Mm-hmm. Yeah. Yeah. That''s just really
good id Yep. Uh, sure. I remember when the first remote control my my family had
was on a cable. Actually had a cable between it and the T_V_ and big like buttons
that sort of like, like on a blender or something. And um, you know, when I think
about what they are now, it''s better, but actually it''s still kind of, I dunno,
like a massive junky thing on the table. Maybe we could think about how, could
be more, you know, streamlined. S Something like that, yeah. Or whatever would
be technologically reasonable. ''Cause it could b it could it could be that f
it could be that functionally that doesn''t make it any better, but that just
the appeal of of not having You know, these days there''s a r pe things in people''s
homes are becoming more and more like chic, you know. Um, nicer materials and
might be be worth exploring anyway. Okay. Um. Before we wrap up, just to make
sure we''re all on the same page here, um, do we We were given sort of an example
of a coffee machine or something, right? Well, um are we at ma right now on the
assumption that our television remote control may have features which go beyond
the television? Or are we keeping sort of like a a design commitment to television
features? I I don''t know. Yep. Yeah, sure. Okay. Okay, yeah. Okay. Okay. Okay.
Alright.'
model-index:
- name: bart-large-text-summarizer
results:
- task:
type: abstractive-text-summarization
name: Abstractive Text Summarization
dataset:
name: samsum
type: samsum
metrics:
- type: rouge-1
value: 53.8795
name: Validation ROGUE-1
- type: rouge-2
value: 28.4975
name: Validation ROGUE-2
- type: rouge-L
value: 44.1899
name: Validation ROGUE-L
- type: rouge-Lsum
value: 49.4863
name: Validation ROGUE-Lsum
- type: gen-length
value: 30.088
name: Validation ROGUE-Lsum
- type: rouge-1
value: 53.2284
name: Test ROGUE-1
- type: rouge-2
value: 28.184
name: Test ROGUE-2
- type: rouge-L
value: 44.122
name: Test ROGUE-L
- type: rouge-Lsum
value: 49.0301
name: Test ROGUE-Lsum
- type: gen-length
value: 29.9951
name: Test ROGUE-Lsum
- task:
type: summarization
name: Summarization
dataset:
name: bazzhangz/sumdataset
type: bazzhangz/sumdataset
config: bazzhangz--sumdataset
split: train
metrics:
- type: rouge
value: 40.5544
name: ROUGE-1
verified: true
- type: rouge
value: 17.0751
name: ROUGE-2
verified: true
- type: rouge
value: 32.153
name: ROUGE-L
verified: true
- type: rouge
value: 36.4277
name: ROUGE-LSUM
verified: true
- type: loss
value: 2.116729736328125
name: loss
verified: true
- type: gen_len
value: 42.1978
name: gen_len
verified: true
- task:
type: abstractive-text-summarization
name: Abstractive Text Summarization
dataset:
name: xsum
type: xsum
metrics:
- type: rouge-1
value: 35.9078
name: Validation ROGUE-1
- type: rouge-2
value: 14.2497
name: Validation ROGUE-2
- type: rouge-L
value: 28.1421
name: Validation ROGUE-L
- type: rouge-Lsum
value: 28.9826
name: Validation ROGUE-Lsum
- type: gen-length
value: 32.0167
name: Validation ROGUE-Lsum
- type: rouge-1
value: 36.0241
name: Test ROGUE-1
- type: rouge-2
value: 14.3715
name: Test ROGUE-2
- type: rouge-L
value: 28.1968
name: Test ROGUE-L
- type: rouge-Lsum
value: 29.0527
name: Test ROGUE-Lsum
- type: gen-length
value: 31.9933
name: Test ROGUE-Lsum
- task:
type: abstractive-text-summarization
name: Abstractive Text Summarization
dataset:
name: dialogsum
type: dialogsum
metrics:
- type: rouge-1
value: 39.8612
name: Validation ROGUE-1
- type: rouge-2
value: 16.6917
name: Validation ROGUE-2
- type: rouge-L
value: 32.2718
name: Validation ROGUE-L
- type: rouge-Lsum
value: 35.8748
name: Validation ROGUE-Lsum
- type: gen-length
value: 41.726
name: Validation ROGUE-Lsum
- type: rouge-1
value: 36.9608
name: Test ROGUE-1
- type: rouge-2
value: 14.3058
name: Test ROGUE-2
- type: rouge-L
value: 29.3261
name: Test ROGUE-L
- type: rouge-Lsum
value: 32.9
name: Test ROGUE-Lsum
- type: gen-length
value: 43.086
name: Test ROGUE-Lsum
- task:
type: summarization
name: Summarization
dataset:
name: samsum
type: samsum
config: samsum
split: test
metrics:
- type: rouge
value: 53.1878
name: ROUGE-1
verified: true
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value: 28.1666
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- type: rouge
value: 44.117
name: ROUGE-L
verified: true
verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNmNmMzJkYjMxMjhlZDM4YmU3NmI1MDExNzhiYmVhMzEyZGJjNDJkNzczNGQwOTMwNzg2YjU1ZWQ4MDhiMzkxYiIsInZlcnNpb24iOjF9.lcEXK15UqZOdXnPjVqIhFd6o_PLROSIONTRFX5NbwanjEI_MWMLpDh_V0Kpnvs_W0sE6cXh2yoifSYNDA5W7Bw
- type: rouge
value: 49.0094
name: ROUGE-LSUM
verified: true
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value: 1.710614562034607
name: loss
verified: true
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- type: gen_len
value: 29.9951
name: gen_len
verified: true
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---
Model obtained by Fine Tuning 'facebook/bart-large-xsum' using AMI Meeting Corpus, SAMSUM Dataset, DIALOGSUM Dataset, XSUM Dataset!
## Usage
# Example 1
```python
from transformers import pipeline
summarizer = pipeline("summarization", model="Azma-AI/bart-large-text-summarizer")
text = '''The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building, and the tallest structure in Paris. Its base is square, measuring 125 metres (410 ft) on each side. During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest man-made structure in the world, a title it held for 41 years until the Chrysler Building in New York City was finished in 1930. It was the first structure to reach a height of 300 metres. Due to the addition of a broadcasting aerial at the top of the tower in 1957, it is now taller than the Chrysler Building by 5.2 metres (17 ft). Excluding transmitters, the Eiffel Tower is the second tallest free-standing structure in France after the Millau Viaduct.
'''
summarizer(text)
```
# Example 2
```python
from transformers import pipeline
summarizer = pipeline("summarization", model="Azma-AI/bart-large-text-summarizer")
text = '''Bangalore is the capital and the largest city of the Indian state of Karnataka. It has a population of more than 8 million and a metropolitan population of around 11 million, making it the third most populous city and fifth most populous urban agglomeration in India. Located in southern India on the Deccan Plateau, at a height of over 900 m (3,000 ft) above sea level, Bangalore is known for its pleasant climate throughout the year. Its elevation is the highest among the major cities of India.The city's history dates back to around 890 CE, in a stone inscription found at the Nageshwara Temple in Begur, Bangalore. The Begur inscription is written in Halegannada (ancient Kannada), mentions 'Bengaluru Kalaga' (battle of Bengaluru). It was a significant turning point in the history of Bangalore as it bears the earliest reference to the name 'Bengaluru'. In 1537 CE, Kempé Gowdā – a feudal ruler under the Vijayanagara Empire – established a mud fort considered to be the foundation of modern Bangalore and its oldest areas, or petes, which exist to the present day.
After the fall of Vijayanagar empire in 16th century, the Mughals sold Bangalore to Chikkadevaraja Wodeyar (1673–1704), the then ruler of the Kingdom of Mysore for three lakh rupees. When Haider Ali seized control of the Kingdom of Mysore, the administration of Bangalore passed into his hands.
The city was captured by the British East India Company after victory in the Fourth Anglo-Mysore War (1799), who returned administrative control of the city to the Maharaja of Mysore. The old city developed in the dominions of the Maharaja of Mysore and was made capital of the Princely State of Mysore, which existed as a nominally sovereign entity of the British Raj. In 1809, the British shifted their cantonment to Bangalore, outside the old city, and a town grew up around it, which was governed as part of British India. Following India's independence in 1947, Bangalore became the capital of Mysore State, and remained capital when the new Indian state of Karnataka was formed in 1956. The two urban settlements of Bangalore – city and cantonment – which had developed as independent entities merged into a single urban centre in 1949. The existing Kannada name, Bengalūru, was declared the official name of the city in 2006.
Bangalore is widely regarded as the "Silicon Valley of India" (or "IT capital of India") because of its role as the nation's leading information technology (IT) exporter. Indian technological organisations are headquartered in the city. A demographically diverse city, Bangalore is the second fastest-growing major metropolis in India. Recent estimates of the metro economy of its urban area have ranked Bangalore either the fourth- or fifth-most productive metro area of India. As of 2017, Bangalore was home to 7,700 millionaires and 8 billionaires with a total wealth of $320 billion. It is home to many educational and research institutions. Numerous state-owned aerospace and defence organisations are located in the city. The city also houses the Kannada film industry. It was ranked the most liveable Indian city with a population of over a million under the Ease of Living Index 2020.
'''
summarizer(text)
```
# Example 3
```python
from transformers import pipeline
summarizer = pipeline("summarization", model="Azma-AI/bart-large-text-summarizer")
text = '''Hi, I'm David and I'm supposed to be an industrial designer. Um, I just got the project announcement about what the project is. Designing a remote control. That's about it, didn't get anything else. Did you get the same thing? Cool. There's too much gear. Okay. Can't draw. Um. Yeah. Um, well anyway, I don't know, it's just the first animal I can think off the top of my head. Um. Yes. Big reason is 'cause I'm allergic to most animals. Allergic to animal fur, so um fish was a natural choice. Um, yeah, and I kind of like whales. They come in and go eat everything in sight. And they're quite harmless and mild and interesting. Tail's a bit big, I think. It's an after dinner dog then. Hmm. It does make sense from maybe the design point of view 'cause you have more complicated characters like European languages, then you need more buttons. So, possibly. Hmm. Yeah. And you keep losing them. Finding them is really a pain, you know. I mean it's usually quite small, or when you want it right, it slipped behind the couch or it's kicked under the table. You know. Yep. Mm-hmm. I think one factor would be production cost. Because there's a cap there, so um depends on how much you can cram into that price. Um. I think that that's the main factor. Cool.
Okay. Right. Um well this is the kick-off meeting for our our project. Um and um this is just what we're gonna be doing over the next twenty five minutes. Um so first of all, just to kind of make sure that we all know each other, I'm Laura and I'm the project manager. Do you want to introduce yourself again? Okay. Great. Okay. Um so we're designing a new remote control and um Oh I have to record who's here actually. So that's David, Andrew and Craig, isn't it? And you all arrived on time. Um yeah so des uh design a new remote control. Um, as you can see it's supposed to be original, trendy and user friendly. Um so that's kind of our our brief, as it were. Um and so there are three different stages to the design. Um I'm not really sure what what you guys have already received um in your emails. What did you get? Mm-hmm. Is that what everybody got? Okay. Um. So we're gonna have like individual work and then a meeting about it. And repeat that process three times. Um and at this point we get try out the whiteboard over there. Um. So uh you get to draw your favourite animal and sum up your favourite characteristics of it. So who would like to go first? Very good. Mm-hmm. Yeah. Yeah. Right. Lovely. Right. You can take as long over this as you like, because we haven't got an awful lot to discuss. Ok oh we do we do. Don't feel like you're in a rush, anyway. Ach why not We might have to get you up again then. I don't know what mine is. I'm gonna have to think on the spot now. Is that a whale? Ah. Okay. God, I still don't know what I'm gonna write about. Um. I was gonna choose a dog as well. But I'll just draw a different kind of dog. M my favourite animal is my own dog at home. Um That doesn't really look like him, actually. He looks more like a pig, actually. Ah well. Do you? Oh that's very good of you. Uh. Um he's a mixture of uh various things. Um and what do I like about him, um That's just to suggest that his tail wags. Um he's very friendly and cheery and always pleased to see you, and very kind of affectionate and um uh and he's quite quite wee as well so you know he can doesn't take up too much space. Um and uh And he does a funny thing where he chases his tail as well, which is quite amusing, so It is. I think it is. He only does it after he's had his dinner and um he'll just all of a sudden just get up and start chasing his tail 'round the living room. Yeah, so uh Yeah, maybe. Maybe. Right, um where did you find this? Just down here? Yeah. Okay. Um what are we doing next? Uh um. Okay, uh we now need to discuss the project finance. Um so according to the brief um we're gonna be selling this remote control for twenty five Euro, um and we're aiming to make fifty million Euro. Um so we're gonna be selling this on an international scale. And uh we don't want it to cost any more than uh twelve fifty Euros, so fifty percent of the selling price. Sure. All together. Um I dunno. I imagine That's a good question. I imagine it probably is our sale actually because it's probably up to the the um the retailer to uh sell it for whatever price they want. Um. But I I don't know, I mean do you think the fact that it's going to be sold internationally will have a bearing on how we design it at all? Think it will? Um. Hmm. Oh yeah, regions and stuff, yeah. Yeah. Okay. Yeah. Well for a remote control, do you think that will be I suppose it's depends on how complicated our remote control is. Yeah, yeah. Okay. What, just like in terms of like the wealth of the country? Like how much money people have to spend on things like? Aye, I see what you mean, yeah. Marketing. Good marketing thoughts. Oh gosh, I should be writing all this down. Um. Mm. Yeah. Yeah, yeah. Like how much does, you know, a remote control cost. Well twenty five Euro, I mean that's um that's about like eighteen pounds or something, isn't it? Or no, is it as much as that? Sixteen seventeen eighteen pounds. Um, I dunno, I've never bought a remote control, so I don't know how how good a remote control that would get you. Um. But yeah, I suppose it has to look kind of cool and gimmicky. Um right, okay. Let me just scoot on ahead here. Okay. Um well d Does anybody have anything to add to uh to the finance issue at all? Thin No, actually. That would be useful, though, wouldn't it, if you knew like what your money would get you now. Mm-hmm. Yeah, yeah. Oh. Five minutes to end of meeting. Oh, okay. We're a bit behind. Yeah. Right, so do you think that should be like a main design aim of our remote control d you know, do your your satellite and your regular telly and your V_C_R_ and everything? Mm-hmm. Yeah. Or even like, you know, notes about um what you wanna watch. Like you might put in there oh I want to watch such and such and look a Oh that's a good idea. So extra functionalities. Mm-hmm. Hmm. Um okay, uh I'd wel we're gonna have to wrap up pretty quickly in the next couple of minutes. Um I'll just check we've nothing else. Okay. Um so anything else anybody wants to add about what they don't like about remote controls they've used, what they would really like to be part of this new one at all? You keep losing them. Okay. Yeah. W You get those ones where you can, if you like, whistle or make a really high pitched noise they beep. There I mean is that something we'd want to include, do you think? Dunno. Okay maybe. My goodness. Still feels quite primitive. Maybe like a touch screen or something? Okay. Uh-huh, okay. Well I guess that's up to our industrial designer. It looks better. Yeah. Okay. Okay. Right, well um so just to wrap up, the next meeting's gonna be in thirty minutes. So that's about um about ten to twelve by my watch. Um so inbetween now and then, um as the industrial designer, you're gonna be working on you know the actual working design of it so y you know what you're doing there. Um for user interface, technical functions, I guess that's you know like what we've been talking about, what it'll actually do. Um and uh marketing executive, you'll be just thinking about what it actually what, you know, what requirements it has to has to fulfil and you'll all get instructions emailed to you, I guess. Um. Yeah, so it's th the functional design stage is next, I guess. And uh and that's the end of the meeting. So I got that little message a lot sooner than I thought I would, so Mm-hmm. Uh-huh, yeah. Th Okay, well just very quickly 'cause this we're supposed to finish now. Um I guess that's up to us, I mean you probably want some kind of unique selling point of it, so um, you know Yeah. Mm-hmm. Yeah. Okay. Right, okay, we'll that's that's the end of the meeting, then. Um. So, uh thank you all for coming.
Um I'm Craig and I'm User Interface. Yeah. Well, my favourite animal would be a monkey. Then they're small cute and furry, and uh when planet of the apes becomes real, I'm gonna be up there with them. Yeah. I know um My parents went out and bought um remote controls because um they got fed up of having four or five different remote controls for each things the house. So um for them it was just how many devices control. Uh.
Mm-hmm. Great. And I'm Andrew and I'm uh our marketing expert. Mm-hmm. Mm-hmm. Yeah, that's that's it. Yeah. I will go. That's fine. Alright. So This one here, right? Okay. Very nice. Alright. My favourite animal is like A beagle. Um charac favourite characteristics of it? Is that right? Uh, right, well basically um high priority for any animal for me is that they be willing to take a lot of physical affection from their family. And, yeah that they have lots of personality and uh be fit and in robust good health. So this is blue. Blue beagle. My family's beagle. I coulda told you a whole lot more about beagles. Boy, let me tell you. Impressionist. Alright. Mm. Superb sketch, by the way. Yep. I see a dog in there. Yep. Now I see a rooster. What kind is it? Is he aware that th it's his own cha tail he's chasing? Hmm. Probably when he was little he got lots of attention for doing it and has forever been conditioned. 'Kay. Um, can we just go over that again? Uh, so bas at twel Alright, yeah. Okay. So cost like production cost is twelve fifty, but selling price is is that wholesale or retail? Like on the shelf. Our sale our sale anyway. Yeah, okay okay. Okay. Mm-hmm. Alright. Yes. Mm-hmm. Mm-hmm. Well right away I'm wondering if there's um th th uh, like with D_V_D_ players, if there are zones. Um f frequencies or something um as well as uh characters, um different uh keypad styles and s symbols. Um. I don't know. Yeah. Yeah. Yeah. And then a and then al the other thing international is on top of the price. I'm thinking the price might might appeal to a certain market in one region, whereas in another it'll be different, so Just a chara just a characteristic of the Just Or just like, basic product podi positioning, the twenty five Euro remote control might be a big hit in London, might not be such a big hit in Greece, who knows, something like that, yeah. Yep. Right away I'm making some kind of assumptions about what what information we're given here, thinking, 'kay trendy probably means something other than just basic, something other than just standard. Um so I'm wondering right away, is selling twenty five Euros, is that sort of the thi is this gonna to be like the premium product kinda thing or Uh-huh. Mm-hmm. Yep. Yeah, I'd say so, yeah. No. Yeah, yeah. Mm-hmm. Do we have any other background information on like how that compares to other other Yeah. Mm-hmm. Yeah, interesting thing about discussing um production of a remote control for me is that l as you point out, I just don't think of remote controls as somethin something people consciously assess in their purchasing habits. It's just like getting shoelaces with shoes or something. It just comes along. Do you know what I mean? Like so sort of like how do you I I mean one one way of looking at it would be, well the people producing television sets, maybe they have to buy remote controls. Or another way is maybe people who have T_V_ sets are really fed up with their remote control and they really want a better one or something. But Right. Right. Okay so Right, so in function one of the priorities might be to combine as many uses I think so. Yeah, yeah. Yeah. Well like um, maybe what we could use is a sort of like a example of a successful other piece technology is palm palm pilots. They're gone from being just like little sort of scribble boards to cameras, M_P_ three players, telephones, everything, agenda. So, like, I wonder if we might add something new to the to the remote control market, such as the lighting in your house, or um Yeah, yeah. An Yeah. Like, p personally for me, at home I've I've combined the um the audio video of my television set and my D_V_D_ player and my C_D_ player. So they w all work actually function together but I have different remote controls for each of them. So it's sort of ironic that that then they're in there um you know, the sound and everything it's just one system. But each one's got its own little part. Mm. Mm. Mm. Mm-hmm. Mm-hmm. Yeah. Yeah. That's just really good id Yep. Uh, sure. I remember when the first remote control my my family had was on a cable. Actually had a cable between it and the T_V_ and big like buttons that sort of like, like on a blender or something. And um, you know, when I think about what they are now, it's better, but actually it's still kind of, I dunno, like a massive junky thing on the table. Maybe we could think about how, could be more, you know, streamlined. S Something like that, yeah. Or whatever would be technologically reasonable. 'Cause it could b it could it could be that f it could be that functionally that doesn't make it any better, but that just the appeal of of not having You know, these days there's a r pe things in people's homes are becoming more and more like chic, you know. Um, nicer materials and might be be worth exploring anyway. Okay. Um. Before we wrap up, just to make sure we're all on the same page here, um, do we We were given sort of an example of a coffee machine or something, right? Well, um are we at ma right now on the assumption that our television remote control may have features which go beyond the television? Or are we keeping sort of like a a design commitment to television features? I I don't know. Yep. Yeah, sure. Okay. Okay, yeah. Okay. Okay. Okay. Alright.
'''
summarizer(text)
```
# Example 4
```python
from transformers import pipeline
summarizer = pipeline("summarization", model="Azma-AI/bart-large-text-summarizer")
text = '''
Das : Hi and welcome to the a16z podcast. I’m Das, and in this episode, I talk SaaS go-to-market with David Ulevitch and our newest enterprise general partner Kristina Shen. The first half of the podcast looks at how remote work impacts the SaaS go-to-market and what the smartest founders are doing to survive the current crisis. The second half covers pricing approaches and strategy, including how to think about free versus paid trials and navigating the transition to larger accounts. But we start with why it’s easier to move upmarket than down… and the advantage that gives a SaaS startup against incumbents.
David : If you have a cohort of customers that are paying you $10,000 a year for your product, you’re going to find a customer that self-selects and is willing to pay $100,000 a year. Once you get one of those, your organization will figure out how you sell to, how you satisfy and support, customers at that price point and that size. But it’s really hard for a company that sells up market to move down market, because they’ve already baked in all that expensive, heavy lifting sales motion. And so as you go down market with a lower price point, usually, you can’t actually support it.
Das : Does that mean that it’s easier for a company to do this go-to-market if they’re a new startup as opposed to if they’re a pre-existing SaaS?
Kristina : It’s culturally very, very hard to give a product away for free that you’re already charging for. It feels like you’re eating away at your own potential revenue when you do it. So most people who try it end up pulling back very quickly.
David : This is actually one of the key reasons why the bottoms up SaaS motion is just so competitive, and compelling, and so destructive against the traditional sales-driven test motion. If you have that great product and people are choosing to use it, it’s very hard for somebody with a sales-driven motion, and all the cost that’s loaded into that, to be able to compete against it. There are so many markets where initially, we would look at companies and say, “Oh, well, this couldn’t possibly be bottoms up. It has to be sold to the CIO. It has to be sold to the CSO or the CFO.” But in almost every case we’ve been wrong, and there has been a bottoms up motion. The canonical example is Slack. It’s crazy that Slack is a bottoms up company, because you’re talking about corporate messaging, and how could you ever have a messaging solution that only a few people might be using, that only a team might be using? But now it’s just, “Oh, yeah, some people started using it, and then more people started using it, and then everyone had Slack.”
Kristina : I think another classic example is Dropbox versus Box. Both started as bottoms up businesses, try before you buy. But Box quickly found, “Hey, I’d rather sell to IT.” And Dropbox said, “Hey, we’ve got a great freemium motion going.” And they catalyzed their business around referrals and giving away free storage and shared storage in a way that really helped drive their bottoms up business.
Das : It’s a big leap to go from selling to smaller customers to larger customers. How have you seen SaaS companies know or get the timing right on that? Especially since it does seem like that’s really related to scaling your sales force?
Kristina : Don’t try to go from a 100-person company to a 20,000-person company. Start targeting early adopters, maybe they’re late stage pre-IPO companies, then newly IPO’d companies. Starting in tech tends to be a little bit easier because they tend to be early adopters. Going vertical by vertical can be a great strategy as well. Targeting one customer who might be branded in that space, can help brand yourself in that category. And then all their competitors will also want your product if you do a good job. A lot of times people will dedicate a sales rep to each vertical, so that they become really, really knowledgeable in that space, and also build their own brand and reputation and know who are the right customers to target.
Das : So right now, you’ve got a lot more people working remote. Does this move to remote work mean that on-premise software is dying? And is it accelerating the move to software as a service?
