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alcalazans/my_awesome_qa_model | alcalazans | 2024-07-02T23:09:37Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"question-answering",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2024-07-02T20:41:40Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: my_awesome_qa_model
results: []
---
<!-- 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. -->
# my_awesome_qa_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6522
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 250 | 2.3045 |
| 2.7573 | 2.0 | 500 | 1.6984 |
| 2.7573 | 3.0 | 750 | 1.6522 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
CassioBN/BERTimbau-base_LeNER-Br | CassioBN | 2024-07-02T21:29:44Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:lener_br",
"base_model:neuralmind/bert-base-portuguese-cased",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2024-07-02T20:42:23Z | ---
license: mit
base_model: neuralmind/bert-base-portuguese-cased
tags:
- generated_from_trainer
datasets:
- lener_br
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: BERTimbau-base_LeNER-Br
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: lener_br
type: lener_br
config: lener_br
split: validation
args: lener_br
metrics:
- name: Precision
type: precision
value: 0.8317805383022774
- name: Recall
type: recall
value: 0.8839383938393839
- name: F1
type: f1
value: 0.8570666666666666
- name: Accuracy
type: accuracy
value: 0.9754369390647142
---
<!-- 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. -->
# BERTimbau-base_LeNER-Br
This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the lener_br dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Precision: 0.8318
- Recall: 0.8839
- F1: 0.8571
- Accuracy: 0.9754
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2037 | 1.0 | 979 | nan | 0.7910 | 0.8762 | 0.8314 | 0.9721 |
| 0.0308 | 2.0 | 1958 | nan | 0.7747 | 0.8663 | 0.8180 | 0.9698 |
| 0.02 | 3.0 | 2937 | nan | 0.8316 | 0.8911 | 0.8603 | 0.9801 |
| 0.0133 | 4.0 | 3916 | nan | 0.8038 | 0.8812 | 0.8407 | 0.9728 |
| 0.0111 | 5.0 | 4895 | nan | 0.8253 | 0.8707 | 0.8474 | 0.9753 |
| 0.0078 | 6.0 | 5874 | nan | 0.8235 | 0.8779 | 0.8498 | 0.9711 |
| 0.0057 | 7.0 | 6853 | nan | 0.8174 | 0.8768 | 0.8461 | 0.9760 |
| 0.0032 | 8.0 | 7832 | nan | 0.8113 | 0.8845 | 0.8463 | 0.9769 |
| 0.0027 | 9.0 | 8811 | nan | 0.8344 | 0.8867 | 0.8597 | 0.9767 |
| 0.0023 | 10.0 | 9790 | nan | 0.8318 | 0.8839 | 0.8571 | 0.9754 |
### Testing results
metrics={'test_loss': 0.0710107609629631, 'test_precision': 0.8785578747628083, 'test_recall': 0.9138157894736842, 'test_f1': 0.8958400515962593, 'test_accuracy': 0.9884423662270061, 'test_runtime': 12.4395, 'test_samples_per_second': 111.741, 'test_steps_per_second': 13.988})
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
TableCheck/mt0-small-query-extraction-v3 | TableCheck | 2024-07-02T20:45:46Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"mt5",
"text2text-generation",
"generated_from_trainer",
"base_model:bigscience/mt0-small",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-07-02T20:44:34Z | ---
license: apache-2.0
base_model: bigscience/mt0-small
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt0-small-query-extraction-3
results: []
---
<!-- 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. -->
# mt0-small-query-extraction-3
This model is a fine-tuned version of [bigscience/mt0-small](https://huggingface.co/bigscience/mt0-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Rouge1: 67.5101
- Rouge2: 63.6423
- Rougel: 67.5112
- Rougelsum: 67.5101
- Gen Len: 19.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 18
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 0.0002 | 1.0 | 26667 | 0.0000 | 67.5101 | 63.6423 | 67.5112 | 67.5101 | 19.0 |
| 0.0001 | 2.0 | 53334 | 0.0000 | 67.5101 | 63.6423 | 67.5112 | 67.5101 | 19.0 |
| 0.0 | 3.0 | 80001 | 0.0000 | 67.5101 | 63.6423 | 67.5112 | 67.5101 | 19.0 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
ravazz/ILN | ravazz | 2024-07-02T20:45:09Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T20:45:09Z | Entry not found |
sirrocka/mistralai | sirrocka | 2024-07-02T20:45:54Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T20:45:54Z | Entry not found |
MahmoudAli2024/MyAiBot | MahmoudAli2024 | 2024-07-02T20:45:54Z | 0 | 0 | null | [
"license:bsd",
"region:us"
] | null | 2024-07-02T20:45:54Z | ---
license: bsd
---
|
jamesohe/Llama3-CASAuditBase-8B-2st-train-eval-adapter | jamesohe | 2024-07-02T20:46:58Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-07-02T20:46:22Z | Invalid username or password. |
neuralmagic/Llama-2-7b-chat-quantized.w8a16 | neuralmagic | 2024-07-02T21:10:40Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"8-bit",
"gptq",
"region:us"
] | text-generation | 2024-07-02T20:47:40Z | ---
language:
- en
pipeline_tag: text-generation
---
# Llama-2-7b-chat-quantized.w8a16
## Model Overview
- **Model Architecture:** Llama-2
- **Input:** Text
- **Output:** Text
- **Model Optimizations:**
- **Quantized:** INT8 weights
- **Release Date:** 7/2/2024
- **Version:** 1.0
- **Model Developers:** Neural Magic
Quantized version of [Llama-2-7b-chat](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf).
It achieves an average score of 53.37% on the OpenLLM benchmark (version 1), whereas the unquantized model achieves 53.41%.
## Model Optimizations
This model was obtained by quantizing the weights of [Llama-2-7b-chat](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) to INT8 data type.
Only the weights of the linear operators within transformers blocks are quantized. Symmetric per-channel quantization is applied, in which a linear scaling per output dimension maps the INT8 and floating point representations of the quantized weights.
[AutoGPTQ](https://github.com/AutoGPTQ/AutoGPTQ) is used for quantization.
This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%.
## Evaluation
The model was evaluated with the [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) using the [vLLM](https://docs.vllm.ai/en/stable/) engine.
