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<!-- 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. -->
# spellcorrector_11_02_050_1_per_word_v6
This model is a fine-tuned version of [Buseak/spellcorrector_11_02_050_1_per_word_v5](https://huggingface.co/Buseak/spellcorrector_11_02_050_1_per_word_v5) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0149
- Precision: 1.0
- Recall: 1.0
- F1: 1.0
- Accuracy: 0.9955
## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0704 | 1.0 | 967 | 0.0434 | 0.9946 | 0.9936 | 0.9941 | 0.9866 |
| 0.0636 | 2.0 | 1934 | 0.0385 | 0.9930 | 0.9941 | 0.9936 | 0.9881 |
| 0.0575 | 3.0 | 2901 | 0.0343 | 0.9979 | 0.9973 | 0.9976 | 0.9894 |
| 0.051 | 4.0 | 3868 | 0.0288 | 0.9984 | 0.9984 | 0.9984 | 0.9910 |
| 0.0473 | 5.0 | 4835 | 0.0243 | 0.9995 | 0.9995 | 0.9995 | 0.9922 |
| 0.043 | 6.0 | 5802 | 0.0223 | 0.9995 | 0.9995 | 0.9995 | 0.9931 |
| 0.0393 | 7.0 | 6769 | 0.0190 | 1.0 | 0.9984 | 0.9992 | 0.9941 |
| 0.0366 | 8.0 | 7736 | 0.0181 | 1.0 | 0.9989 | 0.9995 | 0.9945 |
| 0.0336 | 9.0 | 8703 | 0.0150 | 1.0 | 0.9995 | 0.9997 | 0.9954 |
| 0.0325 | 10.0 | 9670 | 0.0149 | 1.0 | 1.0 | 1.0 | 0.9955 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "Buseak/spellcorrector_11_02_050_1_per_word_v5", "model-index": [{"name": "spellcorrector_11_02_050_1_per_word_v6", "results": []}]} | token-classification | Buseak/spellcorrector_11_02_050_1_per_word_v6 | [
"transformers",
"tensorboard",
"safetensors",
"canine",
"token-classification",
"generated_from_trainer",
"base_model:Buseak/spellcorrector_11_02_050_1_per_word_v5",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-11T19:46:36+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #canine #token-classification #generated_from_trainer #base_model-Buseak/spellcorrector_11_02_050_1_per_word_v5 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| spellcorrector\_11\_02\_050\_1\_per\_word\_v6
=============================================
This model is a fine-tuned version of Buseak/spellcorrector\_11\_02\_050\_1\_per\_word\_v5 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0149
* Precision: 1.0
* Recall: 1.0
* F1: 1.0
* Accuracy: 0.9955
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: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
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"### Training results",
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
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"passage: TAGS\n#transformers #tensorboard #safetensors #canine #token-classification #generated_from_trainer #base_model-Buseak/spellcorrector_11_02_050_1_per_word_v5 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | tommymarto/LernnaviBERT_mcqbert3_correct_answers_768 | [
"transformers",
"safetensors",
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #bert #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
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Use the code below to get started with the model.
## Training Details
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## Evaluation
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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] |
null | null | stable-baselines3 |
# **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
...
```
| {"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": "241.51 +/- 12.81", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | zubchick/deep-rl-class-unit1 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-11T19:52:16+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
39,
41,
17
] | [
"passage: TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
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null | null | peft |
<!-- 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. -->
# meditron-qlora-samsum
This model is a fine-tuned version of [epfl-llm/meditron-7b](https://huggingface.co/epfl-llm/meditron-7b) on the generator 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 1
### Training results
### Framework versions
- PEFT 0.7.2.dev0
- Transformers 4.38.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1 | {"license": "llama2", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "epfl-llm/meditron-7b", "model-index": [{"name": "meditron-qlora-samsum", "results": []}]} | null | Farhang87/meditron-qlora-samsum | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:epfl-llm/meditron-7b",
"license:llama2",
"region:us"
] | 2024-02-11T19:53:16+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-epfl-llm/meditron-7b #license-llama2 #region-us
|
# meditron-qlora-samsum
This model is a fine-tuned version of epfl-llm/meditron-7b on the generator 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 1
### Training results
### Framework versions
- PEFT 0.7.2.dev0
- Transformers 4.38.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1 | [
"# meditron-qlora-samsum\n\nThis model is a fine-tuned version of epfl-llm/meditron-7b on the generator dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: constant\n- lr_scheduler_warmup_ratio: 0.03\n- num_epochs: 1",
"### Training results",
"### Framework versions\n\n- PEFT 0.7.2.dev0\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.2+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
"TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-epfl-llm/meditron-7b #license-llama2 #region-us \n",
"# meditron-qlora-samsum\n\nThis model is a fine-tuned version of epfl-llm/meditron-7b on the generator dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: constant\n- lr_scheduler_warmup_ratio: 0.03\n- num_epochs: 1",
"### Training results",
"### Framework versions\n\n- PEFT 0.7.2.dev0\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.2+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
59,
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"passage: TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-epfl-llm/meditron-7b #license-llama2 #region-us \n# meditron-qlora-samsum\n\nThis model is a fine-tuned version of epfl-llm/meditron-7b on the generator dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: constant\n- lr_scheduler_warmup_ratio: 0.03\n- num_epochs: 1### Training results### Framework versions\n\n- PEFT 0.7.2.dev0\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.2+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
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null | null | transformers |
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# Model Card for Model ID
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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:
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- Language(s) (NLP):
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### Model Sources [optional]
- Repository:
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- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
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### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
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### Testing Data, Factors & Metrics
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null | null | null |
NOT FOR USE - BUG INSTINSTINSTINSTINSTINST --
# This model was merged, trained, and so on, thanks to the knowledge I gained from reading Maxime Labonne's course. Special thanks to him!