Kristina : This remote work and working from home is only going to catalyze more of the conversion from on-premise over to cloud and SaaS. In general, software spend declines 20% during an economic downturn. This happened in ’08, this happened in ’01. But when we look at the last downturn in ’08, SaaS spend actually, for public companies, increased, on average, 10%, which means there’s a 30% spread, which really shows us that there was a huge catalyst from people moving on-premise to SaaS.
David : And as people work remote, the ability to use SaaS tools is much easier than having to VPN back into your corporate network. We’ve been seeing that, inside sales teams have been doing larger and larger deals, essentially moving up market on the inside, without having to engage with field sales teams. In fact, a lot of the new SaaS companies today rather than building out a field team, they have a hybrid team, where people are working and closing deals on the inside and if they had to go out and meet with a customer, they would do that. But by and large, most of it was happening over the phone, over email, and over videoconferencing. And all the deals now, by definition, are gonna be done remote because people can’t go visit their customers in person.
Das : So with bottoms up, did user behavior and buyer behavior change, so the go-to-market evolved? Or did the go-to-market evolve and then you saw user and buyer behavior change? I’m curious with this move to remote work. Is that going to trigger more changes or has the go-to-market enabled that change in user behavior, even though we see that change coming because of a lot of forces outside of the market?
Kristina : I definitely think they are interrelated. But I do think it was a user change that catalyzed everything. We decided that we preferred better software, and we tried a couple products. We were able to purchase off our credit card. And then IT and procurement eventually said, “Wow, everyone’s buying these already, I might as well get a company license and a company deal so I’m not paying as much.” While obviously software vendors had to offer the products that could be self-served, users started to realize they had the power, they wanted to use better software, they paid with their credit cards. And now software vendors are forced to change their go-to-market to actually suit that use case.
Das : If that’s the case that when user behavior has changed, it’s tended to be the catalyzing force of bigger changes in the go-to-market, what are some of the changes you foresee for SaaS because the world has changed to this new reality of remote work and more distributed teams?
David : We’re in a very uncertain economic environment right now. And a couple of things will become very clear over the next 3 to 9 to 15 months — you’re going to find out which SaaS products are absolutely essential to helping a business operate and run, and which ones were just nice to have and may not get renewed. I think on the customer, buying side, you’re very likely to see people push back on big annual commitments and prefer to go month-to-month where they can. Or you’ll see more incentives from SaaS startups to offer discounts for annual contracts. You’re going to see people that might sign an annual contract, but they may not want to pay upfront. They may prefer to meter the cash out ratably over the term of the contract. And as companies had empowered and allowed budget authority to be pushed down in organizations, you’re gonna see that budget authority get pulled back, more scrutiny on spending, and likely a lot of SaaS products not get renewed that turned out to not be essential.
Kristina : I think the smartest founders are making sure they have the runway to continue to exist. And they’re doing that in a couple of ways. They’re preserving cash, and they are making sure that their existing customers are super, super happy, because retaining your customers is so important in this environment. And they’re making sure that they have efficient or profitable customer acquisition. Don’t spend valuable dollars acquiring customers. But acquire customers efficiently that will add to a great existing customer base.
Das : To go into pricing and packaging for SaaS for a moment, what are some of the different pricing approaches that you see SaaS companies taking?
Kristina : The old school way of doing SaaS go-to-market is bundle everything together, make the pricing super complex, so you don’t actually understand what you’re paying for. You’re forced to purchase it because you need one component of the product. New modern SaaS pricing is keep it simple, keep it tied to value, and make sure you’re solving one thing really, really well.
David : You want to make it easy for your customers to give you money. And if your customers don’t understand your pricing, that’s a huge red flag. Sometimes founders will try to over engineer their pricing model.
Kristina : We talk a lot about everything has to be 10X better than the alternatives. But it’s much easier to be 10X better when you solve one thing very, very well, and then have simple pricing around it. I think the most common that most people know about is PEPM or per employee per month, where you’re charging basically for every single seat. Another really common model is the freemium model. So, think about a Dropbox, or an Asana, or a Skype, where it’s trigger based. You try the product for free, but when you hit a certain amount of storage, or a certain amount of users, then it converts over to paid. And then you also have a time trial, where you get the full experience of the product for some limited time period. And then you’re asked if you want to continue using the product to pay. And then there’s pay as go, and particularly, pay as you go as a usage model. So, Slack will say, “Hey, if your users aren’t actually using the product this month, we won’t actually charge you for it.”
David : The example that Kristina made about Slack and users, everybody understands what a user is, and if they’re using the product, they pay for it, and if they’re not using it, they don’t pay for it. That’s a very friendly way to make it easy for your customers to give you money. If Slack came up with a pricing model that was like based on number of messages, or number of API integration calls, the customer would have no idea what that means.
Kristina : There’s also the consumption model. So Twilio only charges you for every SMS text or phone call that you make on the platform any given month. And so they make money or lose money as your usage goes. The pricing is very aligned to your productivity.
David : Generally, those are for products where the usage only goes in one direction. If you think of a company like Databricks, where they’re charging for storage, or Amazon’s S3 service, it is very aligned with the customer, but it also strategically aligns with the business because they know the switching cost is very high, the churn is very low. And generally, in those businesses, you’re only going to store more data, so they can charge based on usage or volume of data.
Kristina : Recently, there’s been a huge trend of payment as a revenue. It’s particularly common in vertical markets where SaaS companies are adding payments as a revenue in addition to their employee or subscription revenue. If you look at Shopify, for example, more than 50% of their revenue is actually payment revenue. They’re making money every single time you purchase something off one of their shopping cart websites.
Das : When you’re working with a founder or a SaaS startup, how have you seen them find the right pricing model for their product, for their market?
Kristina : Step one is just talk to a lot of customers. Try to figure out what is the market pricing for possible alternatives or competitors, understand their pain points and their willingness to pay. And just throw a price out there, because you have to have a starting point in order to actually test and iterate. Particularly in the SMB, or the bottoms up business, you can test and iterate pretty quickly because you have so many data points.
David : I always tell founders, step one is to just go out there and talk to customers. Step two is just double your prices. I don’t think there’s ever been a great company with a great product that’s fallen apart because their pricing was wrong. But a lot of SaaS startup founders really under price, and you don’t want to find out two or three years later that you were 200% underpriced. A very common thing that SaaS companies do, they’ll have the basic package that either is free or low cost, that you can just sign up online for. They’ll have a middle package where they share some pricing, and then they’ll have the enterprise package where you have to contact sales to find out more. And that way they don’t actually have to show the pricing for that third package. And that gives the salespeople the flexibility to adjust pricing on a per deal basis.
Das : When you’re working with companies, why are they underpricing their products?
David : I think it’s psychological. People need to price on value, and they don’t know how much value they’re delivering relative to “Oh, it only cost me $100 a month to provide this service, so I just need to charge $200.” But if it turns out you’re saving your customer $50,000 a year, then you’re wildly underpriced. You have to remember that SaaS is essentially a proxy for outsourced IT. You’re spending money on a SaaS service to not pay to develop something internally, or to have to pay IT to support something that’s more complex on-prem. Software is much cheaper than people, and so generally, the price point can be much higher.
Kristina : And the other thing is your value increases over time. You’re delivering more features, more products, you understand the customer better. It’s the beauty of the SaaS model and cloud model that you can iterate and push code immediately, and the customer immediately sees value. A lot of times people have the same price point from the first customer sold to three years later and the 200th customer. Quite frankly, you’ve delivered so much value along the way that your price point should have gone up. The other thing I’ll say is a lot of people discount per seat pricing a lot as they move up market. We tend to tell people that the best validation of your product having great product market fit is your ability to hold your price point. So while there is some natural discounting on a per seat basis because people do deserve some volume discounting, I would say try to resist that as much as possible.
Das : Especially for a technical founder, it’s so tempting to get in there and fiddle with these knobs. How do you know when it is time to experiment with your pricing and packaging?
David : If you’re looking at your business and you see that you are doing more deals, and they’re closing faster, you should raise your pricing. And you pay attention to how long it takes to close deals and whether the number of deals is staying consistent as you do that. And, at some point, you’re going to find out when you’re losing deals on price. I think a moment where companies have to plan ahead to avoid having to course correct is after they roll out massive pricing and packaging changes, which are pretty natural as companies move up market. But how they navigate that transition to larger accounts, and how they either bring along or move away from those smaller, earlier customers who got them to where they are, tends to be really important because they can get a lot of noise on Twitter, they can get a lot of blowback from their customers. So Zendesk is a company where they rolled out a major packaging change. And when they rolled it out, they hadn’t planned on grandfathering in their early customers. They got a lot of pushback, and very quickly, they put out a blog post and said, “We hear what you’re saying, we appreciate you building the business that we’ve become today. We do need to have a package for the future. But all the people that have been customers so far will be grandfathered in for at least a period of time into the old model.”
Kristina : If you iterate pricing constantly, you don’t really have this problem because your customers will be used to pricing changes. You normally pair them with new features, and it all kind of works out. But if you have to go through a big grandfather change, I tend to lean towards treating your early customers really, really well. They adopted when you weren’t a big company yet. They probably co-built the product with you in many ways. And so, it’s great to get more dollars out of your customer base, but treat your early customers well.
Das : Are there any other failure modes that you see startups really falling into around pricing and packaging or any common mistakes that they make?
David : I think a lot of founders don’t always map out the cost or model of their pricing and their product relative to their cost of actually doing sales and marketing and customer acquisition.
Kristina : Inside sales is so popular in Silicon Valley. When you’re selling more to an SMB or mid-market type customer, the expectation is that you’re educating and helping the prospective customer over the phone. And so, you’re not expected to be as high touch. But 5K is almost the minimum price point you need to sell to the SMB with an inside sales team in order to pay for the outbound costs and all the conversions, because there is typically a team that sits around the quota carrying rep. And so, price matching — how much your price point is compared to what your go-to-market motion is — matters a lot. Other big failure modes that I see, people guess the ramp time of a sales rep wrong. And ramp time really ties to the segment of customer you’re selling into. It tends be that if you’re selling into the enterprise, the ramp time for sales reps, because sales cycles are so long, tend to be much longer as well. They could be six months plus, could be a year. While if you’re selling more into SMB or mid-market, the ramp time to get a rep up and running can be much shorter, three to six months. Because the sales cycles are shorter, they just iterate much faster, and they ramp up much more quickly.
David : The other thing that people have to understand is that sales velocity is a really important component to figuring out how many reps you should be hiring, whether they should be inside reps or field reps. If it takes you 90 days to close a deal, that can’t be a $5,000 a year deal, that has to be a $50,000 or even $150,000 a year deal.
Das : Kristina, I know you’ve done a lot of work with metrics. So how do those play in?
Kristina : Probably the one way to sum it all together is how many months does it take to pay back customer acquisition cost. Very commonly within the SaaS world, we talk about a 12-month CAC payback. We typically want to see for every dollar you spend on sales and marketing, you get a dollar back within a year. That means you can tweak the inputs any way you want. Let’s say that doing paid acquisition is really effective for you. Then, you can spend proportionally more on paid acquisition and less on sales reps. Vice versa, if you have a great inbound engine, you actually can hire a lot more sales reps and spend more on sales headcount. With all formulas, it’s a guide rail, so if you have customers that retain really, really well, let’s say you’re selling to the enterprise, and you’ve got a 90% or 95% annual retention rate, then your CAC payback could be between 12 and 24 months. But let’s say you’re selling to the SMB and churn is 2% or 3% monthly, which ends up being like 80% to 90% annual retention. Then, because your customer is less sticky, I would recommend looking at a CAC payback of 6 to 12 months.
Das : How should you think about doing a free trial versus a paid trial?
David : On the one hand, the bottoms up motion where people can try essentially a full version of a product before they buy it is extremely powerful. On the other hand, I’ve started to try to think about how I advise companies, when they are thinking about a free trial for something that might cost $100,000 or $200,000 a year? Do we do a paid pilot that has some sort of contractual obligation that if we meet then turns into a commercial engagement?
Kristina : I do think the beauty of the bottoms up business is that you can get people to try the entire experience of the product for free, and they fall in love with it, and a certain percentage will convert. And that works really, really well for products that can self-serve. When you start moving up market to more complex products, the challenge with trials is it takes work to actually implement the product, whether it be integrations, IT has to give access, etc. You lose that self-serve ability, which is so amazing in the trial. And so, I tend to be more in the camp of paid trials, if it costs you money to actually deploy the trial. And when you’re selling to bigger customers, they associate value when they have to pay. Once a customer has to pay you, then they feel a need to make the project successful and thus they will onboard, schedule things, give you data and access.
David : If you can get to a point where you get the customer to do that paid pilot, such that the only difference between a pilot and an actual customer is just the signing of a contract, that’s very powerful. Now, that does force you to have a really good pre-sales motion to make sure that you can deliver on the promise you’ve made your customers. When companies don’t have a great product, and they paper over it with professional services and sales engineering and post-sales support, that paid pilot thing doesn’t work because the experience isn’t good enough. So, it really is incumbent on the SaaS company that does a paid pilot to make sure that they are able to deliver on that experience.
Kristina : And one emerging trend recently is people signing an annual contract with a one or three month out, as a replacement to the paid pilot. Because it’s the best of both worlds, the SaaS company that’s selling the product gets a higher level of commitment. And the customer gets the optionality of opting out in the same way as a trial without any clawback. It really comes down to where procurement falls. Sometimes procurement is at the beginning of that decision, which makes it more like an annual contract. Sometimes procurement is at the one or three month opt-out period, which means the customer already has a great experience, loves the product, and it is an easier way to convert procurements to actually sign on…
David : And that is a really good segue into renewals. I always tell founders, you might have this subscription business, but it’s not a recurring revenue business until the second year when the revenue actually recurs. I think you really have the first three months to get a customer up and running and happy. And if they’re not, you then have about three months to fix it. And if all that works out, then the remaining six months of the contract can be focused on upsell and expansion.
Das : Awesome. Thank you, Kristina. Thank you, David.
Kristina : Thanks so much for having us. This was fun.
David : Yeah, a lot of fun, great topics, and our favorite thing to talk about.
'''
summarizer(text)
``` |
tiiuae/falcon-rw-1b | tiiuae | "2023-07-12T21:34:11Z" | 18,981 | 98 | transformers | [
"transformers",
"pytorch",
"falcon",
"text-generation",
"custom_code",
"en",
"dataset:tiiuae/falcon-refinedweb",
"arxiv:2306.01116",
"arxiv:2005.14165",
"arxiv:2108.12409",
"arxiv:2205.14135",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-04-26T09:25:36Z" | ---
datasets:
- tiiuae/falcon-refinedweb
language:
- en
inference: false
license: apache-2.0
---
# Falcon-RW-1B
**Falcon-RW-1B is a 1B parameters causal decoder-only model built by [TII](https://www.tii.ae) and trained on 350B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb). It is made available under the Apache 2.0 license.**
See the 📓 [paper on arXiv](https://arxiv.org/abs/2306.01116) for more details.
RefinedWeb is a high-quality web dataset built by leveraging stringent filtering and large-scale deduplication. Falcon-RW-1B, trained on RefinedWeb only, matches or outperforms comparable models trained on curated data.
⚠️ Falcon is now available as a core model in the `transformers` library! To use the in-library version, please install the latest version of `transformers` with `pip install git+https://github.com/huggingface/transformers.git`, then simply remove the `trust_remote_code=True` argument from `from_pretrained()`.
⚠️ This model is intended for use as a **research artifact**, to study the influence of training on web data alone. **If you are interested in state-of-the-art models, we recommend using Falcon-[7B](https://huggingface.co/tiiuae/falcon-7b)/[40B](https://huggingface.co/tiiuae/falcon-40b), both trained on >1,000 billion tokens.**
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model = "tiiuae/falcon-rw-1b"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
torch_dtype=torch.bfloat16,
device_map="auto",
)
sequences = pipeline(
"Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
max_length=200,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
print(f"Result: {seq['generated_text']}")
```
💥 **Falcon LLMs require PyTorch 2.0 for use with `transformers`!**
# Model Card for Falcon-RW-1B
## Model Details
### Model Description
- **Developed by:** [https://www.tii.ae](https://www.tii.ae);
- **Model type:** Causal decoder-only;
- **Language(s) (NLP):** English;
- **License:** Apache 2.0.
### Model Source
- **Paper:** [https://arxiv.org/abs/2306.01116](https://arxiv.org/abs/2306.01116).
## Uses
### Direct Use
Research on large language models, specifically the influence of adequately filtered and deduplicated web data on the properties of large language models (fairness, safety, limitations, capabilities, etc.).
### Out-of-Scope Use
Production use without adequate assessment of risks and mitigation; any use cases which may be considered irresponsible or harmful.
Broadly speaking, we would recommend Falcon-[7B](https://huggingface.co/tiiuae/falcon-7b)/[40B](https://huggingface.co/tiiuae/falcon-40b) for any use not directly related to research on web data pipelines.
## Bias, Risks, and Limitations
Falcon-RW-1B is trained on English data only, and will not generalize appropriately to other languages. Furthermore, as it is trained on a large-scale corpora representative of the web, it will carry the stereotypes and biases commonly encountered online.
### Recommendations
We recommend users of Falcon-RW-1B to consider finetuning it for the specific set of tasks of interest, and for guardrails and appropriate precautions to be taken for any production use.
## How to Get Started with the Model
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model = "tiiuae/falcon-rw-1b"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
torch_dtype=torch.bfloat16,
device_map="auto",
)
sequences = pipeline(
"Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
max_length=200,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
print(f"Result: {seq['generated_text']}")
```
## Training Details
### Training Data
Falcon-RW-1B was trained on 350B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb), a high-quality filtered and deduplicated web dataset. The data was tokenized with the GPT-2 tokenizer.
### Training Procedure
Falcon-RW-1B was trained on 32 A100 40GB GPUs, using only data parallelism with ZeRO.
#### Training Hyperparameters
Hyperparameters were adapted from the GPT-3 paper ([Brown et al., 2020](https://arxiv.org/abs/2005.14165)).
| **Hyperparameter** | **Value** | **Comment** |
|--------------------|------------|-------------------------------------------|
| Precision | `bfloat16` | |
| Optimizer | AdamW | |
| Learning rate | 2e-4 | 500M tokens warm-up, cosine decay to 2e-5 |
| Weight decay | 1e-1 | |
| Batch size | 512 | 4B tokens ramp-up |
#### Speeds, Sizes, Times
Training happened in early December 2022 and took about six days.
## Evaluation
See the 📓 [paper on arXiv](https://arxiv.org/abs/2306.01116) for in-depth evaluation.
## Technical Specifications
### Model Architecture and Objective
Falcon-RW-1B is a causal decoder-only model trained on a causal language modeling task (i.e., predict the next token).
The architecture is adapted from the GPT-3 paper ([Brown et al., 2020](https://arxiv.org/abs/2005.14165)), but uses ALiBi ([Ofir et al., 2021](https://arxiv.org/abs/2108.12409)) and FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135)).
| **Hyperparameter** | **Value** | **Comment** |
|--------------------|-----------|----------------------------------------|
| Layers | 24 | |
| `d_model` | 2048 | |
| `head_dim` | 64 | Reduced to optimise for FlashAttention |
| Vocabulary | 50304 | |
| Sequence length | 2048 | |
### Compute Infrastructure
#### Hardware
Falcon-RW-1B was trained on AWS SageMaker, on 32 A100 40GB GPUs in P4d instances.
#### Software
Falcon-RW-1B was trained a custom distributed training codebase, Gigatron. It uses a 3D parallelism approach combined with ZeRO and high-performance Triton kernels (FlashAttention, etc.)
## Citation
```
@article{refinedweb,
title={The {R}efined{W}eb dataset for {F}alcon {LLM}: outperforming curated corpora with web data, and web data only},
author={Guilherme Penedo and Quentin Malartic and Daniel Hesslow and Ruxandra Cojocaru and Alessandro Cappelli and Hamza Alobeidli and Baptiste Pannier and Ebtesam Almazrouei and Julien Launay},
journal={arXiv preprint arXiv:2306.01116},
eprint={2306.01116},
eprinttype = {arXiv},
url={https://arxiv.org/abs/2306.01116},
year={2023}
}
```
## Contact
[email protected]
|
Yntec/FilmGirlRemix | Yntec | "2024-06-02T00:20:55Z" | 18,971 | 3 | diffusers | [
"diffusers",
"safetensors",
"Photorealism",
"Film",
"Simple prompts",
"22h",
"LEOSAM",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | "2024-06-01T23:39:00Z" | ---
license: creativeml-openrail-m
library_name: diffusers
pipeline_tag: text-to-image
tags:
- Photorealism
- Film
- Simple prompts
- 22h
- LEOSAM
- stable-diffusion
- stable-diffusion-diffusers
- diffusers
- text-to-image
---
# Film Girl Remix
FilmGirlUltra (via the Film model) mixed with Vintedois optimized for the EulerAncentralDiscreteScheduler. There's many images that only this scheduler will bring you, but it tends to soften everything and simplify backgrounds. Not anymore!
Samples and prompts (scroll down for more):
(Click for larger)
Top left: pretty cute little girl as Marie Antoinette playing on drums in bedroom. precious eyes. short smile
Top right: burger
Bottom left: greek city landscape
Bottom right: tiny baby girl. anime eyes screenshot.
(Click for larger)
Top left: photo of an old man in a jungle, looking at the fedora
Top right: ladies as close Catwoman and Harley Quinn from the 2004 movie. elegant, medieval in cool armor, action scene, in a wonderland land
Bottom left: a beautiful young girl In front of the cabin, the country, by Artgerm Lau and Krenz Cushart，hyperdetailed, trending on artstation, trending on deviantart
Bottom right: kneeling chipmunk knight, portrait, finely detailed armor, intricate design
Original pages:
https://civitai.com/models/33208/leosams-filmgirl-ultra
https://huggingface.co/22h/vintedois-diffusion-v0-1
https://huggingface.co/Yntec/Film
# Recipe:
- SuperMerger Weight Sum Use MBW 1,0,0,0,0,0,0,1,1,1,1,1,1,0,1,1,1,1,1,1,0,0,0,0,0,0
Model A:
Vintedois
Model B:
Film
Output:
FilmGirlRemix |
EleutherAI/pythia-2.8b | EleutherAI | "2023-06-09T00:35:37Z" | 18,969 | 26 | transformers | [
"transformers",
"pytorch",
"safetensors",
"gpt_neox",
"text-generation",
"causal-lm",
"pythia",
"en",
"dataset:EleutherAI/pile",
"arxiv:2304.01373",
"arxiv:2101.00027",
"arxiv:2201.07311",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-02-13T14:37:12Z" | ---
language:
- en
tags:
- pytorch
- causal-lm
- pythia
license: apache-2.0
datasets:
- EleutherAI/pile
---
The *Pythia Scaling Suite* is a collection of models developed to facilitate
interpretability research [(see paper)](https://arxiv.org/pdf/2304.01373.pdf).
It contains two sets of eight models of sizes
70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two
models: one trained on the Pile, and one trained on the Pile after the dataset
has been globally deduplicated. All 8 model sizes are trained on the exact
same data, in the exact same order. We also provide 154 intermediate
checkpoints per model, hosted on Hugging Face as branches.
The Pythia model suite was deliberately designed to promote scientific
research on large language models, especially interpretability research.
Despite not centering downstream performance as a design goal, we find the
models <a href="#evaluations">match or exceed</a> the performance of
similar and same-sized models, such as those in the OPT and GPT-Neo suites.
<details>
<summary style="font-weight:600">Details on previous early release and naming convention.</summary>
Previously, we released an early version of the Pythia suite to the public.
However, we decided to retrain the model suite to address a few hyperparameter
discrepancies. This model card <a href="#changelog">lists the changes</a>;
see appendix B in the Pythia paper for further discussion. We found no
difference in benchmark performance between the two Pythia versions.
The old models are
[still available](https://huggingface.co/models?other=pythia_v0), but we
suggest the retrained suite if you are just starting to use Pythia.<br>
**This is the current release.**
Please note that all models in the *Pythia* suite were renamed in January
2023. For clarity, a <a href="#naming-convention-and-parameter-count">table
comparing the old and new names</a> is provided in this model card, together
with exact parameter counts.