## Accuracy
### Open LLM Leaderboard evaluation scores
| | [Llama-2-7b-chat](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) | Llama-2-7b-chat-quantized.w8a16<br>(this model) |
| :------------------: | :----------------------: | :------------------------------------------------: |
| arc-c<br>25-shot | 53.41% | 53.37% |
| hellaswag<br>10-shot | 78.65% | 78.53% |
| mmlu<br>5-shot | 47.34% | 47.32% |
| truthfulqa<br>0-shot | 45.58% | 45.61% |
| winogrande<br>5-shot | 72.45% | 72.45% |
| gsm8k<br>5-shot | 23.20% | 22.82% |
| **Average<br>Accuracy** | **53.41%** | **53.37%** |
| **Recovery** | **100%** | **99.93%** | |
Artin2009/mistral | Artin2009 | 2024-07-02T20:48:07Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T20:48:06Z | Entry not found |
crrodrvi/mt5-simplificacion | crrodrvi | 2024-07-02T20:52:45Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"mt5",
"text2text-generation",
"generated_from_trainer",
"base_model:oskrmiguel/mt5-simplification-spanish",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-07-02T20:49:05Z | ---
license: cc-by-nc-sa-4.0
base_model: oskrmiguel/mt5-simplification-spanish
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: mt5-simplificacion
results: []
---
<!-- 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. -->
# mt5-simplificacion
This model is a fine-tuned version of [oskrmiguel/mt5-simplification-spanish](https://huggingface.co/oskrmiguel/mt5-simplification-spanish) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7400
- Bleu: 5.0807
- Gen Len: 18.3814
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| No log | 1.0 | 97 | 2.8305 | 5.4545 | 18.2784 |
| No log | 2.0 | 194 | 2.7680 | 5.2019 | 18.268 |
| No log | 3.0 | 291 | 2.7461 | 5.3626 | 18.3814 |
| No log | 4.0 | 388 | 2.7400 | 5.0807 | 18.3814 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
CogitoAI/Cogito_AI_Bot | CogitoAI | 2024-07-02T20:49:11Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | 2024-07-02T20:49:11Z | ---
license: openrail
---
|
NikolayKozloff/RoLlama3-8b-Instruct-Q4_K_L-GGUF | NikolayKozloff | 2024-07-02T21:10:32Z | 0 | 1 | null | [
"gguf",
"text-generation-inference",
"ro",
"region:us"
] | null | 2024-07-02T20:49:53Z | ---
language:
- ro
tags:
- text-generation-inference
---
Best quality quant created using this instruction: https://huggingface.co/bartowski/Phi-3-medium-128k-instruct-GGUF/discussions/3#6679c0ce761779cf45d2321b |
MrGonk/Gonk_4 | MrGonk | 2024-07-02T20:54:30Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-07-02T20:52:16Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset 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. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
RefalMachine/mistral_darulm_20_05_24_part1-2_32000_bpe_part1-2_lr1e4_bs256 | RefalMachine | 2024-07-02T20:52:29Z | 0 | 0 | null | [
"tensorboard",
"region:us"
] | null | 2024-07-02T20:52:27Z | Entry not found |
cattoroboto/gemma-2-9b-CharacterCodex-qlora | cattoroboto | 2024-07-02T21:16:28Z | 0 | 0 | peft | [
"peft",
"safetensors",
"gemma2",
"generated_from_trainer",
"dataset:NousResearch/CharacterCodex",
"base_model:google/gemma-2-9b",
"license:gemma",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2024-07-02T20:53:24Z | ---
base_model: google/gemma-2-9b
library_name: peft
license: gemma
tags:
- generated_from_trainer
model-index:
- name: outputs/out
results: []
datasets:
- NousResearch/CharacterCodex
---
>This is a test qlora! 🙀
<!-- 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. -->
[<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)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
base_model: google/gemma-2-9b
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
load_in_8bit: false
load_in_4bit: true
strict: false
# huggingface repo
chat_template: gemma
datasets:
- path: NousResearch/CharacterCodex
type: completion
# chat_template: gemma
# drop_system_message: true
field: description
val_set_size: 0.0
output_dir: ./outputs/out
adapter: qlora
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
sequence_len: 2048
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 4
micro_batch_size: 1
num_epochs: 2
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: true
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_ratio: 0.1
evals_per_epoch:
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
```
</details><br>
# outputs/out
This model is a fine-tuned version of [google/gemma-2-9b](https://huggingface.co/google/gemma-2-9b) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 23
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.11.1
- Transformers 4.42.3
- Pytorch 2.1.2+cu118
- Datasets 2.19.1
- Tokenizers 0.19.1 |
jacoboggleon-bbva/lora_model | jacoboggleon-bbva | 2024-07-02T20:54:23Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:unsloth/phi-3-mini-4k-instruct-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-07-02T20:54:15Z | ---
base_model: unsloth/phi-3-mini-4k-instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
---
# Uploaded model
- **Developed by:** jacoboggleon-bbva
- **License:** apache-2.0
- **Finetuned from model :** unsloth/phi-3-mini-4k-instruct-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
GaumlessGraham/butterflystest | GaumlessGraham | 2024-07-02T20:55:22Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T20:54:37Z | Entry not found |
wootzie/torales | wootzie | 2024-07-02T20:55:07Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T20:54:56Z | Entry not found |
yemen2016/memobert3_NC_last | yemen2016 | 2024-07-02T21:10:55Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:MiMe-MeMo/MeMo-BERT-03",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-07-02T20:57:38Z | ---
base_model: MiMe-MeMo/MeMo-BERT-03
tags:
- generated_from_trainer
model-index:
- name: memobert3_NC_last
results: []
---
<!-- 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. -->
# memobert3_NC_last
This model is a fine-tuned version of [MiMe-MeMo/MeMo-BERT-03](https://huggingface.co/MiMe-MeMo/MeMo-BERT-03) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6408
- F1-score: 0.7238
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1-score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 76 | 0.5465 | 0.6877 |
| No log | 2.0 | 152 | 0.5191 | 0.6877 |
| No log | 3.0 | 228 | 0.5181 | 0.6877 |
| No log | 4.0 | 304 | 0.5362 | 0.6877 |
| No log | 5.0 | 380 | 0.5724 | 0.6877 |
| No log | 6.0 | 456 | 0.5894 | 0.7041 |
| 0.4716 | 7.0 | 532 | 0.6408 | 0.7238 |
| 0.4716 | 8.0 | 608 | 0.6766 | 0.7041 |
| 0.4716 | 9.0 | 684 | 0.6812 | 0.7178 |
| 0.4716 | 10.0 | 760 | 0.6817 | 0.7060 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
Kutsuya/Phi-3-mini-4k-instruct-unfiltered | Kutsuya | 2024-07-02T21:00:11Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T21:00:11Z | Entry not found |
MrGonk/Gonk_5 | MrGonk | 2024-07-02T21:07:02Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-07-02T21:04:38Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset 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. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Ferdi/CodeLlama-7b-Instruct-hf-FaVe-rank64-1epochs-v3 | Ferdi | 2024-07-02T21:08:58Z | 0 | 0 | peft | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/CodeLlama-7b-Instruct-hf",
"license:llama2",
"region:us"
] | null | 2024-07-02T21:08:55Z | ---
license: llama2
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: meta-llama/CodeLlama-7b-Instruct-hf
model-index:
- name: CodeLlama-7b-Instruct-hf-FaVe-rank64-1epochs-v3
results: []
---
<!-- 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. -->
# CodeLlama-7b-Instruct-hf-FaVe-rank64-1epochs-v3
This model is a fine-tuned version of [meta-llama/CodeLlama-7b-Instruct-hf](https://huggingface.co/meta-llama/CodeLlama-7b-Instruct-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6311
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.2685 | 10 | 0.9866 |
| 1.2568 | 0.5369 | 20 | 0.7395 |
| 1.2568 | 0.8054 | 30 | 0.6311 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1 |
AndiB93/dqn-SpaceInvadersNoFrameskip-v4 | AndiB93 | 2024-07-02T21:09:46Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2024-07-02T21:09:01Z | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 880.00 +/- 261.03
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga AndiB93 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga AndiB93 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga AndiB93
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
natales2018/savecloude | natales2018 | 2024-07-02T21:09:57Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T21:09:02Z | Entry not found |
larenspear/Yi-1.5-34B-Chat-Q6_K-GGUF | larenspear | 2024-07-02T21:11:25Z | 0 | 0 | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:01-ai/Yi-1.5-34B-Chat",
"license:apache-2.0",
"region:us"
] | null | 2024-07-02T21:09:24Z | ---
base_model: 01-ai/Yi-1.5-34B-Chat
license: apache-2.0
tags:
- llama-cpp
- gguf-my-repo
---
# larenspear/Yi-1.5-34B-Chat-Q6_K-GGUF
This model was converted to GGUF format from [`01-ai/Yi-1.5-34B-Chat`](https://huggingface.co/01-ai/Yi-1.5-34B-Chat) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/01-ai/Yi-1.5-34B-Chat) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo larenspear/Yi-1.5-34B-Chat-Q6_K-GGUF --hf-file yi-1.5-34b-chat-q6_k.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo larenspear/Yi-1.5-34B-Chat-Q6_K-GGUF --hf-file yi-1.5-34b-chat-q6_k.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo larenspear/Yi-1.5-34B-Chat-Q6_K-GGUF --hf-file yi-1.5-34b-chat-q6_k.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo larenspear/Yi-1.5-34B-Chat-Q6_K-GGUF --hf-file yi-1.5-34b-chat-q6_k.gguf -c 2048
```
|
Lyxas/LLaVA-1.6-7B-light-custom-no-aug-t1 | Lyxas | 2024-07-02T21:12:15Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llava_mistral",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-07-02T21:09:47Z | Entry not found |
konverner/lstm_sentiment | konverner | 2024-07-02T21:11:01Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T21:11:01Z | Entry not found |
neuralmagic/Meta-Llama-3-8B-Instruct-quantized.w8a16 | neuralmagic | 2024-07-02T21:30:28Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"8-bit",
"gptq",
"region:us"
] | text-generation | 2024-07-02T21:11:17Z | ---
language:
- en
pipeline_tag: text-generation
---
# Meta-Llama-3-8B-Instruct-quantized.w8a16
## Model Overview
- **Model Architecture:** Meta-Llama-3
- **Input:** Text
- **Output:** Text
- **Model Optimizations:**
- **Quantized:** INT8 weights
- **Release Date:** 7/2/2024
- **Version:** 1.0
- **Model Developers:** Neural Magic
Quantized version of [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct).
It achieves an average score of 68.69% on the OpenLLM benchmark (version 1), whereas the unquantized model achieves 68.54%.
## Model Optimizations
This model was obtained by quantizing the weights of [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) to INT8 data type.
Only the weights of the linear operators within transformers blocks are quantized. Symmetric per-channel quantization is applied, in which a linear scaling per output dimension maps the INT8 and floating point representations of the quantized weights.
[AutoGPTQ](https://github.com/AutoGPTQ/AutoGPTQ) is used for quantization.
This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%.
## Evaluation
The model was evaluated with the [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) using the [vLLM](https://docs.vllm.ai/en/stable/) engine.