[Labonne LLM Course](https://github.com/mlabonne/llm-course)
# NeuTrixOmniBe-DPO
NeuTrix7000-7b-DPO is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
## 🧩 Configuration
```yaml
MODEL_NAME = "NeuTrix7000-7b-DPO"
yaml_config = """
slices:
- sources:
- model: CultriX/NeuralTrix-7B-dpo
layer_range: [0, 32]
- model: paulml/OmniBeagleSquaredMBX-v3-7B-v2
layer_range: [0, 32]
merge_method: slerp
base_model: CultriX/NeuralTrix-7B-dpo
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
"""
```
It was then trained with DPO using:
* Intel/orca_dpo_pairs
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Kukedlc/NeuTrix7000-7b-DPO"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=128, do_sample=True, temperature=0.5, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"]) | {"license": "apache-2.0", "tags": ["merge", "mergekit", "#dpo", "MaximeLabonne", "#mergeofmerge"], "base_model": ["CultriX/NeuralTrix-7B-dpo", "paulml/OmniBeagleSquaredMBX-v3-7B-v2"]} | null | Kukedlc/NeutriX7000-7b-DPO | [
"merge",
"mergekit",
"#dpo",
"MaximeLabonne",
"#mergeofmerge",
"base_model:CultriX/NeuralTrix-7B-dpo",
"base_model:paulml/OmniBeagleSquaredMBX-v3-7B-v2",
"license:apache-2.0",
"region:us"
] | 2024-02-11T20:00:00+00:00 | [] | [] | TAGS
#merge #mergekit ##dpo #MaximeLabonne ##mergeofmerge #base_model-CultriX/NeuralTrix-7B-dpo #base_model-paulml/OmniBeagleSquaredMBX-v3-7B-v2 #license-apache-2.0 #region-us
|
NOT FOR USE - BUG INSTINSTINSTINSTINSTINST --
# This model was merged, trained, and so on, thanks to the knowledge I gained from reading Maxime Labonne's course. Special thanks to him!
Labonne LLM Course
!NeuTrixOmniBe
# NeuTrixOmniBe-DPO
NeuTrix7000-7b-DPO is a merge of the following models using LazyMergekit:
## Configuration
It was then trained with DPO using:
* Intel/orca_dpo_pairs
## Usage
'''python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Kukedlc/NeuTrix7000-7b-DPO"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=128, do_sample=True, temperature=0.5, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"]) | [
"# This model was merged, trained, and so on, thanks to the knowledge I gained from reading Maxime Labonne's course. Special thanks to him! \nLabonne LLM Course\n\n!NeuTrixOmniBe",
"# NeuTrixOmniBe-DPO\n\nNeuTrix7000-7b-DPO is a merge of the following models using LazyMergekit:",
"## Configuration\n\n\n\nIt was then trained with DPO using: \n* Intel/orca_dpo_pairs",
"## Usage\n\n'''python\n!pip install -qU transformers accelerate\nfrom transformers import AutoTokenizer\nimport transformers\nimport torch\nmodel = \"Kukedlc/NeuTrix7000-7b-DPO\"\nmessages = [{\"role\": \"user\", \"content\": \"What is a large language model?\"}]\ntokenizer = AutoTokenizer.from_pretrained(model)\nprompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)\npipeline = transformers.pipeline(\n \"text-generation\",\n model=model,\n torch_dtype=torch.float16,\n device_map=\"auto\",\n)\noutputs = pipeline(prompt, max_new_tokens=128, do_sample=True, temperature=0.5, top_k=50, top_p=0.95)\nprint(outputs[0][\"generated_text\"])"
] | [
"TAGS\n#merge #mergekit ##dpo #MaximeLabonne ##mergeofmerge #base_model-CultriX/NeuralTrix-7B-dpo #base_model-paulml/OmniBeagleSquaredMBX-v3-7B-v2 #license-apache-2.0 #region-us \n",
"# This model was merged, trained, and so on, thanks to the knowledge I gained from reading Maxime Labonne's course. Special thanks to him! \nLabonne LLM Course\n\n!NeuTrixOmniBe",
"# NeuTrixOmniBe-DPO\n\nNeuTrix7000-7b-DPO is a merge of the following models using LazyMergekit:",
"## Configuration\n\n\n\nIt was then trained with DPO using: \n* Intel/orca_dpo_pairs",