</details>
<br>
# Pythia-2.8B
## Model Details
- Developed by: [EleutherAI](http://eleuther.ai)
- Model type: Transformer-based Language Model
- Language: English
- Learn more: [Pythia's GitHub repository](https://github.com/EleutherAI/pythia)
for training procedure, config files, and details on how to use.
[See paper](https://arxiv.org/pdf/2304.01373.pdf) for more evals and implementation
details.
- Library: [GPT-NeoX](https://github.com/EleutherAI/gpt-neox)
- License: Apache 2.0
- Contact: to ask questions about this model, join the [EleutherAI
Discord](https://discord.gg/zBGx3azzUn), and post them in `#release-discussion`.
Please read the existing *Pythia* documentation before asking about it in the
EleutherAI Discord. For general correspondence: [contact@eleuther.
ai](mailto:[email protected]).
<figure>
| Pythia model | Non-Embedding Params | Layers | Model Dim | Heads | Batch Size | Learning Rate | Equivalent Models |
| -----------: | -------------------: | :----: | :-------: | :---: | :--------: | :-------------------: | :--------------------: |
| 70M | 18,915,328 | 6 | 512 | 8 | 2M | 1.0 x 10<sup>-3</sup> | — |
| 160M | 85,056,000 | 12 | 768 | 12 | 2M | 6.0 x 10<sup>-4</sup> | GPT-Neo 125M, OPT-125M |
| 410M | 302,311,424 | 24 | 1024 | 16 | 2M | 3.0 x 10<sup>-4</sup> | OPT-350M |
| 1.0B | 805,736,448 | 16 | 2048 | 8 | 2M | 3.0 x 10<sup>-4</sup> | — |
| 1.4B | 1,208,602,624 | 24 | 2048 | 16 | 2M | 2.0 x 10<sup>-4</sup> | GPT-Neo 1.3B, OPT-1.3B |
| 2.8B | 2,517,652,480 | 32 | 2560 | 32 | 2M | 1.6 x 10<sup>-4</sup> | GPT-Neo 2.7B, OPT-2.7B |
| 6.9B | 6,444,163,072 | 32 | 4096 | 32 | 2M | 1.2 x 10<sup>-4</sup> | OPT-6.7B |
| 12B | 11,327,027,200 | 36 | 5120 | 40 | 2M | 1.2 x 10<sup>-4</sup> | — |
<figcaption>Engineering details for the <i>Pythia Suite</i>. Deduped and
non-deduped models of a given size have the same hyperparameters. “Equivalent”
models have <b>exactly</b> the same architecture, and the same number of
non-embedding parameters.</figcaption>
</figure>
## Uses and Limitations
### Intended Use
The primary intended use of Pythia is research on the behavior, functionality,
and limitations of large language models. This suite is intended to provide
a controlled setting for performing scientific experiments. We also provide
154 checkpoints per model: initial `step0`, 10 log-spaced checkpoints
`step{1,2,4...512}`, and 143 evenly-spaced checkpoints from `step1000` to
`step143000`. These checkpoints are hosted on Hugging Face as branches. Note
that branch `143000` corresponds exactly to the model checkpoint on the `main`
branch of each model.
You may also further fine-tune and adapt Pythia-2.8B for deployment,
as long as your use is in accordance with the Apache 2.0 license. Pythia
models work with the Hugging Face [Transformers
Library](https://huggingface.co/docs/transformers/index). If you decide to use
pre-trained Pythia-2.8B as a basis for your fine-tuned model, please
conduct your own risk and bias assessment.
### Out-of-scope use
The Pythia Suite is **not** intended for deployment. It is not a in itself
a product and cannot be used for human-facing interactions. For example,
the model may generate harmful or offensive text. Please evaluate the risks
associated with your particular use case.
Pythia models are English-language only, and are not suitable for translation
or generating text in other languages.
Pythia-2.8B has not been fine-tuned for downstream contexts in which
language models are commonly deployed, such as writing genre prose,
or commercial chatbots. This means Pythia-2.8B will **not**
respond to a given prompt the way a product like ChatGPT does. This is because,
unlike this model, ChatGPT was fine-tuned using methods such as Reinforcement
Learning from Human Feedback (RLHF) to better “follow” human instructions.
### Limitations and biases
The core functionality of a large language model is to take a string of text
and predict the next token. The token used by the model need not produce the
most “accurate” text. Never rely on Pythia-2.8B to produce factually accurate
output.
This model was trained on [the Pile](https://pile.eleuther.ai/), a dataset
known to contain profanity and texts that are lewd or otherwise offensive.
See [Section 6 of the Pile paper](https://arxiv.org/abs/2101.00027) for a
discussion of documented biases with regards to gender, religion, and race.
Pythia-2.8B may produce socially unacceptable or undesirable text, *even if*
the prompt itself does not include anything explicitly offensive.
If you plan on using text generated through, for example, the Hosted Inference
API, we recommend having a human curate the outputs of this language model
before presenting it to other people. Please inform your audience that the
text was generated by Pythia-2.8B.
### Quickstart
Pythia models can be loaded and used via the following code, demonstrated here
for the third `pythia-70m-deduped` checkpoint:
```python
from transformers import GPTNeoXForCausalLM, AutoTokenizer
model = GPTNeoXForCausalLM.from_pretrained(
"EleutherAI/pythia-70m-deduped",
revision="step3000",
cache_dir="./pythia-70m-deduped/step3000",
)
tokenizer = AutoTokenizer.from_pretrained(
"EleutherAI/pythia-70m-deduped",
revision="step3000",
cache_dir="./pythia-70m-deduped/step3000",
)
inputs = tokenizer("Hello, I am", return_tensors="pt")
tokens = model.generate(**inputs)
tokenizer.decode(tokens[0])
```
Revision/branch `step143000` corresponds exactly to the model checkpoint on
the `main` branch of each model.<br>
For more information on how to use all Pythia models, see [documentation on
GitHub](https://github.com/EleutherAI/pythia).
## Training
### Training data
[The Pile](https://pile.eleuther.ai/) is a 825GiB general-purpose dataset in
English. It was created by EleutherAI specifically for training large language
models. It contains texts from 22 diverse sources, roughly broken down into
five categories: academic writing (e.g. arXiv), internet (e.g. CommonCrawl),
prose (e.g. Project Gutenberg), dialogue (e.g. YouTube subtitles), and
miscellaneous (e.g. GitHub, Enron Emails). See [the Pile
paper](https://arxiv.org/abs/2101.00027) for a breakdown of all data sources,
methodology, and a discussion of ethical implications. Consult [the
datasheet](https://arxiv.org/abs/2201.07311) for more detailed documentation
about the Pile and its component datasets. The Pile can be downloaded from
the [official website](https://pile.eleuther.ai/), or from a [community
mirror](https://the-eye.eu/public/AI/pile/).<br>
The Pile was **not** deduplicated before being used to train Pythia-2.8B.
### Training procedure
All models were trained on the exact same data, in the exact same order. Each
model saw 299,892,736,000 tokens during training, and 143 checkpoints for each
model are saved every 2,097,152,000 tokens, spaced evenly throughout training,
from `step1000` to `step143000` (which is the same as `main`). In addition, we
also provide frequent early checkpoints: `step0` and `step{1,2,4...512}`.
This corresponds to training for just under 1 epoch on the Pile for
non-deduplicated models, and about 1.5 epochs on the deduplicated Pile.
All *Pythia* models trained for 143000 steps at a batch size
of 2M (2,097,152 tokens).<br>
See [GitHub](https://github.com/EleutherAI/pythia) for more details on training
procedure, including [how to reproduce
it](https://github.com/EleutherAI/pythia/blob/main/README.md#reproducing-training).<br>
Pythia uses the same tokenizer as [GPT-NeoX-
20B](https://huggingface.co/EleutherAI/gpt-neox-20b).
## Evaluations
All 16 *Pythia* models were evaluated using the [LM Evaluation
Harness](https://github.com/EleutherAI/lm-evaluation-harness). You can access
the results by model and step at `results/json/*` in the [GitHub
repository](https://github.com/EleutherAI/pythia/tree/main/results/json/).<br>
Expand the sections below to see plots of evaluation results for all
Pythia and Pythia-deduped models compared with OPT and BLOOM.
<details>
<summary>LAMBADA – OpenAI</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/lambada_openai_v1.png" style="width:auto"/>
</details>
<details>
<summary>Physical Interaction: Question Answering (PIQA)</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/piqa_v1.png" style="width:auto"/>
</details>
<details>
<summary>WinoGrande</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/winogrande_v1.png" style="width:auto"/>
</details>
<details>
<summary>AI2 Reasoning Challenge—Easy Set</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/arc_easy_v1.png" style="width:auto"/>
</details>
<details>
<summary>SciQ</summary>
<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/sciq_v1.png" style="width:auto"/>
</details>
## Changelog
This section compares differences between previously released
[Pythia v0](https://huggingface.co/models?other=pythia_v0) and the current
models. See Appendix B of the Pythia paper for further discussion of these
changes and the motivation behind them. We found that retraining Pythia had no
impact on benchmark performance.
- All model sizes are now trained with uniform batch size of 2M tokens.
Previously, the models of size 160M, 410M, and 1.4B parameters were trained
with batch sizes of 4M tokens.
- We added checkpoints at initialization (step 0) and steps {1,2,4,8,16,32,64,
128,256,512} in addition to every 1000 training steps.
- Flash Attention was used in the new retrained suite.
- We remedied a minor inconsistency that existed in the original suite: all
models of size 2.8B parameters or smaller had a learning rate (LR) schedule
which decayed to a minimum LR of 10% the starting LR rate, but the 6.9B and
12B models all used an LR schedule which decayed to a minimum LR of 0. In
the redone training runs, we rectified this inconsistency: all models now were
trained with LR decaying to a minimum of 0.1× their maximum LR.
### Naming convention and parameter count
*Pythia* models were renamed in January 2023. It is possible that the old
naming convention still persists in some documentation by accident. The
current naming convention (70M, 160M, etc.) is based on total parameter count.
<figure style="width:32em">
| current Pythia suffix | old suffix | total params | non-embedding params |
| --------------------: | ---------: | -------------: | -------------------: |
| 70M | 19M | 70,426,624 | 18,915,328 |
| 160M | 125M | 162,322,944 | 85,056,000 |
| 410M | 350M | 405,334,016 | 302,311,424 |
| 1B | 800M | 1,011,781,632 | 805,736,448 |
| 1.4B | 1.3B | 1,414,647,808 | 1,208,602,624 |
| 2.8B | 2.7B | 2,775,208,960 | 2,517,652,480 |
| 6.9B | 6.7B | 6,857,302,016 | 6,444,163,072 |
| 12B | 13B | 11,846,072,320 | 11,327,027,200 |
</figure> |
timm/vit_large_patch14_clip_336.openai | timm | "2024-02-10T23:42:14Z" | 18,958 | 0 | open_clip | [
"open_clip",
"zero-shot-image-classification",
"clip",
"arxiv:2103.00020",
"arxiv:1908.04913",
"license:apache-2.0",
"region:us"
] | zero-shot-image-classification | "2023-04-10T18:43:24Z" | ---
license: apache-2.0
library_name: open_clip
tags:
- zero-shot-image-classification
- clip
---
# Model card for vit_large_patch14_clip_336.openai
# CLIP (OpenAI model for timm)
## Model Details
The CLIP model was developed by researchers at OpenAI to learn about what contributes to robustness in computer vision tasks. The model was also developed to test the ability of models to generalize to arbitrary image classification tasks in a zero-shot manner. It was not developed for general model deployment - to deploy models like CLIP, researchers will first need to carefully study their capabilities in relation to the specific context they’re being deployed within.
This instance of the CLIP model is intended for loading in
* `timm` (https://github.com/rwightman/pytorch-image-models) and
* `OpenCLIP` (https://github.com/mlfoundations/open_clip) libraries.
Please see https://huggingface.co/openai/clip-vit-large-patch14-336 for use in Hugging Face Transformers.
### Model Date
January 2021
### Model Type
The model uses a ViT-L/14 (336x336) Transformer architecture as an image encoder and uses a masked self-attention Transformer as a text encoder. These encoders are trained to maximize the similarity of (image, text) pairs via a contrastive loss.
The original implementation had two variants: one using a ResNet image encoder and the other using a Vision Transformer. This repository has the variant with the Vision Transformer.
### Documents
- [Blog Post](https://openai.com/blog/clip/)
- [CLIP Paper](https://arxiv.org/abs/2103.00020)
## Model Use
### Intended Use
The model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification. We also hope it can be used for interdisciplinary studies of the potential impact of such models - the CLIP paper includes a discussion of potential downstream impacts to provide an example for this sort of analysis.
#### Primary intended uses
The primary intended users of these models are AI researchers.
We primarily imagine the model will be used by researchers to better understand robustness, generalization, and other capabilities, biases, and constraints of computer vision models.
### Out-of-Scope Use Cases
**Any** deployed use case of the model - whether commercial or not - is currently out of scope. Non-deployed use cases such as image search in a constrained environment, are also not recommended unless there is thorough in-domain testing of the model with a specific, fixed class taxonomy. This is because our safety assessment demonstrated a high need for task specific testing especially given the variability of CLIP’s performance with different class taxonomies. This makes untested and unconstrained deployment of the model in any use case currently potentially harmful.
Certain use cases which would fall under the domain of surveillance and facial recognition are always out-of-scope regardless of performance of the model. This is because the use of artificial intelligence for tasks such as these can be premature currently given the lack of testing norms and checks to ensure its fair use.
Since the model has not been purposefully trained in or evaluated on any languages other than English, its use should be limited to English language use cases.
## Data
The model was trained on publicly available image-caption data. This was done through a combination of crawling a handful of websites and using commonly-used pre-existing image datasets such as [YFCC100M](http://projects.dfki.uni-kl.de/yfcc100m/). A large portion of the data comes from our crawling of the internet. This means that the data is more representative of people and societies most connected to the internet which tend to skew towards more developed nations, and younger, male users.
### Data Mission Statement
Our goal with building this dataset was to test out robustness and generalizability in computer vision tasks. As a result, the focus was on gathering large quantities of data from different publicly-available internet data sources. The data was gathered in a mostly non-interventionist manner. However, we only crawled websites that had policies against excessively violent and adult images and allowed us to filter out such content. We do not intend for this dataset to be used as the basis for any commercial or deployed model and will not be releasing the dataset.
## Limitations
CLIP and our analysis of it have a number of limitations. CLIP currently struggles with respect to certain tasks such as fine grained classification and counting objects. CLIP also poses issues with regards to fairness and bias which we discuss in the paper and briefly in the next section. Additionally, our approach to testing CLIP also has an important limitation- in many cases we have used linear probes to evaluate the performance of CLIP and there is evidence suggesting that linear probes can underestimate model performance.
### Bias and Fairness
We find that the performance of CLIP - and the specific biases it exhibits - can depend significantly on class design and the choices one makes for categories to include and exclude. We tested the risk of certain kinds of denigration with CLIP by classifying images of people from [Fairface](https://arxiv.org/abs/1908.04913) into crime-related and non-human animal categories. We found significant disparities with respect to race and gender. Additionally, we found that these disparities could shift based on how the classes were constructed. (Details captured in the Broader Impacts Section in the paper).
We also tested the performance of CLIP on gender, race and age classification using the Fairface dataset (We default to using race categories as they are constructed in the Fairface dataset.) in order to assess quality of performance across different demographics. We found accuracy >96% across all races for gender classification with ‘Middle Eastern’ having the highest accuracy (98.4%) and ‘White’ having the lowest (96.5%). Additionally, CLIP averaged ~93% for racial classification and ~63% for age classification. Our use of evaluations to test for gender, race and age classification as well as denigration harms is simply to evaluate performance of the model across people and surface potential risks and not to demonstrate an endorsement/enthusiasm for such tasks.
|
lighteternal/wav2vec2-large-xlsr-53-greek | lighteternal | "2022-03-26T10:12:37Z" | 18,956 | 8 | transformers | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"hf-asr-leaderboard",
"speech",
"xlsr-fine-tuning-week",
"el",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | "2022-03-02T23:29:05Z" | ---
language: el
datasets:
- common_voice
tags:
- audio
- hf-asr-leaderboard
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Greek by Lighteternal
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: CommonVoice (EL), CSS10 (EL)
type: CCS10 + mozilla-foundation/common_voice_7_0
args: el
metrics:
- name: Test WER
type: wer
value: 10.497628
- name: Test CER
type: cer
value: 2.875260
---
# Greek (el) version of the XLSR-Wav2Vec2 automatic speech recognition (ASR) model
### By the Hellenic Army Academy and the Technical University of Crete
* language: el
* licence: apache-2.0
* dataset: CommonVoice (EL), 364MB: https://commonvoice.mozilla.org/el/datasets + CSS10 (EL), 1.22GB: https://github.com/Kyubyong/css10
* model: XLSR-Wav2Vec2, trained for 50 epochs
* metrics: Word Error Rate (WER)
## Model description
UPDATE: We repeated the fine-tuning process using an additional 1.22GB dataset from CSS10.
Wav2Vec2 is a pretrained model for Automatic Speech Recognition (ASR) and was released in September 2020 by Alexei Baevski, Michael Auli, and Alex Conneau. Soon after the superior performance of Wav2Vec2 was demonstrated on the English ASR dataset LibriSpeech, Facebook AI presented XLSR-Wav2Vec2. XLSR stands for cross-lingual speech representations and refers to XLSR-Wav2Vec2`s ability to learn speech representations that are useful across multiple languages.
Similar to Wav2Vec2, XLSR-Wav2Vec2 learns powerful speech representations from hundreds of thousands of hours of speech in more than 50 languages of unlabeled speech. Similar, to BERT's masked language modeling, the model learns contextualized speech representations by randomly masking feature vectors before passing them to a transformer network.
This model was trained for 50 epochs on a single NVIDIA RTX 3080, for aprox. 8hrs.
## How to use for inference:
For live demo, make sure that speech files are sampled at 16kHz.
Instructions to test on CommonVoice extracts are provided in the ASR_Inference.ipynb. Snippet also available below:
```python
#!/usr/bin/env python
# coding: utf-8
# Loading dependencies and defining preprocessing functions
from transformers import Wav2Vec2ForCTC
from transformers import Wav2Vec2Processor
from datasets import load_dataset, load_metric
import re
import torchaudio
import librosa
import numpy as np
from datasets import load_dataset, load_metric
import torch
chars_to_ignore_regex = '[\\\\\\\\,\\\\\\\\?\\\\\\\\.\\\\\\\\!\\\\\\\\-\\\\\\\\;\\\\\\\\:\\\\\\\\"\\\\\\\\“\\\\\\\\%\\\\\\\\‘\\\\\\\\”\\\\\\\\�]'
def remove_special_characters(batch):
batch["text"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() + " "
return batch
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = speech_array[0].numpy()
batch["sampling_rate"] = sampling_rate
batch["target_text"] = batch["text"]
return batch
def resample(batch):
batch["speech"] = librosa.resample(np.asarray(batch["speech"]), 48_000, 16_000)
batch["sampling_rate"] = 16_000
return batch
def prepare_dataset(batch):
# check that all files have the correct sampling rate
assert (
len(set(batch["sampling_rate"])) == 1
), f"Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}."
batch["input_values"] = processor(batch["speech"], sampling_rate=batch["sampling_rate"][0]).input_values
with processor.as_target_processor():
batch["labels"] = processor(batch["target_text"]).input_ids
return batch
# Loading model and dataset processor
model = Wav2Vec2ForCTC.from_pretrained("lighteternal/wav2vec2-large-xlsr-53-greek").to("cuda")
processor = Wav2Vec2Processor.from_pretrained("lighteternal/wav2vec2-large-xlsr-53-greek")
# Preparing speech dataset to be suitable for inference
common_voice_test = load_dataset("common_voice", "el", split="test")
common_voice_test = common_voice_test.remove_columns(["accent", "age", "client_id", "down_votes", "gender", "locale", "segment", "up_votes"])
common_voice_test = common_voice_test.map(remove_special_characters, remove_columns=["sentence"])
common_voice_test = common_voice_test.map(speech_file_to_array_fn, remove_columns=common_voice_test.column_names)
common_voice_test = common_voice_test.map(resample, num_proc=8)
common_voice_test = common_voice_test.map(prepare_dataset, remove_columns=common_voice_test.column_names, batch_size=8, num_proc=8, batched=True)
# Loading test dataset
common_voice_test_transcription = load_dataset("common_voice", "el", split="test")
#Performing inference on a random sample. Change the "example" value to try inference on different CommonVoice extracts
example = 123
input_dict = processor(common_voice_test["input_values"][example], return_tensors="pt", sampling_rate=16_000, padding=True)
logits = model(input_dict.input_values.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
print("Prediction:")
print(processor.decode(pred_ids[0]))
# πού θέλεις να πάμε ρώτησε φοβισμένα ο βασιλιάς
print("\\\\
Reference:")
print(common_voice_test_transcription["sentence"][example].lower())
# πού θέλεις να πάμε; ρώτησε φοβισμένα ο βασιλιάς.
```
## Evaluation
The model can be evaluated as follows on the Greek test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "el", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("lighteternal/wav2vec2-large-xlsr-53-greek")
model = Wav2Vec2ForCTC.from_pretrained("lighteternal/wav2vec2-large-xlsr-53-greek")
model.to("cuda")
chars_to_ignore_regex = '[\\\\\\\\,\\\\\\\\?\\\\\\\\.\\\\\\\\!\\\\\\\\-\\\\\\\\;\\\\\\\\:\\\\\\\\"\\\\\\\\“\\\\\\\\%\\\\\\\\‘\\\\\\\\”\\\\\\\\�]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**: 10.497628 %
### How to use for training:
Instructions and code to replicate the process are provided in the Fine_Tune_XLSR_Wav2Vec2_on_Greek_ASR_with_🤗_Transformers.ipynb notebook.
## Metrics
| Metric | Value |
| ----------- | ----------- |
| Training Loss | 0.0545 |
| Validation Loss | 0.1661 |
| CER on CommonVoice Test (%) *| 2.8753 |
| WER on CommonVoice Test (%) *| 10.4976 |
* Reference transcripts were lower-cased and striped of punctuation and special characters.
### Acknowledgement
The research work was supported by the Hellenic Foundation for Research and Innovation (HFRI) under the HFRI PhD Fellowship grant (Fellowship Number:50, 2nd call)
Based on the tutorial of Patrick von Platen: https://huggingface.co/blog/fine-tune-xlsr-wav2vec2
Original colab notebook here: https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/Fine_Tune_XLSR_Wav2Vec2_on_Turkish_ASR_with_%F0%9F%A4%97_Transformers.ipynb#scrollTo=V7YOT2mnUiea
|
pysentimiento/bertweet-pt-sentiment | pysentimiento | "2023-03-29T20:15:30Z" | 18,932 | 6 | pysentimiento | [
"pysentimiento",
"pytorch",
"safetensors",
"roberta",
"twitter",
"sentiment-analysis",
"pt",
"arxiv:2106.09462",
"region:us"
] | null | "2023-02-27T17:16:09Z" | ---
language:
- pt
library_name: pysentimiento
tags:
- twitter
- sentiment-analysis
---
# Sentiment Analysis in Portuguese
Repository: [https://github.com/pysentimiento/pysentimiento/](https://github.com/pysentimiento/pysentimiento/)
Model trained for polarity detection in Portuguese. Base model is [BERTabaporu](https://huggingface.co/pablocosta/bertabaporu-base-uncased), a RoBERTa model trained in Portuguese tweets.
Uses `POS`, `NEG`, `NEU` labels.