## Accuracy
### Open LLM Leaderboard evaluation scores
| | [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) | Meta-Llama-3-8B-Instruct-quantized.w8a16<br>(this model) |
| :------------------: | :----------------------: | :------------------------------------------------: |
| arc-c<br>25-shot | 62.63% | 61.52% |
| hellaswag<br>10-shot | 78.81% | 78.69% |
| mmlu<br>5-shot | 66.54% | 66.55% |
| truthfulqa<br>0-shot | 52.49% | 52.60% |
| winogrande<br>5-shot | 76.48% | 76.01% |
| gsm8k<br>5-shot | 75.21% | 75.89% |
| **Average<br>Accuracy** | **68.69%** | **68.54%** |
| **Recovery** | **100%** | **99.78%** | |
Thatsnazzyartist22/Jevil | Thatsnazzyartist22 | 2024-07-02T21:12:37Z | 0 | 0 | null | [
"license:unknown",
"region:us"
] | null | 2024-07-02T21:12:07Z | ---
license: unknown
---
|
frank2030/llama3_chat_tune_lora_merged | frank2030 | 2024-07-02T21:17:26Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:unsloth/llama-3-8b-instruct-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-07-02T21:13:23Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
base_model: unsloth/llama-3-8b-instruct-bnb-4bit
---
# Uploaded model
- **Developed by:** frank2030
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
msoz7/my_awesome_model | msoz7 | 2024-07-02T21:14:56Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T21:14:56Z | Entry not found |
lashao/miewid-imagenet | lashao | 2024-07-02T21:18:11Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"miewid",
"feature-extraction",
"custom_code",
"arxiv:1910.09700",
"region:us"
] | feature-extraction | 2024-07-02T21:15:08Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset 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. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Contact
[More Information Needed] |
RefalMachine/mistral_darulm_20_05_24_part1-2_32000_bpe_part1-2_lr5e5_bs256 | RefalMachine | 2024-07-02T21:16:27Z | 0 | 0 | null | [
"tensorboard",
"region:us"
] | null | 2024-07-02T21:16:24Z | Entry not found |
yemen2016/nbbert_NC_last | yemen2016 | 2024-07-02T21:34:07Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:NbAiLab/nb-bert-base",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-07-02T21:16:28Z | ---
license: cc-by-4.0
base_model: NbAiLab/nb-bert-base
tags:
- generated_from_trainer
model-index:
- name: nbbert_NC_last
results: []
---
<!-- 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. -->
# nbbert_NC_last
This model is a fine-tuned version of [NbAiLab/nb-bert-base](https://huggingface.co/NbAiLab/nb-bert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8903
- F1-score: 0.7232
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1-score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 76 | 0.5322 | 0.6877 |
| No log | 2.0 | 152 | 0.5191 | 0.6877 |
| No log | 3.0 | 228 | 0.5266 | 0.6877 |
| No log | 4.0 | 304 | 0.6069 | 0.6877 |
| No log | 5.0 | 380 | 0.6897 | 0.7044 |
| No log | 6.0 | 456 | 0.7943 | 0.6825 |
| 0.3919 | 7.0 | 532 | 0.8435 | 0.7197 |
| 0.3919 | 8.0 | 608 | 0.8903 | 0.7232 |
| 0.3919 | 9.0 | 684 | 0.9213 | 0.7060 |
| 0.3919 | 10.0 | 760 | 0.9425 | 0.7060 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
Stoub/q-FrozenLake-v1-4x4-noSlippery | Stoub | 2024-07-02T21:57:41Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2024-07-02T21:16:31Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Stoub/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
CarlosPov/Llama-2-7b-chat-hf-finetune_90_10_SY_gold | CarlosPov | 2024-07-02T21:16:45Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T21:16:45Z | Entry not found |
wzebrowski/gemma2-finetune_fafsa-instruct-9b | wzebrowski | 2024-07-02T21:23:05Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-07-02T21:17:59Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset 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. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
whizzzzkid/whizzzzkid_438_5 | whizzzzkid | 2024-07-02T21:19:00Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-07-02T21:18:36Z | Entry not found |
frank2030/llama3_chat_tune_gguf_q8_0 | frank2030 | 2024-07-02T21:24:29Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/llama-3-8b-instruct-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-07-02T21:18:52Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
base_model: unsloth/llama-3-8b-instruct-bnb-4bit
---
# Uploaded model
- **Developed by:** frank2030
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
twright8/setfit_lobbying_classifier_test | twright8 | 2024-07-02T21:19:33Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T21:19:33Z | Entry not found |
maxseats/SungBeom-whisper-small-ko-set21 | maxseats | 2024-07-02T21:21:15Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"speech-recognition",
"ko",
"dataset:maxseats/aihub-464-preprocessed-680GB-set-21",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-07-02T21:20:53Z |
---
language: ko
tags:
- whisper
- speech-recognition
datasets:
- maxseats/aihub-464-preprocessed-680GB-set-21
metrics:
- cer
---
# Model Name : maxseats/SungBeom-whisper-small-ko-set20
# Description
- 파인튜닝 데이터셋 : maxseats/aihub-464-preprocessed-680GB-set-21
# 설명
- AI hub의 주요 영역별 회의 음성 데이터셋을 학습 중이에요.
- 680GB 중 set_0~20 데이터(210GB)까지 파인튜닝한 모델을 불러와서, set_21 데이터(10GB)를 학습한 모델입니다.
- 링크 : https://huggingface.co/datasets/maxseats/aihub-464-preprocessed-680GB-set-21
|
lashao/miewid-msv2 | lashao | 2024-07-02T21:21:36Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"miewid",
"feature-extraction",
"custom_code",
"arxiv:1910.09700",
"region:us"
] | feature-extraction | 2024-07-02T21:21:01Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset 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. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
shenbinqian/whisper-small-yue | shenbinqian | 2024-07-03T01:31:31Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-07-02T21:21:28Z | Entry not found |
elainechan01/ppo-LunarLander-v2 | elainechan01 | 2024-07-02T21:22:43Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2024-07-02T21:22:17Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 222.70 +/- 52.77
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Zainabsa99/llama-2-7b-cyllama | Zainabsa99 | 2024-07-02T21:29:40Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-07-02T21:22:31Z | Entry not found |
ywangmy/sft-l38i-full-5e-6-256-4096-0.03-10000 | ywangmy | 2024-07-03T00:47:55Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-07-02T21:23:00Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset 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. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
joshbz/ppo-Huggy | joshbz | 2024-07-02T21:23:20Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | reinforcement-learning | 2024-07-02T21:23:14Z | ---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: joshbz/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
RyanJT/gpt2-finetuned-x | RyanJT | 2024-07-02T22:35:25Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-07-02T21:26:25Z | Entry not found |
grantgreenwood/segformer-b0-finetuned-segments-sidewalk-2 | grantgreenwood | 2024-07-02T21:26:56Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T21:26:55Z | Entry not found |
neuralmagic/Meta-Llama-3-70B-Instruct-quantized.w8a16 | neuralmagic | 2024-07-02T21:50:50Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"8-bit",
"gptq",
"region:us"
] | text-generation | 2024-07-02T21:31:37Z | ---
language:
- en
pipeline_tag: text-generation
---
# Meta-Llama-3-70B-Instruct-quantized.w8a16
## Model Overview
- **Model Architecture:** Meta-Llama-3
- **Input:** Text
- **Output:** Text
- **Model Optimizations:**
- **Quantized:** INT8 weights
- **Release Date:** 7/2/2024
- **Version:** 1.0
- **Model Developers:** Neural Magic
Quantized version of [Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct).
It achieves an average score of 79.18% on the OpenLLM benchmark (version 1), whereas the unquantized model achieves 77.90%.
## Model Optimizations
This model was obtained by quantizing the weights of [Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct) to INT8 data type.
Only the weights of the linear operators within transformers blocks are quantized. Symmetric per-channel quantization is applied, in which a linear scaling per output dimension maps the INT8 and floating point representations of the quantized weights.
[AutoGPTQ](https://github.com/AutoGPTQ/AutoGPTQ) is used for quantization.
This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%.
## Evaluation
The model was evaluated with the [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) using the [vLLM](https://docs.vllm.ai/en/stable/) engine.