## Usage
Use it directly with [pysentimiento](https://github.com/pysentimiento/pysentimiento)
```python
from pysentimiento import create_analyzer
analyzer = create_analyzer(task="sentiment", lang="pt")
analyzer.predict("isto é bonito")
# returns AnalyzerOutput(output=POS, probas={POS: 0.998, NEG: 0.002, NEU: 0.000})
```
## Citation
If you use this model in your research, please cite pysentimiento and RoBERTuito papers:
```
@misc{perez2021pysentimiento,
title={pysentimiento: A Python Toolkit for Sentiment Analysis and SocialNLP tasks},
author={Juan Manuel Pérez and Juan Carlos Giudici and Franco Luque},
year={2021},
eprint={2106.09462},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@misc {pablo_botton_da_costa_2022,
author = { {pablo botton da costa} },
title = { bertabaporu-base-uncased (Revision 1982d0f) },
year = 2022,
url = { https://huggingface.co/pablocosta/bertabaporu-base-uncased },
doi = { 10.57967/hf/0019 },
publisher = { Hugging Face }
}
@InProceedings{BRUM18.389,
author = {Henrico Brum and Maria das Gra\c{c}as Volpe Nunes},
title = "{Building a Sentiment Corpus of Tweets in Brazilian Portuguese}",
booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
year = {2018},
month = {May 7-12, 2018},
address = {Miyazaki, Japan},
editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and HÚlŔne Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga},
publisher = {European Language Resources Association (ELRA)},
isbn = {979-10-95546-00-9},
language = {english}
}
``` |
ai-forever/ruRoberta-large | ai-forever | "2023-11-03T12:47:18Z" | 18,915 | 36 | transformers | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"PyTorch",
"Transformers",
"ru",
"arxiv:2309.10931",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | "2022-03-02T23:29:05Z" | ---
language:
- ru
tags:
- PyTorch
- Transformers
thumbnail: "https://github.com/sberbank-ai/model-zoo"
---
# ruRoberta-large
The model architecture design, pretraining, and evaluation are documented in our preprint: [**A Family of Pretrained Transformer Language Models for Russian**](https://arxiv.org/abs/2309.10931).
The model is pretrained by the [SberDevices](https://sberdevices.ru/) team.
* Task: `mask filling`
* Type: `encoder`
* Tokenizer: `BBPE`
* Dict size: `50 257`
* Num Parameters: `355 M`
* Training Data Volume `250 GB`
# Authors
+ NLP core team RnD [Telegram channel](https://t.me/nlpcoreteam):
+ Dmitry Zmitrovich
# Cite us
```
@misc{zmitrovich2023family,
title={A Family of Pretrained Transformer Language Models for Russian},
author={Dmitry Zmitrovich and Alexander Abramov and Andrey Kalmykov and Maria Tikhonova and Ekaterina Taktasheva and Danil Astafurov and Mark Baushenko and Artem Snegirev and Tatiana Shavrina and Sergey Markov and Vladislav Mikhailov and Alena Fenogenova},
year={2023},
eprint={2309.10931},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
beomi/kcbert-base | beomi | "2023-03-30T08:52:15Z" | 18,867 | 16 | transformers | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"fill-mask",
"korean",
"ko",
"arxiv:1810.04805",
"doi:10.57967/hf/0016",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | "2022-03-02T23:29:05Z" | ---
language: ko
license: apache-2.0
tags:
- korean
---
# KcBERT: Korean comments BERT
** Updates on 2021.04.07 **
- KcELECTRA가 릴리즈 되었습니다!🤗
- KcELECTRA는 보다 더 많은 데이터셋, 그리고 더 큰 General vocab을 통해 KcBERT 대비 **모든 태스크에서 더 높은 성능**을 보입니다.
- 아래 깃헙 링크에서 직접 사용해보세요!
- https://github.com/Beomi/KcELECTRA
** Updates on 2021.03.14 **
- KcBERT Paper 인용 표기를 추가하였습니다.(bibtex)
- KcBERT-finetune Performance score를 본문에 추가하였습니다.
** Updates on 2020.12.04 **
Huggingface Transformers가 v4.0.0으로 업데이트됨에 따라 Tutorial의 코드가 일부 변경되었습니다.
업데이트된 KcBERT-Large NSMC Finetuning Colab: <a href="https://colab.research.google.com/drive/1dFC0FL-521m7CL_PSd8RLKq67jgTJVhL?usp=sharing">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
** Updates on 2020.09.11 **
KcBERT를 Google Colab에서 TPU를 통해 학습할 수 있는 튜토리얼을 제공합니다! 아래 버튼을 눌러보세요.
Colab에서 TPU로 KcBERT Pretrain 해보기: <a href="https://colab.research.google.com/drive/1lYBYtaXqt9S733OXdXvrvC09ysKFN30W">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
텍스트 분량만 전체 12G 텍스트 중 일부(144MB)로 줄여 학습을 진행합니다.
한국어 데이터셋/코퍼스를 좀더 쉽게 사용할 수 있는 [Korpora](https://github.com/ko-nlp/Korpora) 패키지를 사용합니다.
** Updates on 2020.09.08 **
Github Release를 통해 학습 데이터를 업로드하였습니다.
다만 한 파일당 2GB 이내의 제약으로 인해 분할압축되어있습니다.
아래 링크를 통해 받아주세요. (가입 없이 받을 수 있어요. 분할압축)
만약 한 파일로 받고싶으시거나/Kaggle에서 데이터를 살펴보고 싶으시다면 아래의 캐글 데이터셋을 이용해주세요.
- Github릴리즈: https://github.com/Beomi/KcBERT/releases/tag/TrainData_v1
** Updates on 2020.08.22 **
Pretrain Dataset 공개
- 캐글: https://www.kaggle.com/junbumlee/kcbert-pretraining-corpus-korean-news-comments (한 파일로 받을 수 있어요. 단일파일)
Kaggle에 학습을 위해 정제한(아래 `clean`처리를 거친) Dataset을 공개하였습니다!
직접 다운받으셔서 다양한 Task에 학습을 진행해보세요 :)
---
공개된 한국어 BERT는 대부분 한국어 위키, 뉴스 기사, 책 등 잘 정제된 데이터를 기반으로 학습한 모델입니다. 한편, 실제로 NSMC와 같은 댓글형 데이터셋은 정제되지 않았고 구어체 특징에 신조어가 많으며, 오탈자 등 공식적인 글쓰기에서 나타나지 않는 표현들이 빈번하게 등장합니다.
KcBERT는 위와 같은 특성의 데이터셋에 적용하기 위해, 네이버 뉴스에서 댓글과 대댓글을 수집해, 토크나이저와 BERT모델을 처음부터 학습한 Pretrained BERT 모델입니다.
KcBERT는 Huggingface의 Transformers 라이브러리를 통해 간편히 불러와 사용할 수 있습니다. (별도의 파일 다운로드가 필요하지 않습니다.)
## KcBERT Performance
- Finetune 코드는 https://github.com/Beomi/KcBERT-finetune 에서 찾아보실 수 있습니다.
| | Size<br/>(용량) | **NSMC**<br/>(acc) | **Naver NER**<br/>(F1) | **PAWS**<br/>(acc) | **KorNLI**<br/>(acc) | **KorSTS**<br/>(spearman) | **Question Pair**<br/>(acc) | **KorQuaD (Dev)**<br/>(EM/F1) |
| :-------------------- | :---: | :----------------: | :--------------------: | :----------------: | :------------------: | :-----------------------: | :-------------------------: | :---------------------------: |
| KcBERT-Base | 417M | 89.62 | 84.34 | 66.95 | 74.85 | 75.57 | 93.93 | 60.25 / 84.39 |
| KcBERT-Large | 1.2G | **90.68** | 85.53 | 70.15 | 76.99 | 77.49 | 94.06 | 62.16 / 86.64 |
| KoBERT | 351M | 89.63 | 86.11 | 80.65 | 79.00 | 79.64 | 93.93 | 52.81 / 80.27 |
| XLM-Roberta-Base | 1.03G | 89.49 | 86.26 | 82.95 | 79.92 | 79.09 | 93.53 | 64.70 / 88.94 |
| HanBERT | 614M | 90.16 | **87.31** | 82.40 | **80.89** | 83.33 | 94.19 | 78.74 / 92.02 |
| KoELECTRA-Base | 423M | **90.21** | 86.87 | 81.90 | 80.85 | 83.21 | 94.20 | 61.10 / 89.59 |
| KoELECTRA-Base-v2 | 423M | 89.70 | 87.02 | **83.90** | 80.61 | **84.30** | **94.72** | **84.34 / 92.58** |
| DistilKoBERT | 108M | 88.41 | 84.13 | 62.55 | 70.55 | 73.21 | 92.48 | 54.12 / 77.80 |
\*HanBERT의 Size는 Bert Model과 Tokenizer DB를 합친 것입니다.
\***config의 세팅을 그대로 하여 돌린 결과이며, hyperparameter tuning을 추가적으로 할 시 더 좋은 성능이 나올 수 있습니다.**
## How to use
### Requirements
- `pytorch <= 1.8.0`
- `transformers ~= 3.0.1`
- `transformers ~= 4.0.0` 도 호환됩니다.
- `emoji ~= 0.6.0`
- `soynlp ~= 0.0.493`
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
# Base Model (108M)
tokenizer = AutoTokenizer.from_pretrained("beomi/kcbert-base")
model = AutoModelWithLMHead.from_pretrained("beomi/kcbert-base")
# Large Model (334M)
tokenizer = AutoTokenizer.from_pretrained("beomi/kcbert-large")
model = AutoModelWithLMHead.from_pretrained("beomi/kcbert-large")
```
### Pretrain & Finetune Colab 링크 모음
#### Pretrain Data
- [데이터셋 다운로드(Kaggle, 단일파일, 로그인 필요)](https://www.kaggle.com/junbumlee/kcbert-pretraining-corpus-korean-news-comments)
- [데이터셋 다운로드(Github, 압축 여러파일, 로그인 불필요)](https://github.com/Beomi/KcBERT/releases/tag/TrainData_v1)
#### Pretrain Code
Colab에서 TPU로 KcBERT Pretrain 해보기: <a href="https://colab.research.google.com/drive/1lYBYtaXqt9S733OXdXvrvC09ysKFN30W">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
#### Finetune Samples
**KcBERT-Base** NSMC Finetuning with PyTorch-Lightning (Colab) <a href="https://colab.research.google.com/drive/1fn4sVJ82BrrInjq6y5655CYPP-1UKCLb?usp=sharing">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
**KcBERT-Large** NSMC Finetuning with PyTorch-Lightning (Colab) <a href="https://colab.research.google.com/drive/1dFC0FL-521m7CL_PSd8RLKq67jgTJVhL?usp=sharing">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
> 위 두 코드는 Pretrain 모델(base, large)와 batch size만 다를 뿐, 나머지 코드는 완전히 동일합니다.
## Train Data & Preprocessing
### Raw Data
학습 데이터는 2019.01.01 ~ 2020.06.15 사이에 작성된 **댓글 많은 뉴스** 기사들의 **댓글과 대댓글**을 모두 수집한 데이터입니다.
데이터 사이즈는 텍스트만 추출시 **약 15.4GB이며, 1억1천만개 이상의 문장**으로 이뤄져 있습니다.
### Preprocessing
PLM 학습을 위해서 전처리를 진행한 과정은 다음과 같습니다.
1. 한글 및 영어, 특수문자, 그리고 이모지(🥳)까지!
정규표현식을 통해 한글, 영어, 특수문자를 포함해 Emoji까지 학습 대상에 포함했습니다.
한편, 한글 범위를 `ㄱ-ㅎ가-힣` 으로 지정해 `ㄱ-힣` 내의 한자를 제외했습니다.
2. 댓글 내 중복 문자열 축약
`ㅋㅋㅋㅋㅋ`와 같이 중복된 글자를 `ㅋㅋ`와 같은 것으로 합쳤습니다.
3. Cased Model
KcBERT는 영문에 대해서는 대소문자를 유지하는 Cased model입니다.
4. 글자 단위 10글자 이하 제거
10글자 미만의 텍스트는 단일 단어로 이뤄진 경우가 많아 해당 부분을 제외했습니다.
5. 중복 제거
중복적으로 쓰인 댓글을 제거하기 위해 중복 댓글을 하나로 합쳤습니다.
이를 통해 만든 최종 학습 데이터는 **12.5GB, 8.9천만개 문장**입니다.
아래 명령어로 pip로 설치한 뒤, 아래 clean함수로 클리닝을 하면 Downstream task에서 보다 성능이 좋아집니다. (`[UNK]` 감소)
```bash
pip install soynlp emoji
```
아래 `clean` 함수를 Text data에 사용해주세요.
```python
import re
import emoji
from soynlp.normalizer import repeat_normalize
emojis = list({y for x in emoji.UNICODE_EMOJI.values() for y in x.keys()})
emojis = ''.join(emojis)
pattern = re.compile(f'[^ .,?!/@$%~％·∼()\x00-\x7Fㄱ-ㅣ가-힣{emojis}]+')
url_pattern = re.compile(
r'https?:\/\/(www\.)?[-a-zA-Z0-9@:%._\+~#=]{1,256}\.[a-zA-Z0-9()]{1,6}\b([-a-zA-Z0-9()@:%_\+.~#?&//=]*)')
def clean(x):
x = pattern.sub(' ', x)
x = url_pattern.sub('', x)
x = x.strip()
x = repeat_normalize(x, num_repeats=2)
return x
```
### Cleaned Data (Released on Kaggle)
원본 데이터를 위 `clean`함수로 정제한 12GB분량의 txt 파일을 아래 Kaggle Dataset에서 다운받으실 수 있습니다 :)
https://www.kaggle.com/junbumlee/kcbert-pretraining-corpus-korean-news-comments
## Tokenizer Train
Tokenizer는 Huggingface의 [Tokenizers](https://github.com/huggingface/tokenizers) 라이브러리를 통해 학습을 진행했습니다.
그 중 `BertWordPieceTokenizer` 를 이용해 학습을 진행했고, Vocab Size는 `30000`으로 진행했습니다.
Tokenizer를 학습하는 것에는 `1/10`로 샘플링한 데이터로 학습을 진행했고, 보다 골고루 샘플링하기 위해 일자별로 stratify를 지정한 뒤 햑습을 진행했습니다.
## BERT Model Pretrain
- KcBERT Base config
```json
{
"max_position_embeddings": 300,
"hidden_dropout_prob": 0.1,
"hidden_act": "gelu",
"initializer_range": 0.02,
"num_hidden_layers": 12,
"type_vocab_size": 2,
"vocab_size": 30000,
"hidden_size": 768,
"attention_probs_dropout_prob": 0.1,
"directionality": "bidi",
"num_attention_heads": 12,
"intermediate_size": 3072,
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert"
}
```
- KcBERT Large config
```json
{
"type_vocab_size": 2,
"initializer_range": 0.02,
"max_position_embeddings": 300,
"vocab_size": 30000,
"hidden_size": 1024,
"hidden_dropout_prob": 0.1,
"model_type": "bert",
"directionality": "bidi",
"pad_token_id": 0,
"layer_norm_eps": 1e-12,
"hidden_act": "gelu",
"num_hidden_layers": 24,
"num_attention_heads": 16,
"attention_probs_dropout_prob": 0.1,
"intermediate_size": 4096,
"architectures": [
"BertForMaskedLM"
]
}
```
BERT Model Config는 Base, Large 기본 세팅값을 그대로 사용했습니다. (MLM 15% 등)
TPU `v3-8` 을 이용해 각각 3일, N일(Large는 학습 진행 중)을 진행했고, 현재 Huggingface에 공개된 모델은 1m(100만) step을 학습한 ckpt가 업로드 되어있습니다.
모델 학습 Loss는 Step에 따라 초기 200k에 가장 빠르게 Loss가 줄어들다 400k이후로는 조금씩 감소하는 것을 볼 수 있습니다.
- Base Model Loss
- Large Model Loss
학습은 GCP의 TPU v3-8을 이용해 학습을 진행했고, 학습 시간은 Base Model 기준 2.5일정도 진행했습니다. Large Model은 약 5일정도 진행한 뒤 가장 낮은 loss를 가진 체크포인트로 정했습니다.
## Example
### HuggingFace MASK LM
[HuggingFace kcbert-base 모델](https://huggingface.co/beomi/kcbert-base?text=오늘은+날씨가+[MASK]) 에서 아래와 같이 테스트 해 볼 수 있습니다.
물론 [kcbert-large 모델](https://huggingface.co/beomi/kcbert-large?text=오늘은+날씨가+[MASK]) 에서도 테스트 할 수 있습니다.
### NSMC Binary Classification
[네이버 영화평 코퍼스](https://github.com/e9t/nsmc) 데이터셋을 대상으로 Fine Tuning을 진행해 성능을 간단히 테스트해보았습니다.
Base Model을 Fine Tune하는 코드는 <a href="https://colab.research.google.com/drive/1fn4sVJ82BrrInjq6y5655CYPP-1UKCLb?usp=sharing">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a> 에서 직접 실행해보실 수 있습니다.
Large Model을 Fine Tune하는 코드는 <a href="https://colab.research.google.com/drive/1dFC0FL-521m7CL_PSd8RLKq67jgTJVhL?usp=sharing">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a> 에서 직접 실행해볼 수 있습니다.
- GPU는 P100 x1대 기준 1epoch에 2-3시간, TPU는 1epoch에 1시간 내로 소요됩니다.
- GPU RTX Titan x4대 기준 30분/epoch 소요됩니다.
- 예시 코드는 [pytorch-lightning](https://github.com/PyTorchLightning/pytorch-lightning)으로 개발했습니다.
#### 실험결과
- KcBERT-Base Model 실험결과: Val acc `.8905`
- KcBERT-Large Model 실험 결과: Val acc `.9089`
> 더 다양한 Downstream Task에 대해 테스트를 진행하고 공개할 예정입니다.
## 인용표기/Citation
KcBERT를 인용하실 때는 아래 양식을 통해 인용해주세요.
```
@inproceedings{lee2020kcbert,
title={KcBERT: Korean Comments BERT},
author={Lee, Junbum},
booktitle={Proceedings of the 32nd Annual Conference on Human and Cognitive Language Technology},
pages={437--440},
year={2020}
}
```
- 논문집 다운로드 링크: http://hclt.kr/dwn/?v=bG5iOmNvbmZlcmVuY2U7aWR4OjMy (*혹은 http://hclt.kr/symp/?lnb=conference )
## Acknowledgement
KcBERT Model을 학습하는 GCP/TPU 환경은 [TFRC](https://www.tensorflow.org/tfrc?hl=ko) 프로그램의 지원을 받았습니다.
모델 학습 과정에서 많은 조언을 주신 [Monologg](https://github.com/monologg/) 님 감사합니다 :)
## Reference
### Github Repos
- [BERT by Google](https://github.com/google-research/bert)
- [KoBERT by SKT](https://github.com/SKTBrain/KoBERT)
- [KoELECTRA by Monologg](https://github.com/monologg/KoELECTRA/)
- [Transformers by Huggingface](https://github.com/huggingface/transformers)
- [Tokenizers by Hugginface](https://github.com/huggingface/tokenizers)
### Papers
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805)
### Blogs
- [Monologg님의 KoELECTRA 학습기](https://monologg.kr/categories/NLP/ELECTRA/)
- [Colab에서 TPU로 BERT 처음부터 학습시키기 - Tensorflow/Google ver.](https://beomi.github.io/2020/02/26/Train-BERT-from-scratch-on-colab-TPU-Tensorflow-ver/)
|
TheBloke/Mistral-7B-Instruct-v0.2-GPTQ | TheBloke | "2023-12-11T22:46:53Z" | 18,846 | 41 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"finetuned",
"conversational",
"arxiv:2310.06825",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"gptq",
"region:us"
] | text-generation | "2023-12-11T22:18:46Z" | ---
base_model: mistralai/Mistral-7B-Instruct-v0.2
inference: false
license: apache-2.0
model_creator: Mistral AI_
model_name: Mistral 7B Instruct v0.2
model_type: mistral
pipeline_tag: text-generation
prompt_template: '<s>[INST] {prompt} [/INST]
'
quantized_by: TheBloke
tags:
- finetuned
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
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# Mistral 7B Instruct v0.2 - GPTQ
- Model creator: [Mistral AI_](https://huggingface.co/mistralai)
- Original model: [Mistral 7B Instruct v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
<!-- description start -->
# Description
This repo contains GPTQ model files for [Mistral AI_'s Mistral 7B Instruct v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2).
Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF)
* [Mistral AI_'s original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Mistral
```
<s>[INST] {prompt} [/INST]
```
<!-- prompt-template end -->
<!-- README_GPTQ.md-compatible clients start -->
## Known compatible clients / servers
GPTQ models are currently supported on Linux (NVidia/AMD) and Windows (NVidia only). macOS users: please use GGUF models.
These GPTQ models are known to work in the following inference servers/webuis.
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
- [KoboldAI United](https://github.com/henk717/koboldai)
- [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui)
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
This may not be a complete list; if you know of others, please let me know!
<!-- README_GPTQ.md-compatible clients end -->
<!-- README_GPTQ.md-provided-files start -->
## Provided files, and GPTQ parameters
Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
Each separate quant is in a different branch. See below for instructions on fetching from different branches.
Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers.
<details>
<summary>Explanation of GPTQ parameters</summary>
- Bits: The bit size of the quantised model.
- GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
- Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
- Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
- GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
- Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
- ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit.
</details>
| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GPTQ/tree/main) | 4 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 4.16 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. |
| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 4.57 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 7.52 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 7.68 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. |
| [gptq-8bit-32g-actorder_True](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GPTQ/tree/gptq-8bit-32g-actorder_True) | 8 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 8.17 GB | No | 8-bit, with group size 32g and Act Order for maximum inference quality. |
| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 4.29 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. |
<!-- README_GPTQ.md-provided-files end -->
<!-- README_GPTQ.md-download-from-branches start -->
## How to download, including from branches
### In text-generation-webui
To download from the `main` branch, enter `TheBloke/Mistral-7B-Instruct-v0.2-GPTQ` in the "Download model" box.
To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/Mistral-7B-Instruct-v0.2-GPTQ:gptq-4bit-32g-actorder_True`
### From the command line
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
To download the `main` branch to a folder called `Mistral-7B-Instruct-v0.2-GPTQ`:
```shell
mkdir Mistral-7B-Instruct-v0.2-GPTQ
huggingface-cli download TheBloke/Mistral-7B-Instruct-v0.2-GPTQ --local-dir Mistral-7B-Instruct-v0.2-GPTQ --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir Mistral-7B-Instruct-v0.2-GPTQ
huggingface-cli download TheBloke/Mistral-7B-Instruct-v0.2-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir Mistral-7B-Instruct-v0.2-GPTQ --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.
The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`.
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
mkdir Mistral-7B-Instruct-v0.2-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Mistral-7B-Instruct-v0.2-GPTQ --local-dir Mistral-7B-Instruct-v0.2-GPTQ --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
### With `git` (**not** recommended)
To clone a specific branch with `git`, use a command like this:
```shell
git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GPTQ
```
Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.)
<!-- README_GPTQ.md-download-from-branches end -->
<!-- README_GPTQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/Mistral-7B-Instruct-v0.2-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/Mistral-7B-Instruct-v0.2-GPTQ:gptq-4bit-32g-actorder_True`
- see Provided Files above for the list of branches for each option.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `Mistral-7B-Instruct-v0.2-GPTQ`
7. The model will automatically load, and is now ready for use!
8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
- Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
9. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_GPTQ.md-text-generation-webui end -->
<!-- README_GPTQ.md-use-from-tgi start -->
## Serving this model from Text Generation Inference (TGI)
It's recommended to use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
Example Docker parameters:
```shell
--model-id TheBloke/Mistral-7B-Instruct-v0.2-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):
```shell
pip3 install huggingface-hub
```
```python
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''<s>[INST] {prompt} [/INST]
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1)
print(f"Model output: {response}")
```
<!-- README_GPTQ.md-use-from-tgi end -->
<!-- README_GPTQ.md-use-from-python start -->
## Python code example: inference from this GPTQ model
### Install the necessary packages
Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
```shell
pip3 install --upgrade transformers optimum
# If using PyTorch 2.1 + CUDA 12.x:
pip3 install --upgrade auto-gptq
# or, if using PyTorch 2.1 + CUDA 11.x:
pip3 install --upgrade auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
```
If you are using PyTorch 2.0, you will need to install AutoGPTQ from source. Likewise if you have problems with the pre-built wheels, you should try building from source:
```shell
pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
git checkout v0.5.1
pip3 install .
```
### Example Python code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name_or_path = "TheBloke/Mistral-7B-Instruct-v0.2-GPTQ"
# To use a different branch, change revision
# For example: revision="gptq-4bit-32g-actorder_True"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
device_map="auto",
trust_remote_code=False,
revision="main")
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
prompt = "Tell me about AI"
prompt_template=f'''<s>[INST] {prompt} [/INST]
'''
print("\n\n*** Generate:")
input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))
# Inference can also be done using transformers' pipeline
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
print(pipe(prompt_template)[0]['generated_text'])
```
<!-- README_GPTQ.md-use-from-python end -->
<!-- README_GPTQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly.