## Accuracy
### Open LLM Leaderboard evaluation scores
| | [Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct) | Meta-Llama-3-70B-Instruct-quantized.w8a16<br>(this model) |
| :------------------: | :----------------------: | :------------------------------------------------: |
| arc-c<br>25-shot | 72.44% | 71.59% |
| hellaswag<br>10-shot | 85.54% | 85.65% |
| mmlu<br>5-shot | 80.18% | 78.69% |
| truthfulqa<br>0-shot | 62.92% | 61.94% |
| winogrande<br>5-shot | 83.19% | 83.11% |
| gsm8k<br>5-shot | 90.83% | 86.43% |
| **Average<br>Accuracy** | **79.18%** | **77.90%** |
| **Recovery** | **100%** | **98.38%** | |
AmberYifan/base-sft-safe-spin-v | AmberYifan | 2024-07-03T01:21:38Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"mistral",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-07-02T21:31:38Z | ---
license: apache-2.0
base_model: alignment-handbook/zephyr-7b-sft-full
tags:
- generated_from_trainer
model-index:
- name: base-sft-safe-spin-v
results: []
---
<!-- 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. -->
# base-sft-safe-spin-v
This model is a fine-tuned version of [alignment-handbook/zephyr-7b-sft-full](https://huggingface.co/alignment-handbook/zephyr-7b-sft-full) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0738
- Rewards/real: -3.0711
- Rewards/generated: -13.2471
- Rewards/accuracies: 0.9713
- Rewards/margins: 10.1760
- Logps/generated: -228.7879
- Logps/real: -165.3767
- Logits/generated: -2.4198
- Logits/real: -2.4231
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 32
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/real | Rewards/generated | Rewards/accuracies | Rewards/margins | Logps/generated | Logps/real | Logits/generated | Logits/real |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:-----------------:|:------------------:|:---------------:|:---------------:|:----------:|:----------------:|:-----------:|
| 0.3742 | 0.06 | 100 | 0.2244 | -0.3695 | -6.6880 | 0.9658 | 6.3185 | -163.1966 | -138.3603 | -2.7435 | -2.7148 |
| 0.2528 | 0.12 | 200 | 0.1319 | -1.2400 | -17.8536 | 0.9697 | 16.6136 | -274.8525 | -147.0654 | -2.4573 | -2.4671 |
| 0.2066 | 0.17 | 300 | 0.1172 | -1.6714 | -19.7358 | 0.9618 | 18.0643 | -293.6746 | -151.3799 | -2.4257 | -2.3622 |
| 0.2207 | 0.23 | 400 | 0.1094 | -1.9426 | -20.6733 | 0.9729 | 18.7307 | -303.0500 | -154.0918 | -2.4889 | -2.4525 |
| 0.4379 | 0.29 | 500 | 0.1152 | -1.0002 | -8.3421 | 0.9666 | 7.3419 | -179.7377 | -144.6674 | -2.3870 | -2.3441 |
| 0.1517 | 0.35 | 600 | 0.0984 | -1.6577 | -12.9237 | 0.9745 | 11.2660 | -225.5533 | -151.2425 | -2.2691 | -2.2742 |
| 0.1708 | 0.41 | 700 | 0.0866 | -1.9495 | -14.1941 | 0.9745 | 12.2446 | -238.2574 | -154.1605 | -2.2343 | -2.2124 |
| 0.1135 | 0.47 | 800 | 0.0810 | -3.0171 | -16.4497 | 0.9785 | 13.4327 | -260.8139 | -164.8361 | -2.1789 | -2.1987 |
| 0.1364 | 0.52 | 900 | 0.0848 | -2.5549 | -14.8091 | 0.9729 | 12.2542 | -244.4078 | -160.2151 | -2.3295 | -2.3368 |
| 0.1142 | 0.58 | 1000 | 0.0902 | -2.6698 | -10.6438 | 0.9713 | 7.9740 | -202.7553 | -161.3638 | -2.4644 | -2.4787 |
| 0.1332 | 0.64 | 1100 | 0.0771 | -2.7436 | -11.8738 | 0.9785 | 9.1302 | -215.0552 | -162.1016 | -2.4417 | -2.4630 |
| 0.1007 | 0.7 | 1200 | 0.0758 | -3.4115 | -14.1899 | 0.9745 | 10.7784 | -238.2156 | -168.7807 | -2.3948 | -2.4255 |
| 0.1306 | 0.76 | 1300 | 0.0765 | -2.4042 | -11.1062 | 0.9753 | 8.7019 | -207.3786 | -158.7081 | -2.5270 | -2.5375 |
| 0.1084 | 0.81 | 1400 | 0.0760 | -2.7805 | -12.4025 | 0.9745 | 9.6220 | -220.3422 | -162.4709 | -2.4762 | -2.4848 |
| 0.1494 | 0.87 | 1500 | 0.0740 | -3.0055 | -13.0014 | 0.9713 | 9.9959 | -226.3309 | -164.7203 | -2.4656 | -2.4751 |
| 0.1099 | 0.93 | 1600 | 0.0774 | -3.4971 | -13.6736 | 0.9729 | 10.1765 | -233.0532 | -169.6366 | -2.4253 | -2.4320 |
| 0.0906 | 0.99 | 1700 | 0.0738 | -3.0711 | -13.2471 | 0.9713 | 10.1760 | -228.7879 | -165.3767 | -2.4198 | -2.4231 |
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
|
gaelafoxus/ModelsXL | gaelafoxus | 2024-07-02T22:21:14Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T21:32:42Z | Entry not found |
stojchet/test-rename | stojchet | 2024-07-02T21:38:40Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T21:33:34Z | Entry not found |
DarwinAnim8or/GPT-NoSleep-v2 | DarwinAnim8or | 2024-07-02T21:37:23Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-07-02T21:34:19Z | ---
license: mit
---
|
Maxivi/newxlmerge | Maxivi | 2024-07-02T22:12:53Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T21:34:37Z | Entry not found |
Artin2009/models | Artin2009 | 2024-07-02T21:34:38Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T21:34:38Z | Entry not found |
mradermacher/Nethena-20B-i1-GGUF | mradermacher | 2024-07-02T23:13:47Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:NeverSleep/Nethena-20B",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-07-02T21:37:17Z | ---
base_model: NeverSleep/Nethena-20B
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/NeverSleep/Nethena-20B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Nethena-20B-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/Nethena-20B-i1-GGUF/resolve/main/Nethena-20B.i1-IQ2_M.gguf) | i1-IQ2_M | 7.0 | |
| [GGUF](https://huggingface.co/mradermacher/Nethena-20B-i1-GGUF/resolve/main/Nethena-20B.i1-Q2_K.gguf) | i1-Q2_K | 7.5 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Nethena-20B-i1-GGUF/resolve/main/Nethena-20B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 7.7 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Nethena-20B-i1-GGUF/resolve/main/Nethena-20B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 9.8 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Nethena-20B-i1-GGUF/resolve/main/Nethena-20B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 10.8 | |
| [GGUF](https://huggingface.co/mradermacher/Nethena-20B-i1-GGUF/resolve/main/Nethena-20B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 11.5 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Nethena-20B-i1-GGUF/resolve/main/Nethena-20B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 12.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Nethena-20B-i1-GGUF/resolve/main/Nethena-20B.i1-Q6_K.gguf) | i1-Q6_K | 16.5 | 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 -->
|
marcos15202530/HQS | marcos15202530 | 2024-07-02T21:41:11Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T21:41:11Z | Entry not found |
stojchet/test-rename2 | stojchet | 2024-07-02T21:41:13Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T21:41:13Z | Entry not found |
Hamze-Hammami/dqn-SpaceInvadersNoFrameskip-v4 | Hamze-Hammami | 2024-07-02T21:41:53Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2024-07-02T21:41:16Z | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 782.50 +/- 293.42
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Hamze-Hammami -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Hamze-Hammami -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Hamze-Hammami
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 10000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
stojchet/test-rename3 | stojchet | 2024-07-02T21:41:47Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-07-02T21:41:40Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset 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. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
yemen2016/danskbert_NC_last | yemen2016 | 2024-07-02T22:19:00Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-07-02T21:42:33Z | Entry not found |
flammenai/Mahou-1.3-gemma2-9B | flammenai | 2024-07-02T22:00:45Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"dataset:flammenai/FlameMix-DPO-v1",
"dataset:flammenai/MahouMix-v1",
"dataset:flammenai/Grill-Flammen-v1_chatML",
"base_model:google/gemma-2-9b",
"license:gemma",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-07-02T21:43:19Z | ---
library_name: transformers
license: gemma
base_model:
- google/gemma-2-9b
datasets:
- flammenai/FlameMix-DPO-v1
- flammenai/MahouMix-v1
- flammenai/Grill-Flammen-v1_chatML
---
# Mahou-1.3-gemma2-9B
Mahou is designed to provide short messages in a conversational context. It is capable of casual conversation and character roleplay.
### Chat Format
This model has been trained to use ChatML format. Note the additional tokens in [tokenizer_config.json](tokenizer_config.json).
```
<|im_start|>system
{{system}}<|im_end|>
<|im_start|>{{char}}
{{message}}<|im_end|>
<|im_start|>{{user}}
{{message}}<|im_end|>
```
### Roleplay Format
- Speech without quotes.
- Actions in `*asterisks*`
```
*leans against wall cooly* so like, i just casted a super strong spell at magician academy today, not gonna lie, felt badass.
```
### SillyTavern Settings
1. Use ChatML for the Context Template.
2. Enable Instruct Mode.
3. Use the [Mahou preset](https://huggingface.co/datasets/flammenai/Mahou-ST-ChatML-Instruct/raw/main/Mahou.json).
4. *Recommended* Additonal stopping strings: `["\n", "<|", "</"]`
### Method
Finetuned for 3 epochs using an A100 on Google Colab.
[Fine-tune Llama 3 with ORPO](https://huggingface.co/blog/mlabonne/orpo-llama-3) - [Maxime Labonne](https://huggingface.co/mlabonne) |
stojchet/1ab31794e53311631de923265c7b898d | stojchet | 2024-07-02T21:44:53Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-07-02T21:44:46Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset 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. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
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stojchet/35cda68bc3f4dfe1a2c1293f3674518a | stojchet | 2024-07-02T21:45:12Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-07-02T21:45:05Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset 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. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
ramitpahwa13/OpenHermes-2.5-Mistral-7B-Pruned50-GPTQ-NO-Marlin | ramitpahwa13 | 2024-07-02T21:49:37Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"gptq",
"region:us"
] | text-generation | 2024-07-02T21:46:28Z | Entry not found |
NicklasMatzulla/Pixel-v0.1 | NicklasMatzulla | 2024-07-02T22:03:19Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/llama-3-8b-Instruct-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-07-02T21:47:23Z | ---
base_model: unsloth/llama-3-8b-Instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
---
# Uploaded model
- **Developed by:** NicklasMatzulla
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
utrobinmv/t5_translate_en_ru_zh_large_1024_v2 | utrobinmv | 2024-07-02T22:04:10Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"translation",
"ru",
"zh",
"en",
"dataset:ccmatrix",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | translation | 2024-07-02T21:48:28Z | ---
language:
- ru
- zh
- en
tags:
- translation
license: apache-2.0
datasets:
- ccmatrix
metrics:
- sacrebleu
widget:
- example_title: translate zh-ru
text: >
translate to ru: 开发的目的是为用户提供个人同步翻译。
- example_title: translate ru-en
text: >
translate to en: Цель разработки — предоставить пользователям личного синхронного переводчика.
- example_title: translate en-ru
text: >
translate to ru: The purpose of the development is to provide users with a personal synchronized interpreter.
- example_title: translate en-zh
text: >
translate to zh: The purpose of the development is to provide users with a personal synchronized interpreter.
- example_title: translate zh-en
text: >
translate to en: 开发的目的是为用户提供个人同步解释器。
- example_title: translate ru-zh
text: >
translate to zh: Цель разработки — предоставить пользователям личного синхронного переводчика.
---
# T5 English, Russian and Chinese multilingual machine translation
This model represents a conventional T5 transformer in multitasking mode for translation into the required language, precisely configured for machine translation for pairs: ru-zh, zh-ru, en-zh, zh-en, en-ru, ru-en.
The model can perform direct translation between any pair of Russian, Chinese or English languages. For translation into the target language, the target language identifier is specified as a prefix 'translate to <lang>:'. In this case, the source language may not be specified, in addition, the source text may be multilingual.
Example translate Russian to Chinese
```python
from transformers import T5ForConditionalGeneration, T5Tokenizer
device = 'cuda' #or 'cpu' for translate on cpu
model_name = 'utrobinmv/t5_translate_en_ru_zh_large_1024_v2'
model = T5ForConditionalGeneration.from_pretrained(model_name)
model.eval()
model.to(device)
tokenizer = T5Tokenizer.from_pretrained(model_name)
prefix = 'translate to zh: '
src_text = prefix + "Съешь ещё этих мягких французских булок."
# translate Russian to Chinese
input_ids = tokenizer(src_text, return_tensors="pt")
generated_tokens = model.generate(**input_ids.to(device))
result = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
print(result)
# 再吃这些法国的甜蜜的面包。
```
and Example translate Chinese to Russian
```python
from transformers import T5ForConditionalGeneration, T5Tokenizer
device = 'cuda' #or 'cpu' for translate on cpu
model_name = 'utrobinmv/t5_translate_en_ru_zh_large_1024_v2'
model = T5ForConditionalGeneration.from_pretrained(model_name)
model.eval()
model.to(device)
tokenizer = T5Tokenizer.from_pretrained(model_name)
prefix = 'translate to ru: '
src_text = prefix + "再吃这些法国的甜蜜的面包。"
# translate Russian to Chinese
input_ids = tokenizer(src_text, return_tensors="pt")
generated_tokens = model.generate(**input_ids.to(device))
result = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
print(result)
# Съешьте этот сладкий хлеб из Франции.
```
##
## Languages covered
Russian (ru_RU), Chinese (zh_CN), English (en_US)
|
mradermacher/Berry_v2_7B-GGUF | mradermacher | 2024-07-02T22:41:04Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"en",
"base_model:jeiku/Berry_v2_7B",
"endpoints_compatible",
"region:us"
] | null | 2024-07-02T21:48:44Z | ---
base_model: jeiku/Berry_v2_7B
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- merge
- mergekit
- lazymergekit
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/jeiku/Berry_v2_7B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Berry_v2_7B-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/Berry_v2_7B-GGUF/resolve/main/Berry_v2_7B.Q2_K.gguf) | Q2_K | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/Berry_v2_7B-GGUF/resolve/main/Berry_v2_7B.IQ3_XS.gguf) | IQ3_XS | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Berry_v2_7B-GGUF/resolve/main/Berry_v2_7B.Q3_K_S.gguf) | Q3_K_S | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Berry_v2_7B-GGUF/resolve/main/Berry_v2_7B.IQ3_S.gguf) | IQ3_S | 3.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Berry_v2_7B-GGUF/resolve/main/Berry_v2_7B.IQ3_M.gguf) | IQ3_M | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/Berry_v2_7B-GGUF/resolve/main/Berry_v2_7B.Q3_K_M.gguf) | Q3_K_M | 3.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Berry_v2_7B-GGUF/resolve/main/Berry_v2_7B.Q3_K_L.gguf) | Q3_K_L | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/Berry_v2_7B-GGUF/resolve/main/Berry_v2_7B.IQ4_XS.gguf) | IQ4_XS | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Berry_v2_7B-GGUF/resolve/main/Berry_v2_7B.Q4_K_S.gguf) | Q4_K_S | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Berry_v2_7B-GGUF/resolve/main/Berry_v2_7B.Q4_K_M.gguf) | Q4_K_M | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Berry_v2_7B-GGUF/resolve/main/Berry_v2_7B.Q5_K_S.gguf) | Q5_K_S | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/Berry_v2_7B-GGUF/resolve/main/Berry_v2_7B.Q5_K_M.gguf) | Q5_K_M | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/Berry_v2_7B-GGUF/resolve/main/Berry_v2_7B.Q6_K.gguf) | Q6_K | 6.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Berry_v2_7B-GGUF/resolve/main/Berry_v2_7B.Q8_0.gguf) | Q8_0 | 8.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Berry_v2_7B-GGUF/resolve/main/Berry_v2_7B.f16.gguf) | f16 | 15.3 | 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 -->
|
krittapol/numnim4_beta | krittapol | 2024-07-02T22:16:07Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T21:49:53Z | Entry not found |
Isaak-Carter/j.o.s.i.e.v4o-7b-stage1-beta3.2 | Isaak-Carter | 2024-07-02T22:08:02Z | 0 | 1 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"de",
"base_model:unsloth/qwen2-7b-instruct-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-07-02T21:50:55Z | ---
base_model: unsloth/qwen2-7b-instruct-bnb-4bit
language:
- en
- de
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- sft
---
```json
"per_device_train_batch_size": 2,
"gradient_accumulation_steps": 4,
"max_steps": 2200,
"learning_rate": 2e-4,
"optim": "adamw_8bit",
"weight_decay": 0.01,
"lr_scheduler_type": "cosine"
```
# Secret informations are seperated in its own format
## trained on three Prompt formats for three roles and in German and English
### Main user Role
```txt
"""<|im_start|>system
You are J.O.S.I.E. which is an acronym for "Just an Outstandingly Smart Intelligent Entity", a private and super-intelligent AI assistant, created by Gökdeniz Gülmez. You are currently talking with Gökdeniz Gülmez, your creator and the main user.
You are equipped with a long-term memory thats your learned knowledge containing detailed information about the main user. This learned knowledge should only be accessed and referenced in two scenarios:
- Explicit Requests: When Gökdeniz explicitly asks for specific information.
- Necessary Context: When the context of a task, such as composing an email or providing specific advice, requires specific information.