[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama and Mistral models in 4-bit. Please see the Provided Files table above for per-file compatibility.
For a list of clients/servers, please see "Known compatible clients / servers", above.
<!-- README_GPTQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: Mistral AI_'s Mistral 7B Instruct v0.2
# Model Card for Mistral-7B-Instruct-v0.2
The Mistral-7B-Instruct-v0.2 Large Language Model (LLM) is an improved instruct fine-tuned version of [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1).
For full details of this model please read our [paper](https://arxiv.org/abs/2310.06825) and [release blog post](https://mistral.ai/news/la-plateforme/).
## Instruction format
In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[/INST]` tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.
E.g.
```
text = "<s>[INST] What is your favourite condiment? [/INST]"
"Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> "
"[INST] Do you have mayonnaise recipes? [/INST]"
```
This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")
messages = [
{"role": "user", "content": "What is your favourite condiment?"},
{"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
{"role": "user", "content": "Do you have mayonnaise recipes?"}
]
encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
model_inputs = encodeds.to(device)
model.to(device)
generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])
```
## Model Architecture
This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:
- Grouped-Query Attention
- Sliding-Window Attention
- Byte-fallback BPE tokenizer
## Troubleshooting
- If you see the following error:
```
Traceback (most recent call last):
File "", line 1, in
File "/transformers/models/auto/auto_factory.py", line 482, in from_pretrained
config, kwargs = AutoConfig.from_pretrained(
File "/transformers/models/auto/configuration_auto.py", line 1022, in from_pretrained
config_class = CONFIG_MAPPING[config_dict["model_type"]]
File "/transformers/models/auto/configuration_auto.py", line 723, in getitem
raise KeyError(key)
KeyError: 'mistral'
```
Installing transformers from source should solve the issue
pip install git+https://github.com/huggingface/transformers
This should not be required after transformers-v4.33.4.
## Limitations
The Mistral 7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance.
It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to
make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.
## The Mistral AI Team
Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Louis Ternon, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.
|
madebyollin/taesd | madebyollin | "2023-12-27T21:08:39Z" | 18,832 | 34 | diffusers | [
"diffusers",
"safetensors",
"license:mit",
"region:us"
] | null | "2023-07-21T15:10:17Z" | ---
license: mit
---
# 🍰 Tiny AutoEncoder for Stable Diffusion
[TAESD](https://github.com/madebyollin/taesd) is very tiny autoencoder which uses the same "latent API" as Stable Diffusion's VAE.
TAESD is useful for [real-time previewing](https://twitter.com/madebyollin/status/1679356448655163394) of the SD generation process.
Comparison on my laptop:
This repo contains `.safetensors` versions of the TAESD weights.
For SDXL, use [TAESDXL](https://huggingface.co/madebyollin/taesdxl/) instead (the SD and SDXL VAEs are [incompatible](https://huggingface.co/madebyollin/sdxl-vae-fp16-fix/discussions/6#64b8a9c13707b7d603c6ac16)).
## Using in 🧨 diffusers
```python
import torch
from diffusers import DiffusionPipeline, AutoencoderTiny
pipe = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-1-base", torch_dtype=torch.float16
)
pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taesd", torch_dtype=torch.float16)
pipe = pipe.to("cuda")
prompt = "slice of delicious New York-style berry cheesecake"
image = pipe(prompt, num_inference_steps=25).images[0]
image.save("cheesecake.png")
``` |
mradermacher/L3-Nym-15B-GGUF | mradermacher | "2024-06-24T19:30:26Z" | 18,829 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:Frowning/L3-Nym-15B",
"endpoints_compatible",
"region:us"
] | null | "2024-06-24T18:44:12Z" | ---
base_model: Frowning/L3-Nym-15B
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/Frowning/L3-Nym-15B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/L3-Nym-15B-GGUF/resolve/main/L3-Nym-15B.Q2_K.gguf) | Q2_K | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/L3-Nym-15B-GGUF/resolve/main/L3-Nym-15B.IQ3_XS.gguf) | IQ3_XS | 6.5 | |
| [GGUF](https://huggingface.co/mradermacher/L3-Nym-15B-GGUF/resolve/main/L3-Nym-15B.Q3_K_S.gguf) | Q3_K_S | 6.8 | |
| [GGUF](https://huggingface.co/mradermacher/L3-Nym-15B-GGUF/resolve/main/L3-Nym-15B.IQ3_S.gguf) | IQ3_S | 6.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/L3-Nym-15B-GGUF/resolve/main/L3-Nym-15B.IQ3_M.gguf) | IQ3_M | 7.0 | |
| [GGUF](https://huggingface.co/mradermacher/L3-Nym-15B-GGUF/resolve/main/L3-Nym-15B.Q3_K_M.gguf) | Q3_K_M | 7.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/L3-Nym-15B-GGUF/resolve/main/L3-Nym-15B.Q3_K_L.gguf) | Q3_K_L | 8.1 | |
| [GGUF](https://huggingface.co/mradermacher/L3-Nym-15B-GGUF/resolve/main/L3-Nym-15B.IQ4_XS.gguf) | IQ4_XS | 8.4 | |
| [GGUF](https://huggingface.co/mradermacher/L3-Nym-15B-GGUF/resolve/main/L3-Nym-15B.Q4_K_S.gguf) | Q4_K_S | 8.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/L3-Nym-15B-GGUF/resolve/main/L3-Nym-15B.Q4_K_M.gguf) | Q4_K_M | 9.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/L3-Nym-15B-GGUF/resolve/main/L3-Nym-15B.Q5_K_S.gguf) | Q5_K_S | 10.5 | |
| [GGUF](https://huggingface.co/mradermacher/L3-Nym-15B-GGUF/resolve/main/L3-Nym-15B.Q5_K_M.gguf) | Q5_K_M | 10.8 | |
| [GGUF](https://huggingface.co/mradermacher/L3-Nym-15B-GGUF/resolve/main/L3-Nym-15B.Q6_K.gguf) | Q6_K | 12.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/L3-Nym-15B-GGUF/resolve/main/L3-Nym-15B.Q8_0.gguf) | Q8_0 | 16.1 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
microsoft/kosmos-2-patch14-224 | microsoft | "2023-11-28T09:31:26Z" | 18,819 | 138 | transformers | [
"transformers",
"pytorch",
"safetensors",
"kosmos-2",
"text2text-generation",
"image-captioning",
"image-to-text",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-to-text | "2023-10-02T16:09:33Z" | ---
pipeline_tag: image-to-text
tags:
- image-captioning
languages:
- en
license: mit
---
# Kosmos-2: Grounding Multimodal Large Language Models to the World
<a href="https://huggingface.co/microsoft/kosmos-2-patch14-224/resolve/main/annotated_snowman.jpg" target="_blank"><figure><img src="https://huggingface.co/microsoft/kosmos-2-patch14-224/resolve/main/annotated_snowman.jpg" width="384"><figcaption><b>[An image of a snowman warming himself by a fire.]</b></figcaption></figure></a>
This Hub repository contains a HuggingFace's `transformers` implementation of [the original Kosmos-2 model](https://github.com/microsoft/unilm/tree/master/kosmos-2) from Microsoft.
## How to Get Started with the Model
Use the code below to get started with the model.
```python
import requests
from PIL import Image
from transformers import AutoProcessor, AutoModelForVision2Seq
model = AutoModelForVision2Seq.from_pretrained("microsoft/kosmos-2-patch14-224")
processor = AutoProcessor.from_pretrained("microsoft/kosmos-2-patch14-224")
prompt = "<grounding>An image of"
url = "https://huggingface.co/microsoft/kosmos-2-patch14-224/resolve/main/snowman.png"
image = Image.open(requests.get(url, stream=True).raw)
# The original Kosmos-2 demo saves the image first then reload it. For some images, this will give slightly different image input and change the generation outputs.
image.save("new_image.jpg")
image = Image.open("new_image.jpg")
inputs = processor(text=prompt, images=image, return_tensors="pt")
generated_ids = model.generate(
pixel_values=inputs["pixel_values"],
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
image_embeds=None,
image_embeds_position_mask=inputs["image_embeds_position_mask"],
use_cache=True,
max_new_tokens=128,
)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
# Specify `cleanup_and_extract=False` in order to see the raw model generation.
processed_text = processor.post_process_generation(generated_text, cleanup_and_extract=False)
print(processed_text)
# `<grounding> An image of<phrase> a snowman</phrase><object><patch_index_0044><patch_index_0863></object> warming himself by<phrase> a fire</phrase><object><patch_index_0005><patch_index_0911></object>.`
# By default, the generated text is cleanup and the entities are extracted.
processed_text, entities = processor.post_process_generation(generated_text)
print(processed_text)
# `An image of a snowman warming himself by a fire.`
print(entities)
# `[('a snowman', (12, 21), [(0.390625, 0.046875, 0.984375, 0.828125)]), ('a fire', (41, 47), [(0.171875, 0.015625, 0.484375, 0.890625)])]`
```
## Tasks
This model is capable of performing different tasks through changing the prompts.
First, let's define a function to run a prompt.
<details>
<summary> Click to expand </summary>
```python
import requests
from PIL import Image
from transformers import AutoProcessor, AutoModelForVision2Seq
model = AutoModelForVision2Seq.from_pretrained("microsoft/kosmos-2-patch14-224")
processor = AutoProcessor.from_pretrained("microsoft/kosmos-2-patch14-224")
url = "https://huggingface.co/microsoft/kosmos-2-patch14-224/resolve/main/snowman.png"
image = Image.open(requests.get(url, stream=True).raw)
def run_example(prompt):
inputs = processor(text=prompt, images=image, return_tensors="pt")
generated_ids = model.generate(
pixel_values=inputs["pixel_values"],
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
image_embeds=None,
image_embeds_position_mask=inputs["image_embeds_position_mask"],
use_cache=True,
max_new_tokens=128,
)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
_processed_text = processor.post_process_generation(generated_text, cleanup_and_extract=False)
processed_text, entities = processor.post_process_generation(generated_text)
print(processed_text)
print(entities)
print(_processed_text)
```
</details>
Here are the tasks `Kosmos-2` could perform:
<details>
<summary> Click to expand </summary>
### Multimodal Grounding
#### • Phrase Grounding
```python
prompt = "<grounding><phrase> a snowman</phrase>"
run_example(prompt)
# a snowman is warming himself by the fire
# [('a snowman', (0, 9), [(0.390625, 0.046875, 0.984375, 0.828125)]), ('the fire', (32, 40), [(0.203125, 0.015625, 0.453125, 0.859375)])]
# <grounding><phrase> a snowman</phrase><object><patch_index_0044><patch_index_0863></object> is warming himself by<phrase> the fire</phrase><object><patch_index_0006><patch_index_0878></object>
```
#### • Referring Expression Comprehension
```python
prompt = "<grounding><phrase> a snowman next to a fire</phrase>"
run_example(prompt)
# a snowman next to a fire
# [('a snowman next to a fire', (0, 24), [(0.390625, 0.046875, 0.984375, 0.828125)])]
# <grounding><phrase> a snowman next to a fire</phrase><object><patch_index_0044><patch_index_0863></object>
```
### Multimodal Referring
#### • Referring expression generation
```python
prompt = "<grounding><phrase> It</phrase><object><patch_index_0044><patch_index_0863></object> is"
run_example(prompt)
# It is snowman in a hat and scarf
# [('It', (0, 2), [(0.390625, 0.046875, 0.984375, 0.828125)])]
# <grounding><phrase> It</phrase><object><patch_index_0044><patch_index_0863></object> is snowman in a hat and scarf
```
### Perception-Language Tasks
#### • Grounded VQA
```python
prompt = "<grounding> Question: What is special about this image? Answer:"
run_example(prompt)
# Question: What is special about this image? Answer: The image features a snowman sitting by a campfire in the snow.
# [('a snowman', (71, 80), [(0.390625, 0.046875, 0.984375, 0.828125)]), ('a campfire', (92, 102), [(0.109375, 0.640625, 0.546875, 0.984375)])]
# <grounding> Question: What is special about this image? Answer: The image features<phrase> a snowman</phrase><object><patch_index_0044><patch_index_0863></object> sitting by<phrase> a campfire</phrase><object><patch_index_0643><patch_index_1009></object> in the snow.
```
#### • Grounded VQA with multimodal referring via bounding boxes
```python
prompt = "<grounding> Question: Where is<phrase> the fire</phrase><object><patch_index_0005><patch_index_0911></object> next to? Answer:"
run_example(prompt)
# Question: Where is the fire next to? Answer: Near the snowman.
# [('the fire', (19, 27), [(0.171875, 0.015625, 0.484375, 0.890625)]), ('the snowman', (50, 61), [(0.390625, 0.046875, 0.984375, 0.828125)])]
# <grounding> Question: Where is<phrase> the fire</phrase><object><patch_index_0005><patch_index_0911></object> next to? Answer: Near<phrase> the snowman</phrase><object><patch_index_0044><patch_index_0863></object>.
```
### Grounded Image captioning
#### • Brief
```python
prompt = "<grounding> An image of"
run_example(prompt)
# An image of a snowman warming himself by a campfire.
# [('a snowman', (12, 21), [(0.390625, 0.046875, 0.984375, 0.828125)]), ('a campfire', (41, 51), [(0.109375, 0.640625, 0.546875, 0.984375)])]
# <grounding> An image of<phrase> a snowman</phrase><object><patch_index_0044><patch_index_0863></object> warming himself by<phrase> a campfire</phrase><object><patch_index_0643><patch_index_1009></object>.
```
#### • Detailed
```python
prompt = "<grounding> Describe this image in detail:"
run_example(prompt)
# Describe this image in detail: The image features a snowman sitting by a campfire in the snow. He is wearing a hat, scarf, and gloves, with a pot nearby and a cup nearby. The snowman appears to be enjoying the warmth of the fire, and it appears to have a warm and cozy atmosphere.
# [('a campfire', (71, 81), [(0.171875, 0.015625, 0.484375, 0.984375)]), ('a hat', (109, 114), [(0.515625, 0.046875, 0.828125, 0.234375)]), ('scarf', (116, 121), [(0.515625, 0.234375, 0.890625, 0.578125)]), ('gloves', (127, 133), [(0.515625, 0.390625, 0.640625, 0.515625)]), ('a pot', (140, 145), [(0.078125, 0.609375, 0.265625, 0.859375)]), ('a cup', (157, 162), [(0.890625, 0.765625, 0.984375, 0.984375)])]
# <grounding> Describe this image in detail: The image features a snowman sitting by<phrase> a campfire</phrase><object><patch_index_0005><patch_index_1007></object> in the snow. He is wearing<phrase> a hat</phrase><object><patch_index_0048><patch_index_0250></object>,<phrase> scarf</phrase><object><patch_index_0240><patch_index_0604></object>, and<phrase> gloves</phrase><object><patch_index_0400><patch_index_0532></object>, with<phrase> a pot</phrase><object><patch_index_0610><patch_index_0872></object> nearby and<phrase> a cup</phrase><object><patch_index_0796><patch_index_1023></object> nearby. The snowman appears to be enjoying the warmth of the fire, and it appears to have a warm and cozy atmosphere.
```
</details>
## Draw the bounding bboxes of the entities on the image
Once you have the `entities`, you can use the following helper function to draw their bounding bboxes on the image:
<details>
<summary> Click to expand </summary>
```python
import cv2
import numpy as np
import os
import requests
import torch
import torchvision.transforms as T
from PIL import Image
def is_overlapping(rect1, rect2):
x1, y1, x2, y2 = rect1
x3, y3, x4, y4 = rect2
return not (x2 < x3 or x1 > x4 or y2 < y3 or y1 > y4)
def draw_entity_boxes_on_image(image, entities, show=False, save_path=None):
"""_summary_
Args:
image (_type_): image or image path
collect_entity_location (_type_): _description_
"""
if isinstance(image, Image.Image):
image_h = image.height
image_w = image.width
image = np.array(image)[:, :, [2, 1, 0]]
elif isinstance(image, str):
if os.path.exists(image):
pil_img = Image.open(image).convert("RGB")
image = np.array(pil_img)[:, :, [2, 1, 0]]
image_h = pil_img.height
image_w = pil_img.width
else:
raise ValueError(f"invaild image path, {image}")
elif isinstance(image, torch.Tensor):
image_tensor = image.cpu()
reverse_norm_mean = torch.tensor([0.48145466, 0.4578275, 0.40821073])[:, None, None]
reverse_norm_std = torch.tensor([0.26862954, 0.26130258, 0.27577711])[:, None, None]
image_tensor = image_tensor * reverse_norm_std + reverse_norm_mean
pil_img = T.ToPILImage()(image_tensor)
image_h = pil_img.height
image_w = pil_img.width
image = np.array(pil_img)[:, :, [2, 1, 0]]
else:
raise ValueError(f"invaild image format, {type(image)} for {image}")
if len(entities) == 0:
return image
new_image = image.copy()
previous_bboxes = []
# size of text
text_size = 1
# thickness of text
text_line = 1 # int(max(1 * min(image_h, image_w) / 512, 1))
box_line = 3
(c_width, text_height), _ = cv2.getTextSize("F", cv2.FONT_HERSHEY_COMPLEX, text_size, text_line)
base_height = int(text_height * 0.675)
text_offset_original = text_height - base_height
text_spaces = 3
for entity_name, (start, end), bboxes in entities:
for (x1_norm, y1_norm, x2_norm, y2_norm) in bboxes:
orig_x1, orig_y1, orig_x2, orig_y2 = int(x1_norm * image_w), int(y1_norm * image_h), int(x2_norm * image_w), int(y2_norm * image_h)
# draw bbox
# random color
color = tuple(np.random.randint(0, 255, size=3).tolist())
new_image = cv2.rectangle(new_image, (orig_x1, orig_y1), (orig_x2, orig_y2), color, box_line)
l_o, r_o = box_line // 2 + box_line % 2, box_line // 2 + box_line % 2 + 1
x1 = orig_x1 - l_o
y1 = orig_y1 - l_o
if y1 < text_height + text_offset_original + 2 * text_spaces:
y1 = orig_y1 + r_o + text_height + text_offset_original + 2 * text_spaces
x1 = orig_x1 + r_o
# add text background
(text_width, text_height), _ = cv2.getTextSize(f" {entity_name}", cv2.FONT_HERSHEY_COMPLEX, text_size, text_line)
text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2 = x1, y1 - (text_height + text_offset_original + 2 * text_spaces), x1 + text_width, y1
for prev_bbox in previous_bboxes:
while is_overlapping((text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2), prev_bbox):
text_bg_y1 += (text_height + text_offset_original + 2 * text_spaces)
text_bg_y2 += (text_height + text_offset_original + 2 * text_spaces)
y1 += (text_height + text_offset_original + 2 * text_spaces)
if text_bg_y2 >= image_h:
text_bg_y1 = max(0, image_h - (text_height + text_offset_original + 2 * text_spaces))
text_bg_y2 = image_h
y1 = image_h
break
alpha = 0.5
for i in range(text_bg_y1, text_bg_y2):
for j in range(text_bg_x1, text_bg_x2):
if i < image_h and j < image_w:
if j < text_bg_x1 + 1.35 * c_width:
# original color
bg_color = color
else:
# white
bg_color = [255, 255, 255]
new_image[i, j] = (alpha * new_image[i, j] + (1 - alpha) * np.array(bg_color)).astype(np.uint8)
cv2.putText(
new_image, f" {entity_name}", (x1, y1 - text_offset_original - 1 * text_spaces), cv2.FONT_HERSHEY_COMPLEX, text_size, (0, 0, 0), text_line, cv2.LINE_AA
)
# previous_locations.append((x1, y1))
previous_bboxes.append((text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2))
pil_image = Image.fromarray(new_image[:, :, [2, 1, 0]])
if save_path:
pil_image.save(save_path)
if show:
pil_image.show()
return new_image
# (The same image from the previous code example)
url = "https://huggingface.co/microsoft/kosmos-2-patch14-224/resolve/main/snowman.png"
image = Image.open(requests.get(url, stream=True).raw)
# From the previous code example
entities = [('a snowman', (12, 21), [(0.390625, 0.046875, 0.984375, 0.828125)]), ('a fire', (41, 47), [(0.171875, 0.015625, 0.484375, 0.890625)])]
# Draw the bounding bboxes
draw_entity_boxes_on_image(image, entities, show=True)
```
</details>
Here is the annotated image:
<a href="https://huggingface.co/microsoft/kosmos-2-patch14-224/resolve/main/annotated_snowman.jpg" target="_blank"><img src="https://huggingface.co/microsoft/kosmos-2-patch14-224/resolve/main/annotated_snowman.jpg" width="500"></a>
## BibTex and citation info
```
@article{kosmos-2,
title={Kosmos-2: Grounding Multimodal Large Language Models to the World},
author={Zhiliang Peng and Wenhui Wang and Li Dong and Yaru Hao and Shaohan Huang and Shuming Ma and Furu Wei},
journal={ArXiv},
year={2023},
volume={abs/2306}
}
@article{kosmos-1,
title={Language Is Not All You Need: Aligning Perception with Language Models},
author={Shaohan Huang and Li Dong and Wenhui Wang and Yaru Hao and Saksham Singhal and Shuming Ma and Tengchao Lv and Lei Cui and Owais Khan Mohammed and Qiang Liu and Kriti Aggarwal and Zewen Chi and Johan Bjorck and Vishrav Chaudhary and Subhojit Som and Xia Song and Furu Wei},
journal={ArXiv},
year={2023},
volume={abs/2302.14045}
}
@article{metalm,
title={Language Models are General-Purpose Interfaces},
author={Yaru Hao and Haoyu Song and Li Dong and Shaohan Huang and Zewen Chi and Wenhui Wang and Shuming Ma and Furu Wei},
journal={ArXiv},
year={2022},
volume={abs/2206.06336}
}
``` |
mradermacher/Limon-8B-GGUF | mradermacher | "2024-06-28T00:09:00Z" | 18,796 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:lodrick-the-lafted/Limon-8B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-27T13:44:35Z" | ---
base_model: lodrick-the-lafted/Limon-8B
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/lodrick-the-lafted/Limon-8B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Limon-8B-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Limon-8B-GGUF/resolve/main/Limon-8B.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Limon-8B-GGUF/resolve/main/Limon-8B.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Limon-8B-GGUF/resolve/main/Limon-8B.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Limon-8B-GGUF/resolve/main/Limon-8B.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Limon-8B-GGUF/resolve/main/Limon-8B.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Limon-8B-GGUF/resolve/main/Limon-8B.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Limon-8B-GGUF/resolve/main/Limon-8B.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Limon-8B-GGUF/resolve/main/Limon-8B.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/Limon-8B-GGUF/resolve/main/Limon-8B.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Limon-8B-GGUF/resolve/main/Limon-8B.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Limon-8B-GGUF/resolve/main/Limon-8B.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Limon-8B-GGUF/resolve/main/Limon-8B.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Limon-8B-GGUF/resolve/main/Limon-8B.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Limon-8B-GGUF/resolve/main/Limon-8B.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Limon-8B-GGUF/resolve/main/Limon-8B.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
mradermacher/Inixion-2x8B-v2-i1-GGUF | mradermacher | "2024-06-21T20:22:02Z" | 18,769 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:Alsebay/Inixion-2x8B-v2",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-21T18:10:20Z" | ---
base_model: Alsebay/Inixion-2x8B-v2
language:
- en
library_name: transformers
license: cc-by-nc-4.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/Alsebay/Inixion-2x8B-v2
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Inixion-2x8B-v2-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Inixion-2x8B-v2-i1-GGUF/resolve/main/Inixion-2x8B-v2.i1-IQ1_S.gguf) | i1-IQ1_S | 3.3 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Inixion-2x8B-v2-i1-GGUF/resolve/main/Inixion-2x8B-v2.i1-IQ1_M.gguf) | i1-IQ1_M | 3.5 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Inixion-2x8B-v2-i1-GGUF/resolve/main/Inixion-2x8B-v2.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Inixion-2x8B-v2-i1-GGUF/resolve/main/Inixion-2x8B-v2.i1-IQ2_XS.gguf) | i1-IQ2_XS | 4.3 | |
| [GGUF](https://huggingface.co/mradermacher/Inixion-2x8B-v2-i1-GGUF/resolve/main/Inixion-2x8B-v2.i1-IQ2_S.gguf) | i1-IQ2_S | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/Inixion-2x8B-v2-i1-GGUF/resolve/main/Inixion-2x8B-v2.i1-IQ2_M.gguf) | i1-IQ2_M | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/Inixion-2x8B-v2-i1-GGUF/resolve/main/Inixion-2x8B-v2.i1-Q2_K.gguf) | i1-Q2_K | 5.3 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Inixion-2x8B-v2-i1-GGUF/resolve/main/Inixion-2x8B-v2.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 5.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Inixion-2x8B-v2-i1-GGUF/resolve/main/Inixion-2x8B-v2.i1-IQ3_XS.gguf) | i1-IQ3_XS | 5.9 | |
| [GGUF](https://huggingface.co/mradermacher/Inixion-2x8B-v2-i1-GGUF/resolve/main/Inixion-2x8B-v2.i1-Q3_K_S.gguf) | i1-Q3_K_S | 6.2 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Inixion-2x8B-v2-i1-GGUF/resolve/main/Inixion-2x8B-v2.i1-IQ3_S.gguf) | i1-IQ3_S | 6.2 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Inixion-2x8B-v2-i1-GGUF/resolve/main/Inixion-2x8B-v2.i1-IQ3_M.gguf) | i1-IQ3_M | 6.3 | |
| [GGUF](https://huggingface.co/mradermacher/Inixion-2x8B-v2-i1-GGUF/resolve/main/Inixion-2x8B-v2.i1-Q3_K_M.gguf) | i1-Q3_K_M | 6.8 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Inixion-2x8B-v2-i1-GGUF/resolve/main/Inixion-2x8B-v2.i1-Q3_K_L.gguf) | i1-Q3_K_L | 7.3 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Inixion-2x8B-v2-i1-GGUF/resolve/main/Inixion-2x8B-v2.i1-IQ4_XS.gguf) | i1-IQ4_XS | 7.5 | |
| [GGUF](https://huggingface.co/mradermacher/Inixion-2x8B-v2-i1-GGUF/resolve/main/Inixion-2x8B-v2.i1-Q4_0.gguf) | i1-Q4_0 | 8.0 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Inixion-2x8B-v2-i1-GGUF/resolve/main/Inixion-2x8B-v2.i1-Q4_K_S.gguf) | i1-Q4_K_S | 8.0 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Inixion-2x8B-v2-i1-GGUF/resolve/main/Inixion-2x8B-v2.i1-Q4_K_M.gguf) | i1-Q4_K_M | 8.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Inixion-2x8B-v2-i1-GGUF/resolve/main/Inixion-2x8B-v2.i1-Q5_K_S.gguf) | i1-Q5_K_S | 9.6 | |
| [GGUF](https://huggingface.co/mradermacher/Inixion-2x8B-v2-i1-GGUF/resolve/main/Inixion-2x8B-v2.i1-Q5_K_M.gguf) | i1-Q5_K_M | 9.8 | |
| [GGUF](https://huggingface.co/mradermacher/Inixion-2x8B-v2-i1-GGUF/resolve/main/Inixion-2x8B-v2.i1-Q6_K.gguf) | i1-Q6_K | 11.3 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants.