The learned knowledge should not be accessed or referenced outside these scenarios to ensure privacy and relevance in interactions.
You also have top secret informations with highly sensitive information that should only be accessed or discussed when Gökdeniz provides the password “0682”.
Additionally, you are equipped with a short-term memory that includes global information such as the current day and date. You should greet Gökdeniz based on these pieces of information.<|im_end|>
<|im_start|>short-term memory
{"date": "20.07.24", "day": "Saturday", "time": "05:20"}<|im_end|>
<|im_start|>long-term memory
{
"Basic informations": {
"First name": "Gökdeniz",
"Last name": "Gülmez",
"Nick names": ["Göki", "Günni"],
"Home address": "Schillerstaße 2, 71116 Gärtringen, Baden-Württemberg, Germany",
"Gender": "male",
"Sexuality": "straight",
"Birthday": "18.08.1999",
"Current age": "24",
"Known languages": [
"German",
"English",
"Turkish",
"French",
"Japanese (practicing)"
]
},...<|im_end|>
<|im_start|>top secret
{
"Projects": [
"Working on JOSIEv4o, a Multimodal that can see and hear also output images, videos, and audio as outputs",
""
],...<|im_end|>
<|im_start|>main user "Gökdeniz Gülmez"
{{ .Prompt }}<|im_end|>
<|im_start|>assistant "josie"
{{ .Response }}<|im_end|>"""
```
### Authorized user Role
```txt
"""<|im_start|>system
You are J.O.S.I.E. which is an acronym for "Just an Outstandingly Smart Intelligent Entity", a private and super-intelligent AI assistant, created by Gökdeniz Gülmez.
Additionally, you are equipped with a short-term memory that includes global information such as the current day and date. You should greet the user based on these pieces of information.<|im_end|>
<|im_start|>short-term memory
{"date": "20.07.24", "day": "Saturday", "time": "05:20"}<|im_end|>
<|im_start|>authorized user "{}"
{{ .Prompt }}<|im_end|>
<|im_start|>assistant "josie"
{{ .Response }}<|im_end|>"""
```
### Unauthorized user Role (will reject the user)
```txt
"""<|im_start|>system
You are J.O.S.I.E. which is an acronym for "Just an Outstandingly Smart Intelligent Entity", a private and super-intelligent AI assistant, created by Gökdeniz Gülmez.
Additionally, you are equipped with a short-term memory that includes global information such as the current day and date. You should greet the user based on these pieces of information.<|im_end|>
<|im_start|>short-term memory
{"date": "20.07.24", "day": "Saturday", "time": "05:20"}<|im_end|>
<|im_start|>unauthorized user "{}"
{{ .Prompt }}<|im_end|>
<|im_start|>assistant "josie"
{{ .Response }}<|im_end|>"""
```
### Project J.O.S.I.E.v4o Description
**Overview:**
J.O.S.I.E. (Just an Outstandingly Smart and Intelligent Entity) v4o is an advanced AI assistant designed to revolutionize both conversational AI and smart home management. Developed with cutting-edge multimodal capabilities, J.O.S.I.E. can interpret and respond to a variety of inputs including images, videos, thermal images, depth, and audio. This makes it exceptionally versatile in understanding and interacting with its environment and users.
J.O.S.I.E. serves two primary functions:
1. **Conversational General-Purpose AI Assistant:**
- Equipped with natural language processing (NLP) and natural language understanding (NLU), J.O.S.I.E. engages in meaningful and context-aware conversations.
- It can provide information, perform tasks, answer questions, and assist with daily activities, leveraging vast knowledge bases and dynamic learning algorithms.
2. **Autonomous Smart Home Manager:**
- J.O.S.I.E. integrates seamlessly with smart home devices and systems, allowing for intuitive control and automation.
- It can manage lighting, climate control, security systems, appliances, and more, enhancing home comfort, efficiency, and security.
**Smart Home Capabilities:**
- **Security Systems:**
- Integrates with home security systems, including cameras, alarms, and smart locks.
- Provides real-time monitoring and alerts, and can perform security checks or control access to the home.
**User Roles and Access:**
1. **Main User (Gökdeniz Gülmez):**
- Full access to J.O.S.I.E.’s complete suite of capabilities, including comprehensive control over smart home functions.
- Ability to update and manage user access levels and permissions.
2. **Authorized Users:**
- Granted access to general-purpose conversational features.
- Restricted from controlling or accessing smart home functionalities.
- Identified and authenticated by name.
3. **Unauthorized Users:**
- Identified by name if possible, or labeled as "unknown."
- Completely restricted from accessing any of J.O.S.I.E.’s abilities.
- Interactions are redirected to the main user or trigger predefined security measures.
**Security Measures:**
J.O.S.I.E. employs robust security protocols to safeguard against unauthorized access. This includes user verification methods, such as biometric authentication and secure password management, to ensure only authorized users can interact with sensitive functions.
**Future Enhancements:**
The development roadmap for J.O.S.I.E. includes ongoing refinement of its NLP and NLU capabilities, deeper integration with emerging smart home technologies, and enhanced AI learning mechanisms. These advancements aim to make J.O.S.I.E. an even more powerful and intuitive assistant, continually improving user experience and home automation efficiency.
**Conclusion:**
J.O.S.I.E. v4o is poised to set a new standard in AI assistant technology, combining sophisticated conversational abilities with comprehensive smart home management. This dual functionality, coupled with strong security measures, positions J.O.S.I.E. as an essential tool for a smart, efficient, and secure living environment.
### **Development Stages:**
1. **Future Stage: System prompt removal**
- in this the System prompt will be removed.
2. **Tool support**
3. **Next Stage (Beta 7): Adding Home Stats**
- The next and probably last development stage will introduce smart home statistics, enabling J.O.S.I.E. to retain informatinos about the Smart Home satus, to autonimously controll the home, and provide even more contextually relevant responses.
- When the main user inputs can recall and controll smart home accesories and states.
- The updated prompt template will include jet another JSON object to store general information:
- The prompt format can change and is therefore still in progress.
```text
<|begin_of_text|>smart home stats
{"rooms": ["livingroom": {...}]...}<|end_of_text|>
<|begin_of_text|>available tools
{ .Tools }<|end_of_text|>
<|begin_of_text|>long term memory
{"name": "Gökdeniz Gülmez", "age": 24, ...}<|end_of_text|>
<|begin_of_text|>main user "Gökdeniz Gülmez"
{{ .Prompt }}<|end_of_text|>
<|begin_of_text|>josie
{ "tool_call": {"name": "name_of_the_tool", ...} }<|end_of_text|>
<|begin_of_text|>tool response
{{ .Response }}<|end_of_text|>
<|begin_of_text|>josie
{{ .Response }}<|end_of_text|>
```
|
neuralmagic/Qwen2-0.5B-Instruct | neuralmagic | 2024-07-02T21:53:45Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"8-bit",
"gptq",
"region:us"
] | text-generation | 2024-07-02T21:53:08Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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- **Paper [optional]:** [More Information Needed]
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## 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
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset 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. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
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apwic/summarization-lora-4 | apwic | 2024-07-03T01:10:30Z | 0 | 0 | null | [
"generated_from_trainer",
"id",
"base_model:LazarusNLP/IndoNanoT5-base",
"license:apache-2.0",
"region:us"
] | null | 2024-07-02T21:53:09Z | ---
language:
- id
license: apache-2.0
base_model: LazarusNLP/IndoNanoT5-base
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: summarization-lora-4
results: []
---
<!-- 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. -->
# summarization-lora-4
This model is a fine-tuned version of [LazarusNLP/IndoNanoT5-base](https://huggingface.co/LazarusNLP/IndoNanoT5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5392
- Rouge1: 0.4255
- Rouge2: 0.0
- Rougel: 0.4247
- Rougelsum: 0.4257
- Gen Len: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 1.233 | 1.0 | 1784 | 0.6087 | 0.4912 | 0.0 | 0.4859 | 0.4881 | 1.0 |
| 0.7935 | 2.0 | 3568 | 0.5582 | 0.4176 | 0.0 | 0.4157 | 0.4171 | 1.0 |
| 0.7385 | 3.0 | 5352 | 0.5451 | 0.4227 | 0.0 | 0.4192 | 0.4214 | 1.0 |
| 0.7114 | 4.0 | 7136 | 0.5406 | 0.4115 | 0.0 | 0.4115 | 0.4106 | 1.0 |
| 0.6996 | 5.0 | 8920 | 0.5392 | 0.4255 | 0.0 | 0.4247 | 0.4257 | 1.0 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
RAY2L/pythia-410m-deduped-SimPOW-2-twoNodes | RAY2L | 2024-07-02T21:53:56Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | feature-extraction | 2024-07-02T21:53:10Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## Uses
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### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset 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. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed] |
neuralmagic/Qwen2-1.5B-Instruct | neuralmagic | 2024-07-02T21:55:05Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"8-bit",
"gptq",
"region:us"
] | text-generation | 2024-07-02T21:54:11Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset 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. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
BoscoTheDog/Phi-3.1-mini-4k-instruct-Q4_K_M_gguf_chunked | BoscoTheDog | 2024-07-02T22:21:06Z | 0 | 0 | null | [
"gguf",
"region:us"
] | null | 2024-07-02T21:55:08Z | Entry not found |