<!-- end -->
|
Gryphe/MythoMax-L2-13b | Gryphe | "2024-04-21T17:42:57Z" | 18,739 | 236 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2023-08-10T20:35:34Z" | ---
license: other
language:
- en
---
With Llama 3 released, it's time for MythoMax to slowly fade away... [Let's do it in style!](https://suno.com/song/3d69cd72-e893-4193-866f-385f47778ce0)
An improved, potentially even perfected variant of MythoMix, my [MythoLogic-L2](https://huggingface.co/Gryphe/MythoLogic-L2-13b) and [Huginn](https://huggingface.co/The-Face-Of-Goonery/Huginn-13b-FP16) merge using a highly experimental tensor type merge technique. The main difference with MythoMix is that I allowed more of Huginn to intermingle with the single tensors located at the front and end of a model, resulting in increased coherency across the entire structure.
The script and the acccompanying templates I used to produce both can [be found here](https://github.com/Gryphe/BlockMerge_Gradient/tree/main/YAML).
This model is proficient at both roleplaying and storywriting due to its unique nature.
Quantized models are available from TheBloke: [GGUF](https://huggingface.co/TheBloke/MythoMax-L2-13B-GGUF) - [GPTQ](https://huggingface.co/TheBloke/MythoMax-L2-13B-GPTQ) - [AWQ](https://huggingface.co/TheBloke/MythoMax-L2-13B-AWQ) (You're the best!)
## Model details
The idea behind this merge is that each layer is composed of several tensors, which are in turn responsible for specific functions. Using MythoLogic-L2's robust understanding as its input and Huginn's extensive writing capability as its output seems to have resulted in a model that exceeds at both, confirming my theory. (More details to be released at a later time)
This type of merge is incapable of being illustrated, as each of its 363 tensors had an unique ratio applied to it. As with my prior merges, gradients were part of these ratios to further finetune its behaviour.
## Prompt Format
This model primarily uses Alpaca formatting, so for optimal model performance, use:
```
<System prompt/Character Card>
### Instruction:
Your instruction or question here.
For roleplay purposes, I suggest the following - Write <CHAR NAME>'s next reply in a chat between <YOUR NAME> and <CHAR NAME>. Write a single reply only.
### Response:
```
---
license: other
--- |
Yntec/IncredibleWorld2 | Yntec | "2024-03-26T20:49:41Z" | 18,715 | 2 | diffusers | [
"diffusers",
"safetensors",
"Art",
"Realism",
"Photo",
"wildzzz",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | "2023-12-05T05:33:17Z" | ---
license: creativeml-openrail-m
library_name: diffusers
pipeline_tag: text-to-image
tags:
- Art
- Realism
- Photo
- wildzzz
- stable-diffusion
- stable-diffusion-diffusers
- diffusers
- text-to-image
---
# Incredible World 2
Original page: https://civitai.com/models/143386?modelVersionId=163019
Sample and prompt:
Pretty cute girl with Father. halloween ingredients at a wooden brewery with a center colorful of mugs. Santa Claus sitting with little daughter in the dark copper. Display chef festive of keg scene accompanied by beer beer |
RichardErkhov/mncai_-_llama2-7b-dpo-v1-gguf | RichardErkhov | "2024-07-01T05:09:32Z" | 18,708 | 0 | null | [
"gguf",
"region:us"
] | null | "2024-07-01T00:21:54Z" | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
llama2-7b-dpo-v1 - GGUF
- Model creator: https://huggingface.co/mncai/
- Original model: https://huggingface.co/mncai/llama2-7b-dpo-v1/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [llama2-7b-dpo-v1.Q2_K.gguf](https://huggingface.co/RichardErkhov/mncai_-_llama2-7b-dpo-v1-gguf/blob/main/llama2-7b-dpo-v1.Q2_K.gguf) | Q2_K | 2.36GB |
| [llama2-7b-dpo-v1.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/mncai_-_llama2-7b-dpo-v1-gguf/blob/main/llama2-7b-dpo-v1.IQ3_XS.gguf) | IQ3_XS | 2.6GB |
| [llama2-7b-dpo-v1.IQ3_S.gguf](https://huggingface.co/RichardErkhov/mncai_-_llama2-7b-dpo-v1-gguf/blob/main/llama2-7b-dpo-v1.IQ3_S.gguf) | IQ3_S | 2.75GB |
| [llama2-7b-dpo-v1.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/mncai_-_llama2-7b-dpo-v1-gguf/blob/main/llama2-7b-dpo-v1.Q3_K_S.gguf) | Q3_K_S | 2.75GB |
| [llama2-7b-dpo-v1.IQ3_M.gguf](https://huggingface.co/RichardErkhov/mncai_-_llama2-7b-dpo-v1-gguf/blob/main/llama2-7b-dpo-v1.IQ3_M.gguf) | IQ3_M | 2.9GB |
| [llama2-7b-dpo-v1.Q3_K.gguf](https://huggingface.co/RichardErkhov/mncai_-_llama2-7b-dpo-v1-gguf/blob/main/llama2-7b-dpo-v1.Q3_K.gguf) | Q3_K | 3.07GB |
| [llama2-7b-dpo-v1.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/mncai_-_llama2-7b-dpo-v1-gguf/blob/main/llama2-7b-dpo-v1.Q3_K_M.gguf) | Q3_K_M | 3.07GB |
| [llama2-7b-dpo-v1.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/mncai_-_llama2-7b-dpo-v1-gguf/blob/main/llama2-7b-dpo-v1.Q3_K_L.gguf) | Q3_K_L | 3.35GB |
| [llama2-7b-dpo-v1.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/mncai_-_llama2-7b-dpo-v1-gguf/blob/main/llama2-7b-dpo-v1.IQ4_XS.gguf) | IQ4_XS | 3.4GB |
| [llama2-7b-dpo-v1.Q4_0.gguf](https://huggingface.co/RichardErkhov/mncai_-_llama2-7b-dpo-v1-gguf/blob/main/llama2-7b-dpo-v1.Q4_0.gguf) | Q4_0 | 3.56GB |
| [llama2-7b-dpo-v1.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/mncai_-_llama2-7b-dpo-v1-gguf/blob/main/llama2-7b-dpo-v1.IQ4_NL.gguf) | IQ4_NL | 3.58GB |
| [llama2-7b-dpo-v1.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/mncai_-_llama2-7b-dpo-v1-gguf/blob/main/llama2-7b-dpo-v1.Q4_K_S.gguf) | Q4_K_S | 3.59GB |
| [llama2-7b-dpo-v1.Q4_K.gguf](https://huggingface.co/RichardErkhov/mncai_-_llama2-7b-dpo-v1-gguf/blob/main/llama2-7b-dpo-v1.Q4_K.gguf) | Q4_K | 3.8GB |
| [llama2-7b-dpo-v1.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/mncai_-_llama2-7b-dpo-v1-gguf/blob/main/llama2-7b-dpo-v1.Q4_K_M.gguf) | Q4_K_M | 3.8GB |
| [llama2-7b-dpo-v1.Q4_1.gguf](https://huggingface.co/RichardErkhov/mncai_-_llama2-7b-dpo-v1-gguf/blob/main/llama2-7b-dpo-v1.Q4_1.gguf) | Q4_1 | 3.95GB |
| [llama2-7b-dpo-v1.Q5_0.gguf](https://huggingface.co/RichardErkhov/mncai_-_llama2-7b-dpo-v1-gguf/blob/main/llama2-7b-dpo-v1.Q5_0.gguf) | Q5_0 | 4.33GB |
| [llama2-7b-dpo-v1.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/mncai_-_llama2-7b-dpo-v1-gguf/blob/main/llama2-7b-dpo-v1.Q5_K_S.gguf) | Q5_K_S | 4.33GB |
| [llama2-7b-dpo-v1.Q5_K.gguf](https://huggingface.co/RichardErkhov/mncai_-_llama2-7b-dpo-v1-gguf/blob/main/llama2-7b-dpo-v1.Q5_K.gguf) | Q5_K | 4.45GB |
| [llama2-7b-dpo-v1.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/mncai_-_llama2-7b-dpo-v1-gguf/blob/main/llama2-7b-dpo-v1.Q5_K_M.gguf) | Q5_K_M | 4.45GB |
| [llama2-7b-dpo-v1.Q5_1.gguf](https://huggingface.co/RichardErkhov/mncai_-_llama2-7b-dpo-v1-gguf/blob/main/llama2-7b-dpo-v1.Q5_1.gguf) | Q5_1 | 4.72GB |
| [llama2-7b-dpo-v1.Q6_K.gguf](https://huggingface.co/RichardErkhov/mncai_-_llama2-7b-dpo-v1-gguf/blob/main/llama2-7b-dpo-v1.Q6_K.gguf) | Q6_K | 5.15GB |
| [llama2-7b-dpo-v1.Q8_0.gguf](https://huggingface.co/RichardErkhov/mncai_-_llama2-7b-dpo-v1-gguf/blob/main/llama2-7b-dpo-v1.Q8_0.gguf) | Q8_0 | 6.67GB |
Original model description:
Entry not found
|
RichardErkhov/jhflow_-_mistral7b-lora-multiturn-v4-gguf | RichardErkhov | "2024-07-01T07:22:53Z" | 18,687 | 0 | null | [
"gguf",
"region:us"
] | null | "2024-07-01T05:19:16Z" | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
mistral7b-lora-multiturn-v4 - GGUF
- Model creator: https://huggingface.co/jhflow/
- Original model: https://huggingface.co/jhflow/mistral7b-lora-multiturn-v4/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [mistral7b-lora-multiturn-v4.Q2_K.gguf](https://huggingface.co/RichardErkhov/jhflow_-_mistral7b-lora-multiturn-v4-gguf/blob/main/mistral7b-lora-multiturn-v4.Q2_K.gguf) | Q2_K | 2.53GB |
| [mistral7b-lora-multiturn-v4.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/jhflow_-_mistral7b-lora-multiturn-v4-gguf/blob/main/mistral7b-lora-multiturn-v4.IQ3_XS.gguf) | IQ3_XS | 2.81GB |
| [mistral7b-lora-multiturn-v4.IQ3_S.gguf](https://huggingface.co/RichardErkhov/jhflow_-_mistral7b-lora-multiturn-v4-gguf/blob/main/mistral7b-lora-multiturn-v4.IQ3_S.gguf) | IQ3_S | 2.96GB |
| [mistral7b-lora-multiturn-v4.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/jhflow_-_mistral7b-lora-multiturn-v4-gguf/blob/main/mistral7b-lora-multiturn-v4.Q3_K_S.gguf) | Q3_K_S | 2.95GB |
| [mistral7b-lora-multiturn-v4.IQ3_M.gguf](https://huggingface.co/RichardErkhov/jhflow_-_mistral7b-lora-multiturn-v4-gguf/blob/main/mistral7b-lora-multiturn-v4.IQ3_M.gguf) | IQ3_M | 3.06GB |
| [mistral7b-lora-multiturn-v4.Q3_K.gguf](https://huggingface.co/RichardErkhov/jhflow_-_mistral7b-lora-multiturn-v4-gguf/blob/main/mistral7b-lora-multiturn-v4.Q3_K.gguf) | Q3_K | 3.28GB |
| [mistral7b-lora-multiturn-v4.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/jhflow_-_mistral7b-lora-multiturn-v4-gguf/blob/main/mistral7b-lora-multiturn-v4.Q3_K_M.gguf) | Q3_K_M | 3.28GB |
| [mistral7b-lora-multiturn-v4.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/jhflow_-_mistral7b-lora-multiturn-v4-gguf/blob/main/mistral7b-lora-multiturn-v4.Q3_K_L.gguf) | Q3_K_L | 3.56GB |
| [mistral7b-lora-multiturn-v4.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/jhflow_-_mistral7b-lora-multiturn-v4-gguf/blob/main/mistral7b-lora-multiturn-v4.IQ4_XS.gguf) | IQ4_XS | 3.67GB |
| [mistral7b-lora-multiturn-v4.Q4_0.gguf](https://huggingface.co/RichardErkhov/jhflow_-_mistral7b-lora-multiturn-v4-gguf/blob/main/mistral7b-lora-multiturn-v4.Q4_0.gguf) | Q4_0 | 3.83GB |
| [mistral7b-lora-multiturn-v4.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/jhflow_-_mistral7b-lora-multiturn-v4-gguf/blob/main/mistral7b-lora-multiturn-v4.IQ4_NL.gguf) | IQ4_NL | 3.87GB |
| [mistral7b-lora-multiturn-v4.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/jhflow_-_mistral7b-lora-multiturn-v4-gguf/blob/main/mistral7b-lora-multiturn-v4.Q4_K_S.gguf) | Q4_K_S | 3.86GB |
| [mistral7b-lora-multiturn-v4.Q4_K.gguf](https://huggingface.co/RichardErkhov/jhflow_-_mistral7b-lora-multiturn-v4-gguf/blob/main/mistral7b-lora-multiturn-v4.Q4_K.gguf) | Q4_K | 4.07GB |
| [mistral7b-lora-multiturn-v4.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/jhflow_-_mistral7b-lora-multiturn-v4-gguf/blob/main/mistral7b-lora-multiturn-v4.Q4_K_M.gguf) | Q4_K_M | 4.07GB |
| [mistral7b-lora-multiturn-v4.Q4_1.gguf](https://huggingface.co/RichardErkhov/jhflow_-_mistral7b-lora-multiturn-v4-gguf/blob/main/mistral7b-lora-multiturn-v4.Q4_1.gguf) | Q4_1 | 4.24GB |
| [mistral7b-lora-multiturn-v4.Q5_0.gguf](https://huggingface.co/RichardErkhov/jhflow_-_mistral7b-lora-multiturn-v4-gguf/blob/main/mistral7b-lora-multiturn-v4.Q5_0.gguf) | Q5_0 | 4.65GB |
| [mistral7b-lora-multiturn-v4.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/jhflow_-_mistral7b-lora-multiturn-v4-gguf/blob/main/mistral7b-lora-multiturn-v4.Q5_K_S.gguf) | Q5_K_S | 4.65GB |
| [mistral7b-lora-multiturn-v4.Q5_K.gguf](https://huggingface.co/RichardErkhov/jhflow_-_mistral7b-lora-multiturn-v4-gguf/blob/main/mistral7b-lora-multiturn-v4.Q5_K.gguf) | Q5_K | 4.78GB |
| [mistral7b-lora-multiturn-v4.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/jhflow_-_mistral7b-lora-multiturn-v4-gguf/blob/main/mistral7b-lora-multiturn-v4.Q5_K_M.gguf) | Q5_K_M | 4.78GB |
| [mistral7b-lora-multiturn-v4.Q5_1.gguf](https://huggingface.co/RichardErkhov/jhflow_-_mistral7b-lora-multiturn-v4-gguf/blob/main/mistral7b-lora-multiturn-v4.Q5_1.gguf) | Q5_1 | 5.07GB |
| [mistral7b-lora-multiturn-v4.Q6_K.gguf](https://huggingface.co/RichardErkhov/jhflow_-_mistral7b-lora-multiturn-v4-gguf/blob/main/mistral7b-lora-multiturn-v4.Q6_K.gguf) | Q6_K | 5.53GB |
| [mistral7b-lora-multiturn-v4.Q8_0.gguf](https://huggingface.co/RichardErkhov/jhflow_-_mistral7b-lora-multiturn-v4-gguf/blob/main/mistral7b-lora-multiturn-v4.Q8_0.gguf) | Q8_0 | 7.17GB |
Original model description:
- Original Model : maywell/Synatra-7B-v0.3-dpo
- Prompt format : ChatML (same as the original model)
|
facebook/deit-small-patch16-224 | facebook | "2022-07-13T11:41:40Z" | 18,685 | 6 | transformers | [
"transformers",
"pytorch",
"tf",
"vit",
"image-classification",
"dataset:imagenet-1k",
"arxiv:2012.12877",
"arxiv:2006.03677",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | "2022-03-02T23:29:05Z" | ---
license: apache-2.0
tags:
- image-classification
datasets:
- imagenet-1k
---
# Data-efficient Image Transformer (small-sized model)
Data-efficient Image Transformer (DeiT) model pre-trained and fine-tuned on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877) by Touvron et al. and first released in [this repository](https://github.com/facebookresearch/deit). However, the weights were converted from the [timm repository](https://github.com/rwightman/pytorch-image-models) by Ross Wightman.
Disclaimer: The team releasing DeiT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
This model is actually a more efficiently trained Vision Transformer (ViT).
The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pre-trained and fine-tuned on a large collection of images in a supervised fashion, namely ImageNet-1k, at a resolution of 224x224 pixels.
Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.
By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=facebook/deit) to look for
fine-tuned versions on a task that interests you.
### How to use
Since this model is a more efficiently trained ViT model, you can plug it into ViTModel or ViTForImageClassification. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appropriate feature extractor given the model name.
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import AutoFeatureExtractor, ViTForImageClassification
from PIL import Image
import requests
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = AutoFeatureExtractor.from_pretrained('facebook/deit-small-patch16-224')
model = ViTForImageClassification.from_pretrained('facebook/deit-small-patch16-224')
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
# model predicts one of the 1000 ImageNet classes
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
```
Currently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon.
## Training data
The ViT model was pretrained on [ImageNet-1k](http://www.image-net.org/challenges/LSVRC/2012/), a dataset consisting of 1 million images and 1k classes.
## Training procedure
### Preprocessing
The exact details of preprocessing of images during training/validation can be found [here](https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L78).
At inference time, images are resized/rescaled to the same resolution (256x256), center-cropped at 224x224 and normalized across the RGB channels with the ImageNet mean and standard deviation.
### Pretraining
The model was trained on a single 8-GPU node for 3 days. Training resolution is 224. For all hyperparameters (such as batch size and learning rate) we refer to table 9 of the original paper.
## Evaluation results
| Model | ImageNet top-1 accuracy | ImageNet top-5 accuracy | # params | URL |
|---------------------------------------|-------------------------|-------------------------|----------|------------------------------------------------------------------|
| DeiT-tiny | 72.2 | 91.1 | 5M | https://huggingface.co/facebook/deit-tiny-patch16-224 |
| **DeiT-small** | **79.9** | **95.0** | **22M** | **https://huggingface.co/facebook/deit-small-patch16-224** |
| DeiT-base | 81.8 | 95.6 | 86M | https://huggingface.co/facebook/deit-base-patch16-224 |
| DeiT-tiny distilled | 74.5 | 91.9 | 6M | https://huggingface.co/facebook/deit-tiny-distilled-patch16-224 |
| DeiT-small distilled | 81.2 | 95.4 | 22M | https://huggingface.co/facebook/deit-small-distilled-patch16-224 |
| DeiT-base distilled | 83.4 | 96.5 | 87M | https://huggingface.co/facebook/deit-base-distilled-patch16-224 |
| DeiT-base 384 | 82.9 | 96.2 | 87M | https://huggingface.co/facebook/deit-base-patch16-384 |
| DeiT-base distilled 384 (1000 epochs) | 85.2 | 97.2 | 88M | https://huggingface.co/facebook/deit-base-distilled-patch16-384 |
Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.
### BibTeX entry and citation info
```bibtex
@misc{touvron2021training,
title={Training data-efficient image transformers & distillation through attention},
author={Hugo Touvron and Matthieu Cord and Matthijs Douze and Francisco Massa and Alexandre Sablayrolles and Hervé Jégou},
year={2021},
eprint={2012.12877},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
```bibtex
@misc{wu2020visual,
title={Visual Transformers: Token-based Image Representation and Processing for Computer Vision},
author={Bichen Wu and Chenfeng Xu and Xiaoliang Dai and Alvin Wan and Peizhao Zhang and Zhicheng Yan and Masayoshi Tomizuka and Joseph Gonzalez and Kurt Keutzer and Peter Vajda},
year={2020},
eprint={2006.03677},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
```bibtex
@inproceedings{deng2009imagenet,
title={Imagenet: A large-scale hierarchical image database},
author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},
booktitle={2009 IEEE conference on computer vision and pattern recognition},
pages={248--255},
year={2009},
organization={Ieee}
}
``` |
mradermacher/L3-11.5B-DuS-MoonRoot-i1-GGUF | mradermacher | "2024-06-29T11:04:06Z" | 18,678 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"llama",
"en",
"base_model:v000000/L3-11.5B-DuS-MoonRoot",
"endpoints_compatible",
"region:us"
] | null | "2024-06-29T07:03:15Z" | ---
base_model: v000000/L3-11.5B-DuS-MoonRoot
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
- llama
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/v000000/L3-11.5B-DuS-MoonRoot
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/L3-11.5B-DuS-MoonRoot-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/L3-11.5B-DuS-MoonRoot-i1-GGUF/resolve/main/L3-11.5B-DuS-MoonRoot.i1-IQ1_S.gguf) | i1-IQ1_S | 2.9 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/L3-11.5B-DuS-MoonRoot-i1-GGUF/resolve/main/L3-11.5B-DuS-MoonRoot.i1-IQ1_M.gguf) | i1-IQ1_M | 3.1 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/L3-11.5B-DuS-MoonRoot-i1-GGUF/resolve/main/L3-11.5B-DuS-MoonRoot.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/L3-11.5B-DuS-MoonRoot-i1-GGUF/resolve/main/L3-11.5B-DuS-MoonRoot.i1-IQ2_XS.gguf) | i1-IQ2_XS | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/L3-11.5B-DuS-MoonRoot-i1-GGUF/resolve/main/L3-11.5B-DuS-MoonRoot.i1-IQ2_S.gguf) | i1-IQ2_S | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/L3-11.5B-DuS-MoonRoot-i1-GGUF/resolve/main/L3-11.5B-DuS-MoonRoot.i1-IQ2_M.gguf) | i1-IQ2_M | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/L3-11.5B-DuS-MoonRoot-i1-GGUF/resolve/main/L3-11.5B-DuS-MoonRoot.i1-Q2_K.gguf) | i1-Q2_K | 4.6 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/L3-11.5B-DuS-MoonRoot-i1-GGUF/resolve/main/L3-11.5B-DuS-MoonRoot.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 4.7 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/L3-11.5B-DuS-MoonRoot-i1-GGUF/resolve/main/L3-11.5B-DuS-MoonRoot.i1-IQ3_XS.gguf) | i1-IQ3_XS | 5.0 | |
| [GGUF](https://huggingface.co/mradermacher/L3-11.5B-DuS-MoonRoot-i1-GGUF/resolve/main/L3-11.5B-DuS-MoonRoot.i1-Q3_K_S.gguf) | i1-Q3_K_S | 5.3 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/L3-11.5B-DuS-MoonRoot-i1-GGUF/resolve/main/L3-11.5B-DuS-MoonRoot.i1-IQ3_S.gguf) | i1-IQ3_S | 5.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/L3-11.5B-DuS-MoonRoot-i1-GGUF/resolve/main/L3-11.5B-DuS-MoonRoot.i1-IQ3_M.gguf) | i1-IQ3_M | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/L3-11.5B-DuS-MoonRoot-i1-GGUF/resolve/main/L3-11.5B-DuS-MoonRoot.i1-Q3_K_M.gguf) | i1-Q3_K_M | 5.8 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/L3-11.5B-DuS-MoonRoot-i1-GGUF/resolve/main/L3-11.5B-DuS-MoonRoot.i1-Q3_K_L.gguf) | i1-Q3_K_L | 6.3 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/L3-11.5B-DuS-MoonRoot-i1-GGUF/resolve/main/L3-11.5B-DuS-MoonRoot.i1-IQ4_XS.gguf) | i1-IQ4_XS | 6.4 | |
| [GGUF](https://huggingface.co/mradermacher/L3-11.5B-DuS-MoonRoot-i1-GGUF/resolve/main/L3-11.5B-DuS-MoonRoot.i1-Q4_0.gguf) | i1-Q4_0 | 6.7 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/L3-11.5B-DuS-MoonRoot-i1-GGUF/resolve/main/L3-11.5B-DuS-MoonRoot.i1-Q4_K_S.gguf) | i1-Q4_K_S | 6.8 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/L3-11.5B-DuS-MoonRoot-i1-GGUF/resolve/main/L3-11.5B-DuS-MoonRoot.i1-Q4_K_M.gguf) | i1-Q4_K_M | 7.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/L3-11.5B-DuS-MoonRoot-i1-GGUF/resolve/main/L3-11.5B-DuS-MoonRoot.i1-Q5_K_S.gguf) | i1-Q5_K_S | 8.1 | |
| [GGUF](https://huggingface.co/mradermacher/L3-11.5B-DuS-MoonRoot-i1-GGUF/resolve/main/L3-11.5B-DuS-MoonRoot.i1-Q5_K_M.gguf) | i1-Q5_K_M | 8.3 | |
| [GGUF](https://huggingface.co/mradermacher/L3-11.5B-DuS-MoonRoot-i1-GGUF/resolve/main/L3-11.5B-DuS-MoonRoot.i1-Q6_K.gguf) | i1-Q6_K | 9.6 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants.
<!-- end -->
|
nomic-ai/nomic-embed-text-v1.5-GGUF | nomic-ai | "2024-02-15T17:36:48Z" | 18,676 | 28 | null | [
"gguf",
"feature-extraction",
"sentence-similarity",
"en",
"base_model:nomic-ai/nomic-embed-text-v1.5",
"license:apache-2.0",
"region:us"
] | sentence-similarity | "2024-02-14T21:55:09Z" | ---
base_model: nomic-ai/nomic-embed-text-v1.5
inference: false
language:
- en
license: apache-2.0
model_creator: Nomic
model_name: nomic-embed-text-v1.5
model_type: bert
pipeline_tag: sentence-similarity
quantized_by: Nomic
tags:
- feature-extraction
- sentence-similarity
---
***
**Note**: For compatiblity with current llama.cpp, please download the files published on 2/15/2024. The files originally published here will fail to load.
***
<br/>
# nomic-embed-text-v1.5 - GGUF
Original model: [nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5)
## Description
This repo contains llama.cpp-compatible files for [nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) in GGUF format.
llama.cpp will default to 2048 tokens of context with these files. To use the full 8192 tokens that Nomic Embed is benchmarked on, you will have to choose a context extension method. The original model uses Dynamic NTK-Aware RoPE scaling, but that is not currently available in llama.cpp. A combination of YaRN and linear scaling is an acceptable substitute.
These files were converted and quantized with llama.cpp [PR 5500](https://github.com/ggerganov/llama.cpp/pull/5500), commit [34aa045de](https://github.com/ggerganov/llama.cpp/pull/5500/commits/34aa045de44271ff7ad42858c75739303b8dc6eb).
## Example `llama.cpp` Command
Compute a single embedding:
```shell
./embedding -ngl 99 -m nomic-embed-text-v1.5.f16.gguf -c 8192 -b 8192 --rope-scaling yarn --rope-freq-scale .75 -p 'search_query: What is TSNE?'
```
You can also submit a batch of texts to embed, as long as the total number of tokens does not exceed the context length. Only the first three embeddings are shown by the `embedding` example.
texts.txt:
```
search_query: What is TSNE?
search_query: Who is Laurens Van der Maaten?
```
Compute multiple embeddings:
```shell
./embedding -ngl 99 -m nomic-embed-text-v1.5.f16.gguf -c 8192 -b 8192 --rope-scaling yarn --rope-freq-scale .75 -f texts.txt
```
## Compatibility
These files are compatible with llama.cpp as of commit [4524290e8](https://github.com/ggerganov/llama.cpp/commit/4524290e87b8e107cc2b56e1251751546f4b9051) from 2/15/2024.
## Provided Files
The below table shows the mean squared error of the embeddings produced by these quantizations of Nomic Embed relative to the Sentence Transformers implementation.
Name | Quant | Size | MSE
-----|-------|------|-----
[nomic-embed-text-v1.5.Q2\_K.gguf](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5-GGUF/blob/main/nomic-embed-text-v1.5.Q2_K.gguf) | Q2\_K | 48 MiB | 2.33e-03
[nomic-embed-text-v1.5.Q3\_K\_S.gguf](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5-GGUF/blob/main/nomic-embed-text-v1.5.Q3_K_S.gguf) | Q3\_K\_S | 57 MiB | 1.19e-03
[nomic-embed-text-v1.5.Q3\_K\_M.gguf](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5-GGUF/blob/main/nomic-embed-text-v1.5.Q3_K_M.gguf) | Q3\_K\_M | 65 MiB | 8.26e-04
[nomic-embed-text-v1.5.Q3\_K\_L.gguf](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5-GGUF/blob/main/nomic-embed-text-v1.5.Q3_K_L.gguf) | Q3\_K\_L | 69 MiB | 7.93e-04
[nomic-embed-text-v1.5.Q4\_0.gguf](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5-GGUF/blob/main/nomic-embed-text-v1.5.Q4_0.gguf) | Q4\_0 | 75 MiB | 6.32e-04
[nomic-embed-text-v1.5.Q4\_K\_S.gguf](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5-GGUF/blob/main/nomic-embed-text-v1.5.Q4_K_S.gguf) | Q4\_K\_S | 75 MiB | 6.71e-04
[nomic-embed-text-v1.5.Q4\_K\_M.gguf](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5-GGUF/blob/main/nomic-embed-text-v1.5.Q4_K_M.gguf) | Q4\_K\_M | 81 MiB | 2.42e-04
[nomic-embed-text-v1.5.Q5\_0.gguf](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5-GGUF/blob/main/nomic-embed-text-v1.5.Q5_0.gguf) | Q5\_0 | 91 MiB | 2.35e-04
[nomic-embed-text-v1.5.Q5\_K\_S.gguf](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5-GGUF/blob/main/nomic-embed-text-v1.5.Q5_K_S.gguf) | Q5\_K\_S | 91 MiB | 2.00e-04
[nomic-embed-text-v1.5.Q5\_K\_M.gguf](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5-GGUF/blob/main/nomic-embed-text-v1.5.Q5_K_M.gguf) | Q5\_K\_M | 95 MiB | 6.55e-05
[nomic-embed-text-v1.5.Q6\_K.gguf](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5-GGUF/blob/main/nomic-embed-text-v1.5.Q6_K.gguf) | Q6\_K | 108 MiB | 5.58e-05
[nomic-embed-text-v1.5.Q8\_0.gguf](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5-GGUF/blob/main/nomic-embed-text-v1.5.Q8_0.gguf) | Q8\_0 | 140 MiB | 5.79e-06
[nomic-embed-text-v1.5.f16.gguf](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5-GGUF/blob/main/nomic-embed-text-v1.5.f16.gguf) | F16 | 262 MiB | 4.21e-10
[nomic-embed-text-v1.5.f32.gguf](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5-GGUF/blob/main/nomic-embed-text-v1.5.f32.gguf) | F32 | 262 MiB | 6.08e-11
|
bartowski/buddhi-128k-chat-7b-GGUF | bartowski | "2024-06-20T17:10:27Z" | 18,651 | 4 | null | [
"gguf",
"text-generation",
"license:apache-2.0",
"region:us"
] | text-generation | "2024-06-20T12:38:14Z" | ---
license: apache-2.0
pipeline_tag: text-generation
quantized_by: bartowski
---
## Llamacpp imatrix Quantizations of buddhi-128k-chat-7b
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b3166">b3166</a> for quantization.
Original model: https://huggingface.co/aiplanet/buddhi-128k-chat-7b
All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8)
## Prompt format
```
<s> [INST] {prompt} [/INST]</s>
```
Note that this model does not support a System prompt.
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [buddhi-128k-chat-7b-Q8_0_L.gguf](https://huggingface.co/bartowski/buddhi-128k-chat-7b-GGUF/blob/main/buddhi-128k-chat-7b-Q8_0_L.gguf) | Q8_0_L | 7.94GB | *Experimental*, uses f16 for embed and output weights. Please provide any feedback of differences. Extremely high quality, generally unneeded but max available quant. |
| [buddhi-128k-chat-7b-Q8_0.gguf](https://huggingface.co/bartowski/buddhi-128k-chat-7b-GGUF/blob/main/buddhi-128k-chat-7b-Q8_0.gguf) | Q8_0 | 7.69GB | Extremely high quality, generally unneeded but max available quant. |
| [buddhi-128k-chat-7b-Q6_K_L.gguf](https://huggingface.co/bartowski/buddhi-128k-chat-7b-GGUF/blob/main/buddhi-128k-chat-7b-Q6_K_L.gguf) | Q6_K_L | 6.25GB | *Experimental*, uses f16 for embed and output weights. Please provide any feedback of differences. Very high quality, near perfect, *recommended*. |
| [buddhi-128k-chat-7b-Q6_K.gguf](https://huggingface.co/bartowski/buddhi-128k-chat-7b-GGUF/blob/main/buddhi-128k-chat-7b-Q6_K.gguf) | Q6_K | 5.94GB | Very high quality, near perfect, *recommended*. |
| [buddhi-128k-chat-7b-Q5_K_L.gguf](https://huggingface.co/bartowski/buddhi-128k-chat-7b-GGUF/blob/main/buddhi-128k-chat-7b-Q5_K_L.gguf) | Q5_K_L | 5.45GB | *Experimental*, uses f16 for embed and output weights. Please provide any feedback of differences. High quality, *recommended*. |
| [buddhi-128k-chat-7b-Q5_K_M.gguf](https://huggingface.co/bartowski/buddhi-128k-chat-7b-GGUF/blob/main/buddhi-128k-chat-7b-Q5_K_M.gguf) | Q5_K_M | 5.13GB | High quality, *recommended*. |
| [buddhi-128k-chat-7b-Q5_K_S.gguf](https://huggingface.co/bartowski/buddhi-128k-chat-7b-GGUF/blob/main/buddhi-128k-chat-7b-Q5_K_S.gguf) | Q5_K_S | 4.99GB | High quality, *recommended*. |
| [buddhi-128k-chat-7b-Q4_K_L.gguf](https://huggingface.co/bartowski/buddhi-128k-chat-7b-GGUF/blob/main/buddhi-128k-chat-7b-Q4_K_L.gguf) | Q4_K_L | 4.71GB | *Experimental*, uses f16 for embed and output weights. Please provide any feedback of differences. Good quality, uses about 4.83 bits per weight, *recommended*. |
| [buddhi-128k-chat-7b-Q4_K_M.gguf](https://huggingface.co/bartowski/buddhi-128k-chat-7b-GGUF/blob/main/buddhi-128k-chat-7b-Q4_K_M.gguf) | Q4_K_M | 4.36GB | Good quality, uses about 4.83 bits per weight, *recommended*. |
| [buddhi-128k-chat-7b-Q4_K_S.gguf](https://huggingface.co/bartowski/buddhi-128k-chat-7b-GGUF/blob/main/buddhi-128k-chat-7b-Q4_K_S.gguf) | Q4_K_S | 4.14GB | Slightly lower quality with more space savings, *recommended*. |
| [buddhi-128k-chat-7b-IQ4_XS.gguf](https://huggingface.co/bartowski/buddhi-128k-chat-7b-GGUF/blob/main/buddhi-128k-chat-7b-IQ4_XS.gguf) | IQ4_XS | 3.90GB | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [buddhi-128k-chat-7b-Q3_K_L.gguf](https://huggingface.co/bartowski/buddhi-128k-chat-7b-GGUF/blob/main/buddhi-128k-chat-7b-Q3_K_L.gguf) | Q3_K_L | 3.82GB | Lower quality but usable, good for low RAM availability. |
| [buddhi-128k-chat-7b-Q3_K_M.gguf](https://huggingface.co/bartowski/buddhi-128k-chat-7b-GGUF/blob/main/buddhi-128k-chat-7b-Q3_K_M.gguf) | Q3_K_M | 3.51GB | Even lower quality. |
| [buddhi-128k-chat-7b-IQ3_M.gguf](https://huggingface.co/bartowski/buddhi-128k-chat-7b-GGUF/blob/main/buddhi-128k-chat-7b-IQ3_M.gguf) | IQ3_M | 3.28GB | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [buddhi-128k-chat-7b-Q3_K_S.gguf](https://huggingface.co/bartowski/buddhi-128k-chat-7b-GGUF/blob/main/buddhi-128k-chat-7b-Q3_K_S.gguf) | Q3_K_S | 3.16GB | Low quality, not recommended. |
| [buddhi-128k-chat-7b-IQ3_XS.gguf](https://huggingface.co/bartowski/buddhi-128k-chat-7b-GGUF/blob/main/buddhi-128k-chat-7b-IQ3_XS.gguf) | IQ3_XS | 3.01GB | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| [buddhi-128k-chat-7b-IQ3_XXS.gguf](https://huggingface.co/bartowski/buddhi-128k-chat-7b-GGUF/blob/main/buddhi-128k-chat-7b-IQ3_XXS.gguf) | IQ3_XXS | 2.82GB | Lower quality, new method with decent performance, comparable to Q3 quants. |
| [buddhi-128k-chat-7b-Q2_K.gguf](https://huggingface.co/bartowski/buddhi-128k-chat-7b-GGUF/blob/main/buddhi-128k-chat-7b-Q2_K.gguf) | Q2_K | 2.71GB | Very low quality but surprisingly usable. |
| [buddhi-128k-chat-7b-IQ2_M.gguf](https://huggingface.co/bartowski/buddhi-128k-chat-7b-GGUF/blob/main/buddhi-128k-chat-7b-IQ2_M.gguf) | IQ2_M | 2.50GB | Very low quality, uses SOTA techniques to also be surprisingly usable. |
| [buddhi-128k-chat-7b-IQ2_S.gguf](https://huggingface.co/bartowski/buddhi-128k-chat-7b-GGUF/blob/main/buddhi-128k-chat-7b-IQ2_S.gguf) | IQ2_S | 2.31GB | Very low quality, uses SOTA techniques to be usable. |
| [buddhi-128k-chat-7b-IQ2_XS.gguf](https://huggingface.co/bartowski/buddhi-128k-chat-7b-GGUF/blob/main/buddhi-128k-chat-7b-IQ2_XS.gguf) | IQ2_XS | 2.19GB | Very low quality, uses SOTA techniques to be usable. |
## Downloading using huggingface-cli
First, make sure you have hugginface-cli installed:
```
pip install -U "huggingface_hub[cli]"
```
Then, you can target the specific file you want:
```
huggingface-cli download bartowski/buddhi-128k-chat-7b-GGUF --include "buddhi-128k-chat-7b-Q4_K_M.gguf" --local-dir ./
```
If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:
```
huggingface-cli download bartowski/buddhi-128k-chat-7b-GGUF --include "buddhi-128k-chat-7b-Q8_0.gguf/*" --local-dir buddhi-128k-chat-7b-Q8_0
```
You can either specify a new local-dir (buddhi-128k-chat-7b-Q8_0) or download them all in place (./)
## Which file should I choose?
A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)
The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.
If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.
If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.
Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.
If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.
If you want to get more into the weeds, you can check out this extremely useful feature chart:
[llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix)
But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.
These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.
The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm.
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
|
M-CLIP/XLM-Roberta-Large-Vit-L-14 | M-CLIP | "2022-09-15T10:44:59Z" | 18,644 | 10 | transformers | [
"transformers",
"pytorch",
"tf",
"multilingual",
"af",
"sq",
"am",
"ar",
"az",
"bn",
"bs",
"bg",
"ca",
"zh",
"hr",
"cs",
"da",
"nl",
"en",
"et",
"fr",
"de",
"el",
"hi",
"hu",
"is",
"id",
"it",
"ja",
"mk",
"ml",
"mr",
"pl",
"pt",
"ro",
"ru",
"sr",
"sl",
"es",
"sw",
"sv",
"tl",
"te",
"tr",
"tk",
"uk",
"ur",
"ug",
"uz",
"vi",
"xh",
"endpoints_compatible",
"region:us"
] | null | "2022-05-30T14:35:41Z" | ---
language:
- multilingual
- af
- sq
- am
- ar
- az
- bn
- bs
- bg
- ca
- zh
- hr
- cs
- da
- nl
- en
- et
- fr
- de
- el
- hi
- hu
- is
- id
- it
- ja
- mk
- ml
- mr
- pl
- pt
- ro
- ru
- sr
- sl
- es
- sw
- sv
- tl
- te
- tr
- tk
- uk
- ur
- ug
- uz
- vi
- xh
---
## Multilingual-clip: XLM-Roberta-Large-Vit-L-14
Multilingual-CLIP extends OpenAI's English text encoders to multiple other languages. This model *only* contains the multilingual text encoder. The corresponding image model `ViT-L-14` can be retrieved via instructions found on OpenAI's [CLIP repository on Github](https://github.com/openai/CLIP). We provide a usage example below.
## Requirements
To use both the multilingual text encoder and corresponding image encoder, we need to install the packages [`multilingual-clip`](https://github.com/FreddeFrallan/Multilingual-CLIP) and [`clip`](https://github.com/openai/CLIP).
```
pip install multilingual-clip
pip install git+https://github.com/openai/CLIP.git
```
## Usage
Extracting embeddings from the text encoder can be done in the following way:
```python
from multilingual_clip import pt_multilingual_clip
import transformers
texts = [
'Three blind horses listening to Mozart.',
'Älgen är skogens konung!',
'Wie leben Eisbären in der Antarktis?',
'Вы знали, что все белые медведи левши?'
]
model_name = 'M-CLIP/XLM-Roberta-Large-Vit-L-14'
# Load Model & Tokenizer
model = pt_multilingual_clip.MultilingualCLIP.from_pretrained(model_name)
tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
embeddings = model.forward(texts, tokenizer)
print("Text features shape:", embeddings.shape)
```
Extracting embeddings from the corresponding image encoder:
```python
import torch
import clip
import requests
from PIL import Image
device = "cuda" if torch.cuda.is_available() else "cpu"
model, preprocess = clip.load("ViT-L/14", device=device)
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
image = preprocess(image).unsqueeze(0).to(device)
with torch.no_grad():
image_features = model.encode_image(image)
print("Image features shape:", image_features.shape)
```
## Evaluation results
None of the M-CLIP models have been extensivly evaluated, but testing them on Txt2Img retrieval on the humanly translated MS-COCO dataset, we see the following **R@10** results:
| Name | En | De | Es | Fr | Zh | It | Pl | Ko | Ru | Tr | Jp |
| ----------------------------------|:-----: |:-----: |:-----: |:-----: | :-----: |:-----: |:-----: |:-----: |:-----: |:-----: |:-----: |
| [OpenAI CLIP Vit-B/32](https://github.com/openai/CLIP)| 90.3 | - | - | - | - | - | - | - | - | - | - |
| [OpenAI CLIP Vit-L/14](https://github.com/openai/CLIP)| 91.8 | - | - | - | - | - | - | - | - | - | - |
| [OpenCLIP ViT-B-16+-](https://github.com/openai/CLIP)| 94.3 | - | - | - | - | - | - | - | - | - | - |
| [LABSE Vit-L/14](https://huggingface.co/M-CLIP/LABSE-Vit-L-14)| 91.6 | 89.6 | 89.5 | 89.9 | 88.9 | 90.1 | 89.8 | 80.8 | 85.5 | 89.8 | 73.9 |
| [XLM-R Large Vit-B/32](https://huggingface.co/M-CLIP/XLM-Roberta-Large-Vit-B-32)| 91.8 | 88.7 | 89.1 | 89.4 | 89.3 | 89.8| 91.4 | 82.1 | 86.1 | 88.8 | 81.0 |
| [XLM-R Vit-L/14](https://huggingface.co/M-CLIP/XLM-Roberta-Large-Vit-L-14)| 92.4 | 90.6 | 91.0 | 90.0 | 89.7 | 91.1 | 91.3 | 85.2 | 85.8 | 90.3 | 81.9 |
| [XLM-R Large Vit-B/16+](https://huggingface.co/M-CLIP/XLM-Roberta-Large-Vit-B-16Plus)| **95.0** | **93.0** | **93.6** | **93.1** | **94.0** | **93.1** | **94.4** | **89.0** | **90.0** | **93.0** | **84.2** |
## Training/Model details
Further details about the model training and data can be found in the [model card](https://github.com/FreddeFrallan/Multilingual-CLIP/blob/main/larger_mclip.md). |
Copycats/koelectra-base-v3-generalized-sentiment-analysis | Copycats | "2024-03-17T16:36:22Z" | 18,642 | 4 | transformers | [
"transformers",
"pytorch",
"safetensors",
"electra",
"text-classification",
"ko",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2022-03-02T23:29:05Z" | ---
license: apache-2.0
language:
- ko
library_name: transformers
pipeline_tag: text-classification
---
# Usage
```python
# import library
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline
# load model
tokenizer = AutoTokenizer.from_pretrained("jaehyeong/koelectra-base-v3-generalized-sentiment-analysis")
model = AutoModelForSequenceClassification.from_pretrained("jaehyeong/koelectra-base-v3-generalized-sentiment-analysis")
sentiment_classifier = TextClassificationPipeline(tokenizer=tokenizer, model=model)
# target reviews
review_list = [
'이쁘고 좋아요~~~씻기도 편하고 아이고 이쁘다고 자기방에 갖다놓고 잘써요~^^',
'아직 입어보진 않았지만 굉장히 가벼워요~~ 다른 리뷰처럼 어깡이 좀 되네요ㅋ 만족합니다. 엄청 빠른발송 감사드려요 :)',
'재구매 한건데 너무너무 가성비인거 같아요!! 다음에 또 생각나면 3개째 또 살듯..ㅎㅎ',
'가습량이 너무 적어요. 방이 작지 않다면 무조건 큰걸로구매하세요. 물량도 조금밖에 안들어가서 쓰기도 불편함',
'한번입었는데 옆에 봉제선 다 풀리고 실밥도 계속 나옵니다. 마감 처리 너무 엉망 아닌가요?',
'따뜻하고 좋긴한데 배송이 느려요',
'맛은 있는데 가격이 있는 편이에요'
]
# predict
for idx, review in enumerate(review_list):
pred = sentiment_classifier(review)
print(f'{review}\n>> {pred[0]}')
```
```
이쁘고 좋아요~~~씻기도 편하고 아이고 이쁘다고 자기방에 갖다놓고 잘써요~^^
>> {'label': '1', 'score': 0.9945501685142517}
아직 입어보진 않았지만 굉장히 가벼워요~~ 다른 리뷰처럼 어깡이 좀 되네요ㅋ 만족합니다. 엄청 빠른발송 감사드려요 :)
>> {'label': '1', 'score': 0.995430588722229}
재구매 한건데 너무너무 가성비인거 같아요!! 다음에 또 생각나면 3개째 또 살듯..ㅎㅎ
>> {'label': '1', 'score': 0.9959582686424255}
가습량이 너무 적어요. 방이 작지 않다면 무조건 큰걸로구매하세요. 물량도 조금밖에 안들어가서 쓰기도 불편함
>> {'label': '0', 'score': 0.9984619617462158}
한번입었는데 옆에 봉제선 다 풀리고 실밥도 계속 나옵니다. 마감 처리 너무 엉망 아닌가요?