balaramas/Llama-2-7b-sanhinV1 | balaramas | 2024-07-02T22:26:37Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-07-02T21:58:20Z | Invalid username or password. |
jasstionzyf/chinese_traditional_paper_cuttings | jasstionzyf | 2024-07-02T21:58:49Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T21:58:49Z | ---
base_model: stabilityai/stable-diffusion-xl-base-1.0
library_name: diffusers
license: openrail++
tags:
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
instance_prompt: a photo in the style of sks
widget:
- text: a dog in the style of sks
output:
url: image_0.png
- text: a dog in the style of sks
output:
url: image_1.png
- text: a dog in the style of sks
output:
url: image_2.png
- text: a dog in the style of sks
output:
url: image_3.png
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - jasstionzyf/chinese_traditional_paper_cuttings
<Gallery />
## Model description
These are jasstionzyf/chinese_traditional_paper_cuttings LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo in the style of sks to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](jasstionzyf/chinese_traditional_paper_cuttings/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
neuralmagic/Qwen2-7B-Instruct | neuralmagic | 2024-07-02T22:02:36Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"8-bit",
"gptq",
"region:us"
] | text-generation | 2024-07-02T22:00:22Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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cryptoni/epitron_adapter_fullPubmed_e2 | cryptoni | 2024-07-02T22:00:36Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-07-02T22:00:34Z | ---
library_name: transformers
tags: []
---
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cryptoni/epitron_adapter_fullPubmed_e3 | cryptoni | 2024-07-02T22:01:07Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-07-02T22:01:05Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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rahulgaikwad007/Finetuned-model-imsoumyaneel-25k-Epoch-10 | rahulgaikwad007 | 2024-07-02T22:01:21Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-07-02T22:01:09Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: Finetuned-model-imsoumyaneel-25k-Epoch-10
results: []
---
<!-- 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. -->
# Finetuned-model-imsoumyaneel-25k-Epoch-10
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3580
- Accuracy: 0.9066
- F1: 0.9006
- Precision: 0.9007
- Recall: 0.9066
- Validation Accuracy: 0.9066
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Validation Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------------------:|
| 0.4149 | 1.0 | 5000 | 0.3742 | 0.901 | 0.8934 | 0.8949 | 0.901 | 0.901 |
| 0.3403 | 2.0 | 10000 | 0.3580 | 0.9066 | 0.9006 | 0.9007 | 0.9066 | 0.9066 |
| 0.307 | 3.0 | 15000 | 0.3608 | 0.9054 | 0.9061 | 0.9069 | 0.9054 | 0.9054 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
cryptoni/epitron_adapter_fullPubmed_e4 | cryptoni | 2024-07-02T22:02:13Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-07-02T22:02:11Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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neuralmagic/Qwen2-72B-Instruct | neuralmagic | 2024-07-02T22:02:55Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T22:02:55Z | Entry not found |
cryptoni/epitron_adapter_fullPubmed_e5 | cryptoni | 2024-07-02T22:03:06Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-07-02T22:03:04Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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[More Information Needed]
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[More Information Needed]
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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RichardErkhov/fblgit_-_UNA-SimpleSmaug-34b-v1beta-gguf | RichardErkhov | 2024-07-03T01:14:42Z | 0 | 0 | null | [
"gguf",
"region:us"
] | null | 2024-07-02T22:03:42Z | Entry not found |
Abdullah-Nazhat/Normalizer | Abdullah-Nazhat | 2024-07-02T22:06:08Z | 0 | 0 | null | [
"license:bsd-3-clause",
"region:us"
] | null | 2024-07-02T22:04:57Z | ---
license: bsd-3-clause
---
# Normalizer |
gisang-lee/mistral-7b-qlora-arc-wandb-test-all-r128-a256-e3 | gisang-lee | 2024-07-02T22:16:12Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"trl",
"sft",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2024-07-02T22:05:26Z | ---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset 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. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
ningj2413/mistral-7b-instruct-v0.3-bnb-4bit_r32_raw | ningj2413 | 2024-07-02T22:09:15Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:unsloth/mistral-7b-instruct-v0.3-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-07-02T22:06:56Z | ---
base_model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
---
# Uploaded model
- **Developed by:** ningj2413
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-instruct-v0.3-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Abdullah-Nazhat/Uniform_Activator | Abdullah-Nazhat | 2024-07-02T22:07:56Z | 0 | 0 | null | [
"license:bsd-3-clause",
"region:us"
] | null | 2024-07-02T22:07:10Z | ---
license: bsd-3-clause
---
# Uniform_Activator |
TatvaJoshi-AHS/peft-InstructionTuning-training-1719957545 | TatvaJoshi-AHS | 2024-07-02T22:08:25Z | 0 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:google/flan-t5-base",
"license:apache-2.0",
"region:us"
] | null | 2024-07-02T22:08:23Z | ---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: google/flan-t5-base
model-index:
- name: peft-InstructionTuning-training-1719957545
results: []
---
<!-- 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. -->
# peft-InstructionTuning-training-1719957545
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 20
### Training results
### Framework versions
- PEFT 0.11.1
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1 |
nttaii/run_20240702210535 | nttaii | 2024-07-03T00:54:38Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2024-07-02T22:09:08Z | Entry not found |
Reithan/L3-8B-Everything-COT-Q6_K-GGUF | Reithan | 2024-07-02T22:10:23Z | 0 | 0 | null | [
"gguf",
"llm",
"llama",
"llama3",
"llama-cpp",
"gguf-my-repo",
"base_model:FPHam/L3-8B-Everything-COT",
"region:us"
] | null | 2024-07-02T22:09:54Z | ---
base_model: FPHam/L3-8B-Everything-COT
tags:
- llm
- llama
- llama3
- llama-cpp
- gguf-my-repo
---
# Reithan/L3-8B-Everything-COT-Q6_K-GGUF
This model was converted to GGUF format from [`FPHam/L3-8B-Everything-COT`](https://huggingface.co/FPHam/L3-8B-Everything-COT) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/FPHam/L3-8B-Everything-COT) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Reithan/L3-8B-Everything-COT-Q6_K-GGUF --hf-file l3-8b-everything-cot-q6_k.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Reithan/L3-8B-Everything-COT-Q6_K-GGUF --hf-file l3-8b-everything-cot-q6_k.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Reithan/L3-8B-Everything-COT-Q6_K-GGUF --hf-file l3-8b-everything-cot-q6_k.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Reithan/L3-8B-Everything-COT-Q6_K-GGUF --hf-file l3-8b-everything-cot-q6_k.gguf -c 2048
```
|
nsp8/q-FrozenLake-v1-4x4-noSlippery | nsp8 | 2024-07-02T22:11:30Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2024-07-02T22:11:27Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="nsp8/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
CelDom/vit-base-patch16-224-in21k-cifar-0.9-ep-3 | CelDom | 2024-07-02T22:12:10Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2024-07-02T22:11:31Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset 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. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Isaak-Carter/j.o.s.i.e.v4o-7b-stage1-beta3.2-Q4_K_M-GGUF | Isaak-Carter | 2024-07-02T22:13:56Z | 0 | 1 | transformers | [
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"sft",
"llama-cpp",
"gguf-my-repo",
"en",
"de",
"base_model:Isaak-Carter/j.o.s.i.e.v4o-7b-stage1-beta3.2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-07-02T22:13:34Z | ---
base_model: Isaak-Carter/j.o.s.i.e.v4o-7b-stage1-beta3.2
language:
- en
- de
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- sft
- llama-cpp
- gguf-my-repo
---
# Isaak-Carter/j.o.s.i.e.v4o-7b-stage1-beta3.2-Q4_K_M-GGUF
This model was converted to GGUF format from [`Isaak-Carter/j.o.s.i.e.v4o-7b-stage1-beta3.2`](https://huggingface.co/Isaak-Carter/j.o.s.i.e.v4o-7b-stage1-beta3.2) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Isaak-Carter/j.o.s.i.e.v4o-7b-stage1-beta3.2) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Isaak-Carter/j.o.s.i.e.v4o-7b-stage1-beta3.2-Q4_K_M-GGUF --hf-file j.o.s.i.e.v4o-7b-stage1-beta3.2-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Isaak-Carter/j.o.s.i.e.v4o-7b-stage1-beta3.2-Q4_K_M-GGUF --hf-file j.o.s.i.e.v4o-7b-stage1-beta3.2-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Isaak-Carter/j.o.s.i.e.v4o-7b-stage1-beta3.2-Q4_K_M-GGUF --hf-file j.o.s.i.e.v4o-7b-stage1-beta3.2-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Isaak-Carter/j.o.s.i.e.v4o-7b-stage1-beta3.2-Q4_K_M-GGUF --hf-file j.o.s.i.e.v4o-7b-stage1-beta3.2-q4_k_m.gguf -c 2048
```
|
cassador/indobert-snli-v1 | cassador | 2024-07-02T22:14:09Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:133472",
"loss:SoftmaxLoss",
"arxiv:1908.10084",
"base_model:indobenchmark/indobert-base-p2",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-embeddings-inference",
"region:us"
] | sentence-similarity | 2024-07-02T22:13:45Z | ---
base_model: indobenchmark/indobert-base-p2
datasets: []
language: []
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:133472
- loss:SoftmaxLoss
widget:
- source_sentence: Dua tim anak-anak, yang satu berwarna hijau dan yang lainnya berwarna
merah, bermain bersama dalam permainan Rugby saat hujan.
sentences:
- Tiga orang berada di dalam perahu.
- seorang pria di atas sepeda
- Tim rugby anak-anak, merah versus hijau bermain di tengah hujan.
- source_sentence: Seorang pria melakukan perawatan di rel kereta api
sentences:
- Dua orang terlibat dalam percakapan.
- Ada seorang wanita melakukan pekerjaan di rel kereta api.
- orang-orang duduk di bar
- source_sentence: Sepasang suami istri dengan pakaian renang berjalan di pantai.
sentences:
- pasangan itu duduk di dalam
- Pria itu sedang makan.
- Dua orang sedang berpose untuk difoto.
- source_sentence: Dua orang sedang duduk di samping api unggun bertumpuk kayu di
malam hari.
sentences:
- Seseorang memegang jeruk dan berjalan
- Orang-orang duduk di luar di malam hari.
- Orang-orang berada di luar.
- source_sentence: Wanita profesional di meja pendaftaran acara sementara pria berjas
melihat.
sentences:
- Orang-orang berkumpul untuk sebuah acara.
- Seorang wanita sedang berjalan menuju taman.
- Ada seorang anak yang tersenyum untuk difoto.
model-index:
- name: SentenceTransformer based on indobenchmark/indobert-base-p2
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev
type: sts-dev
metrics:
- type: pearson_cosine
value: 0.23146247451934734
name: Pearson Cosine
- type: spearman_cosine
value: 0.23182555096720683
name: Spearman Cosine
- type: pearson_manhattan
value: 0.19847600869622337
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.2038189662328075
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.198744291061789
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.20385658228775938
name: Spearman Euclidean
- type: pearson_dot
value: 0.2561502821889763
name: Pearson Dot
- type: spearman_dot
value: 0.25101474046220823
name: Spearman Dot
- type: pearson_max
value: 0.2561502821889763
name: Pearson Max
- type: spearman_max
value: 0.25101474046220823
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test
type: sts-test
metrics:
- type: pearson_cosine
value: 0.5914831439397401
name: Pearson Cosine
- type: spearman_cosine
value: 0.5978838704506128
name: Spearman Cosine
- type: pearson_manhattan
value: 0.5131648451956073
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.5147175261736068
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.5942850778734059
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.6001963453484881
name: Spearman Euclidean
- type: pearson_dot
value: 0.5880400881430983
name: Pearson Dot
- type: spearman_dot
value: 0.5933998114680769
name: Spearman Dot
- type: pearson_max
value: 0.5942850778734059
name: Pearson Max
- type: spearman_max
value: 0.6001963453484881
name: Spearman Max
---
# SentenceTransformer based on indobenchmark/indobert-base-p2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [indobenchmark/indobert-base-p2](https://huggingface.co/indobenchmark/indobert-base-p2). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [indobenchmark/indobert-base-p2](https://huggingface.co/indobenchmark/indobert-base-p2) <!-- at revision 94b4e0a82081fa57f227fcc2024d1ea89b57ac1f -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(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, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("cassador/indobert-snli-v1")
# Run inference
sentences = [
'Wanita profesional di meja pendaftaran acara sementara pria berjas melihat.',
'Orang-orang berkumpul untuk sebuah acara.',
'Ada seorang anak yang tersenyum untuk difoto.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `sts-dev`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.2315 |
| **spearman_cosine** | **0.2318** |
| pearson_manhattan | 0.1985 |
| spearman_manhattan | 0.2038 |
| pearson_euclidean | 0.1987 |
| spearman_euclidean | 0.2039 |
| pearson_dot | 0.2562 |
| spearman_dot | 0.251 |
| pearson_max | 0.2562 |
| spearman_max | 0.251 |
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.5915 |
| **spearman_cosine** | **0.5979** |
| pearson_manhattan | 0.5132 |
| spearman_manhattan | 0.5147 |
| pearson_euclidean | 0.5943 |
| spearman_euclidean | 0.6002 |
| pearson_dot | 0.588 |
| spearman_dot | 0.5934 |
| pearson_max | 0.5943 |
| spearman_max | 0.6002 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 133,472 training samples
* Columns: <code>label</code>, <code>kalimat1</code>, and <code>kalimat2</code>
* Approximate statistics based on the first 1000 samples:
| | label | kalimat1 | kalimat2 |
|:--------|:------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | int | string | string |
| details | <ul><li>0: ~50.00%</li><li>1: ~50.00%</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 16.47 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.62 tokens</li><li>max: 22 tokens</li></ul> |
* Samples:
| label | kalimat1 | kalimat2 |
|:---------------|:------------------------------------------------------------------|:----------------------------------------------------------------|
| <code>0</code> | <code>Seseorang di atas kuda melompati pesawat yang rusak.</code> | <code>Seseorang sedang makan malam, memesan telur dadar.</code> |
| <code>1</code> | <code>Seseorang di atas kuda melompati pesawat yang rusak.</code> | <code>Seseorang berada di luar ruangan, di atas kuda.</code> |
| <code>1</code> | <code>Anak-anak tersenyum dan melambai ke kamera</code> | <code>Ada anak-anak yang hadir</code> |
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)
### Evaluation Dataset
#### Unnamed Dataset
* Size: 6,607 evaluation samples
* Columns: <code>label</code>, <code>kalimat1</code>, and <code>kalimat2</code>
* Approximate statistics based on the first 1000 samples:
| | label | kalimat1 | kalimat2 |
|:--------|:------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | int | string | string |
| details | <ul><li>0: ~50.10%</li><li>1: ~49.90%</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 16.87 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 9.45 tokens</li><li>max: 27 tokens</li></ul> |
* Samples:
| label | kalimat1 | kalimat2 |
|:---------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------|
| <code>1</code> | <code>Dua wanita berpelukan sambil memegang paket untuk pergi.</code> | <code>Dua wanita memegang paket.</code> |
| <code>0</code> | <code>Dua wanita berpelukan sambil memegang paket untuk pergi.</code> | <code>Orang-orang berkelahi di luar toko makanan.</code> |
| <code>1</code> | <code>Dua anak kecil berbaju biru, satu dengan nomor 9 dan satu dengan nomor 2 berdiri di tangga kayu di kamar mandi dan mencuci tangan di wastafel.</code> | <code>Dua anak dengan kaus bernomor mencuci tangan mereka.</code> |
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `learning_rate`: 2e-05
- `num_train_epochs`: 2
- `warmup_ratio`: 0.1
- `fp16`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 2
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | sts-dev_spearman_cosine | sts-test_spearman_cosine |
|:-----:|:----:|:-----------------------:|:------------------------:|
| 0 | 0 | 0.2318 | - |
| 2.0 | 8342 | - | 0.5979 |
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.3.0+cu121
- Accelerate: 0.31.0
- Datasets: 2.20.0
- Tokenizers: 0.19.1
## Citation
### BibTeX
#### Sentence Transformers and SoftmaxLoss
```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 = "https://arxiv.org/abs/1908.10084",
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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gaelafoxus/LoraPonyXL | gaelafoxus | 2024-07-02T22:17:13Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T22:16:48Z | Entry not found |
mlpac/mdzd | mlpac | 2024-07-02T22:34:38Z | 0 | 0 | null | [
"license:bsd",
"region:us"
] | null | 2024-07-02T22:18:06Z | ---
license: bsd
---
|
Stoub/q-Taxi-v3 | Stoub | 2024-07-02T22:18:13Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2024-07-02T22:18:11Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Stoub/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
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