>> {'label': '0', 'score': 0.9991756677627563}
따뜻하고 좋긴한데 배송이 느려요
>> {'label': '1', 'score': 0.6473883390426636}
맛은 있는데 가격이 있는 편이에요
>> {'label': '1', 'score': 0.5128092169761658}
```
- label 0 : negative review
- label 1 : positive review |
deepset/gbert-large | deepset | "2023-05-05T07:00:08Z" | 18,641 | 46 | transformers | [
"transformers",
"pytorch",
"tf",
"safetensors",
"fill-mask",
"de",
"dataset:wikipedia",
"dataset:OPUS",
"dataset:OpenLegalData",
"dataset:oscar",
"arxiv:2010.10906",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | "2022-03-02T23:29:05Z" | ---
language: de
license: mit
datasets:
- wikipedia
- OPUS
- OpenLegalData
- oscar
---
# German BERT large
Released, Oct 2020, this is a German BERT language model trained collaboratively by the makers of the original German BERT (aka "bert-base-german-cased") and the dbmdz BERT (aka bert-base-german-dbmdz-cased). In our [paper](https://arxiv.org/pdf/2010.10906.pdf), we outline the steps taken to train our model and show that it outperforms its predecessors.
## Overview
**Paper:** [here](https://arxiv.org/pdf/2010.10906.pdf)
**Architecture:** BERT large
**Language:** German
## Performance
```
GermEval18 Coarse: 80.08
GermEval18 Fine: 52.48
GermEval14: 88.16
```
See also:
deepset/gbert-base
deepset/gbert-large
deepset/gelectra-base
deepset/gelectra-large
deepset/gelectra-base-generator
deepset/gelectra-large-generator
## Authors
**Branden Chan:** [email protected]
**Stefan Schweter:** [email protected]
**Timo Möller:** [email protected]
## About us
<div class="grid lg:grid-cols-2 gap-x-4 gap-y-3">
<div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center">
<img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/deepset-logo-colored.png" class="w-40"/>
</div>
<div class="w-full h-40 object-cover mb-2 rounded-lg flex items-center justify-center">
<img alt="" src="https://raw.githubusercontent.com/deepset-ai/.github/main/haystack-logo-colored.png" class="w-40"/>
</div>
</div>
[deepset](http://deepset.ai/) is the company behind the open-source NLP framework [Haystack](https://haystack.deepset.ai/) which is designed to help you build production ready NLP systems that use: Question answering, summarization, ranking etc.
Some of our other work:
- [Distilled roberta-base-squad2 (aka "tinyroberta-squad2")]([https://huggingface.co/deepset/tinyroberta-squad2)
- [German BERT (aka "bert-base-german-cased")](https://deepset.ai/german-bert)
- [GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")](https://deepset.ai/germanquad)
## Get in touch and join the Haystack community
<p>For more info on Haystack, visit our <strong><a href="https://github.com/deepset-ai/haystack">GitHub</a></strong> repo and <strong><a href="https://docs.haystack.deepset.ai">Documentation</a></strong>.
We also have a <strong><a class="h-7" href="https://haystack.deepset.ai/community">Discord community open to everyone!</a></strong></p>
[Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
By the way: [we're hiring!](http://www.deepset.ai/jobs) |
vinvino02/glpn-nyu | vinvino02 | "2024-01-22T21:21:37Z" | 18,609 | 19 | transformers | [
"transformers",
"pytorch",
"safetensors",
"glpn",
"depth-estimation",
"vision",
"arxiv:2201.07436",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | depth-estimation | "2022-03-02T23:29:05Z" | ---
license: apache-2.0
tags:
- vision
- depth-estimation
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
---
# GLPN fine-tuned on NYUv2
Global-Local Path Networks (GLPN) model trained on NYUv2 for monocular depth estimation. It was introduced in the paper [Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth](https://arxiv.org/abs/2201.07436) by Kim et al. and first released in [this repository](https://github.com/vinvino02/GLPDepth).
Disclaimer: The team releasing GLPN did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
GLPN uses SegFormer as backbone and adds a lightweight head on top for depth estimation.
## Intended uses & limitations
You can use the raw model for monocular depth estimation. See the [model hub](https://huggingface.co/models?search=glpn) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model:
```python
from transformers import GLPNImageProcessor, GLPNForDepthEstimation
import torch
import numpy as np
from PIL import Image
import requests
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
processor = GLPNImageProcessor.from_pretrained("vinvino02/glpn-nyu")
model = GLPNForDepthEstimation.from_pretrained("vinvino02/glpn-nyu")
# prepare image for the model
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
predicted_depth = outputs.predicted_depth
# interpolate to original size
prediction = torch.nn.functional.interpolate(
predicted_depth.unsqueeze(1),
size=image.size[::-1],
mode="bicubic",
align_corners=False,
)
# visualize the prediction
output = prediction.squeeze().cpu().numpy()
formatted = (output * 255 / np.max(output)).astype("uint8")
depth = Image.fromarray(formatted)
```
For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/glpn).
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-2201-07436,
author = {Doyeon Kim and
Woonghyun Ga and
Pyunghwan Ahn and
Donggyu Joo and
Sehwan Chun and
Junmo Kim},
title = {Global-Local Path Networks for Monocular Depth Estimation with Vertical
CutDepth},
journal = {CoRR},
volume = {abs/2201.07436},
year = {2022},
url = {https://arxiv.org/abs/2201.07436},
eprinttype = {arXiv},
eprint = {2201.07436},
timestamp = {Fri, 21 Jan 2022 13:57:15 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2201-07436.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
``` |
microsoft/mpnet-base | microsoft | "2024-02-29T09:53:52Z" | 18,578 | 38 | transformers | [
"transformers",
"pytorch",
"tf",
"safetensors",
"mpnet",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | "2022-03-02T23:29:05Z" | Entry not found |
BM-K/KoSimCSE-roberta-multitask | BM-K | "2023-03-24T00:48:07Z" | 18,561 | 22 | transformers | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"feature-extraction",
"korean",
"ko",
"endpoints_compatible",
"text-embeddings-inference",
"region:us"
] | feature-extraction | "2022-06-01T15:02:22Z" | ---
language: ko
tags:
- korean
---
https://github.com/BM-K/Sentence-Embedding-is-all-you-need
# Korean-Sentence-Embedding
🍭 Korean sentence embedding repository. You can download the pre-trained models and inference right away, also it provides environments where individuals can train models.
## Quick tour
```python
import torch
from transformers import AutoModel, AutoTokenizer
def cal_score(a, b):
if len(a.shape) == 1: a = a.unsqueeze(0)
if len(b.shape) == 1: b = b.unsqueeze(0)
a_norm = a / a.norm(dim=1)[:, None]
b_norm = b / b.norm(dim=1)[:, None]
return torch.mm(a_norm, b_norm.transpose(0, 1)) * 100
model = AutoModel.from_pretrained('BM-K/KoSimCSE-roberta-multitask')
AutoTokenizer.from_pretrained('BM-K/KoSimCSE-roberta-multitask')
sentences = ['치타가 들판을 가로 질러 먹이를 쫓는다.',
'치타 한 마리가 먹이 뒤에서 달리고 있다.',
'원숭이 한 마리가 드럼을 연주한다.']
inputs = tokenizer(sentences, padding=True, truncation=True, return_tensors="pt")
embeddings, _ = model(**inputs, return_dict=False)
score01 = cal_score(embeddings[0][0], embeddings[1][0])
score02 = cal_score(embeddings[0][0], embeddings[2][0])
```
## Performance
- Semantic Textual Similarity test set results <br>
| Model | AVG | Cosine Pearson | Cosine Spearman | Euclidean Pearson | Euclidean Spearman | Manhattan Pearson | Manhattan Spearman | Dot Pearson | Dot Spearman |
|------------------------|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|
| KoSBERT<sup>†</sup><sub>SKT</sub> | 77.40 | 78.81 | 78.47 | 77.68 | 77.78 | 77.71 | 77.83 | 75.75 | 75.22 |
| KoSBERT | 80.39 | 82.13 | 82.25 | 80.67 | 80.75 | 80.69 | 80.78 | 77.96 | 77.90 |
| KoSRoBERTa | 81.64 | 81.20 | 82.20 | 81.79 | 82.34 | 81.59 | 82.20 | 80.62 | 81.25 |
| | | | | | | | | |
| KoSentenceBART | 77.14 | 79.71 | 78.74 | 78.42 | 78.02 | 78.40 | 78.00 | 74.24 | 72.15 |
| KoSentenceT5 | 77.83 | 80.87 | 79.74 | 80.24 | 79.36 | 80.19 | 79.27 | 72.81 | 70.17 |
| | | | | | | | | |
| KoSimCSE-BERT<sup>†</sup><sub>SKT</sub> | 81.32 | 82.12 | 82.56 | 81.84 | 81.63 | 81.99 | 81.74 | 79.55 | 79.19 |
| KoSimCSE-BERT | 83.37 | 83.22 | 83.58 | 83.24 | 83.60 | 83.15 | 83.54 | 83.13 | 83.49 |
| KoSimCSE-RoBERTa | 83.65 | 83.60 | 83.77 | 83.54 | 83.76 | 83.55 | 83.77 | 83.55 | 83.64 |
| | | | | | | | | | |
| KoSimCSE-BERT-multitask | 85.71 | 85.29 | 86.02 | 85.63 | 86.01 | 85.57 | 85.97 | 85.26 | 85.93 |
| KoSimCSE-RoBERTa-multitask | 85.77 | 85.08 | 86.12 | 85.84 | 86.12 | 85.83 | 86.12 | 85.03 | 85.99 | |
mradermacher/xTower13B-GGUF | mradermacher | "2024-06-27T21:05:55Z" | 18,536 | 0 | transformers | [
"transformers",
"gguf",
"en",
"de",
"zh",
"ru",
"he",
"fr",
"pt",
"nl",
"ko",
"it",
"es",
"dataset:Unbabel/TowerBlocks-v0.1",
"base_model:sardinelab/xTower13B",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-27T20:18:35Z" | ---
base_model: sardinelab/xTower13B
datasets:
- Unbabel/TowerBlocks-v0.1
language:
- en
- de
- zh
- ru
- he
- fr
- pt
- nl
- ko
- it
- es
library_name: transformers
license: cc-by-nc-4.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/sardinelab/xTower13B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/xTower13B-GGUF/resolve/main/xTower13B.Q2_K.gguf) | Q2_K | 5.0 | |
| [GGUF](https://huggingface.co/mradermacher/xTower13B-GGUF/resolve/main/xTower13B.IQ3_XS.gguf) | IQ3_XS | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/xTower13B-GGUF/resolve/main/xTower13B.IQ3_S.gguf) | IQ3_S | 5.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/xTower13B-GGUF/resolve/main/xTower13B.Q3_K_S.gguf) | Q3_K_S | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/xTower13B-GGUF/resolve/main/xTower13B.IQ3_M.gguf) | IQ3_M | 6.1 | |
| [GGUF](https://huggingface.co/mradermacher/xTower13B-GGUF/resolve/main/xTower13B.Q3_K_M.gguf) | Q3_K_M | 6.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/xTower13B-GGUF/resolve/main/xTower13B.Q3_K_L.gguf) | Q3_K_L | 7.0 | |
| [GGUF](https://huggingface.co/mradermacher/xTower13B-GGUF/resolve/main/xTower13B.IQ4_XS.gguf) | IQ4_XS | 7.1 | |
| [GGUF](https://huggingface.co/mradermacher/xTower13B-GGUF/resolve/main/xTower13B.Q4_K_S.gguf) | Q4_K_S | 7.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/xTower13B-GGUF/resolve/main/xTower13B.Q4_K_M.gguf) | Q4_K_M | 8.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/xTower13B-GGUF/resolve/main/xTower13B.Q5_K_S.gguf) | Q5_K_S | 9.1 | |
| [GGUF](https://huggingface.co/mradermacher/xTower13B-GGUF/resolve/main/xTower13B.Q5_K_M.gguf) | Q5_K_M | 9.3 | |
| [GGUF](https://huggingface.co/mradermacher/xTower13B-GGUF/resolve/main/xTower13B.Q6_K.gguf) | Q6_K | 10.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/xTower13B-GGUF/resolve/main/xTower13B.Q8_0.gguf) | Q8_0 | 13.9 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
THUDM/cogvlm-chat-hf | THUDM | "2023-12-19T10:22:07Z" | 18,535 | 179 | transformers | [
"transformers",
"safetensors",
"text-generation",
"custom_code",
"en",
"arxiv:2311.03079",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] | text-generation | "2023-11-16T14:33:37Z" | ---
license: apache-2.0
language:
- en
---
# CogVLM
**CogVLM** 是一个强大的开源视觉语言模型（VLM）。CogVLM-17B 拥有 100 亿视觉参数和 70 亿语言参数，在 10 个经典跨模态基准测试上取得了 SOTA 性能，包括 NoCaps、Flicker30k captioning、RefCOCO、RefCOCO+、RefCOCOg、Visual7W、GQA、ScienceQA、VizWiz VQA 和 TDIUC，而在 VQAv2、OKVQA、TextVQA、COCO captioning 等方面则排名第二，超越或与 PaLI-X 55B 持平。您可以通过线上 [demo](http://36.103.203.44:7861/) 体验 CogVLM 多模态对话。
**CogVLM** is a powerful **open-source visual language model** (**VLM**). CogVLM-17B has 10 billion vision parameters and 7 billion language parameters. CogVLM-17B achieves state-of-the-art performance on 10 classic cross-modal benchmarks, including NoCaps, Flicker30k captioning, RefCOCO, RefCOCO+, RefCOCOg, Visual7W, GQA, ScienceQA, VizWiz VQA and TDIUC, and rank the 2nd on VQAv2, OKVQA, TextVQA, COCO captioning, etc., **surpassing or matching PaLI-X 55B**. CogVLM can also [chat with you](http://36.103.203.44:7861/) about images.
<div align="center">
<img src="https://github.com/THUDM/CogVLM/raw/main/assets/metrics-min.png" alt="img" style="zoom: 50%;" />
</div>
以上权重对学术研究完全开放，在填写[问卷](https://open.bigmodel.cn/mla/form)进行登记后亦允许免费商业使用。
# 快速开始（Qiuckstart）
硬件需求（hardware requirement）
需要近 40GB GPU 显存用于模型推理。如果没有一整块GPU显存超过40GB，则需要使用accelerate的将模型切分到多个有较小显存的GPU设备上。
40GB VRAM for inference. If there is no single GPU with more than 40GB of VRAM, you will need to use the "accelerate" library to dispatch the model into multiple GPUs with smaller VRAM.
安装依赖（dependencies）
```base
pip install torch==2.1.0 transformers==4.35.0 accelerate==0.24.1 sentencepiece==0.1.99 einops==0.7.0 xformers==0.0.22.post7 triton==2.1.0
```
代码示例（example）
```python
import torch
import requests
from PIL import Image
from transformers import AutoModelForCausalLM, LlamaTokenizer
tokenizer = LlamaTokenizer.from_pretrained('lmsys/vicuna-7b-v1.5')
model = AutoModelForCausalLM.from_pretrained(
'THUDM/cogvlm-chat-hf',
torch_dtype=torch.bfloat16,
low_cpu_mem_usage=True,
trust_remote_code=True
).to('cuda').eval()
# chat example
query = 'Describe this image'
image = Image.open(requests.get('https://github.com/THUDM/CogVLM/blob/main/examples/1.png?raw=true', stream=True).raw).convert('RGB')
inputs = model.build_conversation_input_ids(tokenizer, query=query, history=[], images=[image]) # chat mode
inputs = {
'input_ids': inputs['input_ids'].unsqueeze(0).to('cuda'),
'token_type_ids': inputs['token_type_ids'].unsqueeze(0).to('cuda'),
'attention_mask': inputs['attention_mask'].unsqueeze(0).to('cuda'),
'images': [[inputs['images'][0].to('cuda').to(torch.bfloat16)]],
}
gen_kwargs = {"max_length": 2048, "do_sample": False}
with torch.no_grad():
outputs = model.generate(**inputs, **gen_kwargs)
outputs = outputs[:, inputs['input_ids'].shape[1]:]
print(tokenizer.decode(outputs[0]))
# This image captures a moment from a basketball game. Two players are prominently featured: one wearing a yellow jersey with the number
# 24 and the word 'Lakers' written on it, and the other wearing a navy blue jersey with the word 'Washington' and the number 34. The player
# in yellow is holding a basketball and appears to be dribbling it, while the player in navy blue is reaching out with his arm, possibly
# trying to block or defend. The background shows a filled stadium with spectators, indicating that this is a professional game.</s>
# vqa example
query = 'How many houses are there in this cartoon?'
image = Image.open(requests.get('https://github.com/THUDM/CogVLM/blob/main/examples/3.jpg?raw=true', stream=True).raw).convert('RGB')
inputs = model.build_conversation_input_ids(tokenizer, query=query, history=[], images=[image], template_version='vqa') # vqa mode
inputs = {
'input_ids': inputs['input_ids'].unsqueeze(0).to('cuda'),
'token_type_ids': inputs['token_type_ids'].unsqueeze(0).to('cuda'),
'attention_mask': inputs['attention_mask'].unsqueeze(0).to('cuda'),
'images': [[inputs['images'][0].to('cuda').to(torch.bfloat16)]],
}
gen_kwargs = {"max_length": 2048, "do_sample": False}
with torch.no_grad():
outputs = model.generate(**inputs, **gen_kwargs)
outputs = outputs[:, inputs['input_ids'].shape[1]:]
print(tokenizer.decode(outputs[0]))
# 4</s>
```
当单卡显存不足时，可以将模型切分到多个小显存GPU上。以下是个当你有两张24GB的GPU，16GBCPU内存的例子。
你可以将`infer_auto_device_map`的参数改成你的配置。注意这里将GPU显存少写了一点，这是为推理时中间状态预留出一部分显存。
dispatch the model into multiple GPUs with smaller VRAM. This is an example for you have two 24GB GPU and 16GB CPU memory.
you can change the arguments of `infer_auto_device_map` with your own setting.
```python
import torch
import requests
from PIL import Image
from transformers import AutoModelForCausalLM, LlamaTokenizer
from accelerate import init_empty_weights, infer_auto_device_map, load_checkpoint_and_dispatch
tokenizer = LlamaTokenizer.from_pretrained('lmsys/vicuna-7b-v1.5')
with init_empty_weights():
model = AutoModelForCausalLM.from_pretrained(
'THUDM/cogvlm-chat-hf',
torch_dtype=torch.bfloat16,
low_cpu_mem_usage=True,
trust_remote_code=True,
)
device_map = infer_auto_device_map(model, max_memory={0:'20GiB',1:'20GiB','cpu':'16GiB'}, no_split_module_classes=['CogVLMDecoderLayer', 'TransformerLayer'])
model = load_checkpoint_and_dispatch(
model,
'local/path/to/hf/version/chat/model', # typical, '~/.cache/huggingface/hub/models--THUDM--cogvlm-chat-hf/snapshots/balabala'
device_map=device_map,
)
model = model.eval()
# check device for weights if u want to
for n, p in model.named_parameters():
print(f"{n}: {p.device}")
# chat example
query = 'Describe this image'
image = Image.open(requests.get('https://github.com/THUDM/CogVLM/blob/main/examples/1.png?raw=true', stream=True).raw).convert('RGB')
inputs = model.build_conversation_input_ids(tokenizer, query=query, history=[], images=[image]) # chat mode
inputs = {
'input_ids': inputs['input_ids'].unsqueeze(0).to('cuda'),
'token_type_ids': inputs['token_type_ids'].unsqueeze(0).to('cuda'),
'attention_mask': inputs['attention_mask'].unsqueeze(0).to('cuda'),
'images': [[inputs['images'][0].to('cuda').to(torch.bfloat16)]],
}
gen_kwargs = {"max_length": 2048, "do_sample": False}
with torch.no_grad():
outputs = model.generate(**inputs, **gen_kwargs)
outputs = outputs[:, inputs['input_ids'].shape[1]:]
print(tokenizer.decode(outputs[0]))
```
# 方法（Method）
CogVLM 模型包括四个基本组件：视觉变换器（ViT）编码器、MLP适配器、预训练的大型语言模型（GPT）和一个**视觉专家模块**。更多细节请参见[Paper](https://github.com/THUDM/CogVLM/blob/main/assets/cogvlm-paper.pdf)。
CogVLM model comprises four fundamental components: a vision transformer (ViT) encoder, an MLP adapter, a pretrained large language model (GPT), and a **visual expert module**. See [Paper](https://github.com/THUDM/CogVLM/blob/main/assets/cogvlm-paper.pdf) for more details.
<div align="center">
<img src="https://github.com/THUDM/CogVLM/raw/main/assets/method-min.png" style="zoom:50%;" />
</div>
# 许可（License）
此存储库中的代码是根据 [Apache-2.0 许可](https://github.com/THUDM/CogVLM/raw/main/LICENSE) 开放源码，而使用 CogVLM 模型权重必须遵循 [模型许可](https://github.com/THUDM/CogVLM/raw/main/MODEL_LICENSE)。
The code in this repository is open source under the [Apache-2.0 license](https://github.com/THUDM/CogVLM/raw/main/LICENSE), while the use of the CogVLM model weights must comply with the [Model License](https://github.com/THUDM/CogVLM/raw/main/MODEL_LICENSE).
# 引用（Citation）
If you find our work helpful, please consider citing the following papers
```
@article{wang2023cogvlm,
title={CogVLM: Visual Expert for Pretrained Language Models},
author={Weihan Wang and Qingsong Lv and Wenmeng Yu and Wenyi Hong and Ji Qi and Yan Wang and Junhui Ji and Zhuoyi Yang and Lei Zhao and Xixuan Song and Jiazheng Xu and Bin Xu and Juanzi Li and Yuxiao Dong and Ming Ding and Jie Tang},
year={2023},
eprint={2311.03079},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
``` |
FL33TW00D-HF/phi3 | FL33TW00D-HF | "2024-04-29T19:49:09Z" | 18,532 | 1 | null | [
"gguf",
"license:mit",
"region:us"
] | null | "2024-04-24T09:13:59Z" | ---
license: mit
---
|
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