"## Usage\n\n'''python\n!pip install -qU transformers accelerate\nfrom transformers import AutoTokenizer\nimport transformers\nimport torch\nmodel = \"Kukedlc/NeuTrix7000-7b-DPO\"\nmessages = [{\"role\": \"user\", \"content\": \"What is a large language model?\"}]\ntokenizer = AutoTokenizer.from_pretrained(model)\nprompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)\npipeline = transformers.pipeline(\n \"text-generation\",\n model=model,\n torch_dtype=torch.float16,\n device_map=\"auto\",\n)\noutputs = pipeline(prompt, max_new_tokens=128, do_sample=True, temperature=0.5, top_k=50, top_p=0.95)\nprint(outputs[0][\"generated_text\"])"
] | [
80,
49,
33,
25,
230
] | [
"passage: TAGS\n#merge #mergekit ##dpo #MaximeLabonne ##mergeofmerge #base_model-CultriX/NeuralTrix-7B-dpo #base_model-paulml/OmniBeagleSquaredMBX-v3-7B-v2 #license-apache-2.0 #region-us \n# This model was merged, trained, and so on, thanks to the knowledge I gained from reading Maxime Labonne's course. Special thanks to him! \nLabonne LLM Course\n\n!NeuTrixOmniBe# NeuTrixOmniBe-DPO\n\nNeuTrix7000-7b-DPO is a merge of the following models using LazyMergekit:## Configuration\n\n\n\nIt was then trained with DPO using: \n* Intel/orca_dpo_pairs## Usage\n\n'''python\n!pip install -qU transformers accelerate\nfrom transformers import AutoTokenizer\nimport transformers\nimport torch\nmodel = \"Kukedlc/NeuTrix7000-7b-DPO\"\nmessages = [{\"role\": \"user\", \"content\": \"What is a large language model?\"}]\ntokenizer = AutoTokenizer.from_pretrained(model)\nprompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)\npipeline = transformers.pipeline(\n \"text-generation\",\n model=model,\n torch_dtype=torch.float16,\n device_map=\"auto\",\n)\noutputs = pipeline(prompt, max_new_tokens=128, do_sample=True, temperature=0.5, top_k=50, top_p=0.95)\nprint(outputs[0][\"generated_text\"])"
] | [
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | tommymarto/LernnaviBERT_mcqbert3_correct_answers_384 | [
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# Model Card for Model ID
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] |
null | null | peft |
# Low-rank decomposition of [valine/OpenPirate](https://huggingface.co/valine/OpenPirate) using [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) as base
Created using [LoRD](https://github.com/thomasgauthier/LoRD)
| {"library_name": "peft", "base_model": "teknium/OpenHermes-2.5-Mistral-7B"} | null | thomasgauthier/OpenPirate-LoRD | [
"peft",
"safetensors",
"base_model:teknium/OpenHermes-2.5-Mistral-7B",
"region:us"
] | 2024-02-11T20:04:58+00:00 | [] | [] | TAGS
#peft #safetensors #base_model-teknium/OpenHermes-2.5-Mistral-7B #region-us
|
# Low-rank decomposition of valine/OpenPirate using teknium/OpenHermes-2.5-Mistral-7B as base
Created using LoRD
| [
"# Low-rank decomposition of valine/OpenPirate using teknium/OpenHermes-2.5-Mistral-7B as base\n\nCreated using LoRD"
] | [
"TAGS\n#peft #safetensors #base_model-teknium/OpenHermes-2.5-Mistral-7B #region-us \n",
"# Low-rank decomposition of valine/OpenPirate using teknium/OpenHermes-2.5-Mistral-7B as base\n\nCreated using LoRD"
] | [
32,
35
] | [
"passage: TAGS\n#peft #safetensors #base_model-teknium/OpenHermes-2.5-Mistral-7B #region-us \n# Low-rank decomposition of valine/OpenPirate using teknium/OpenHermes-2.5-Mistral-7B as base\n\nCreated using LoRD"
] | [
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null | null | peft |
<!-- 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. -->
# results
This model is a fine-tuned version of [yentinglin/Taiwan-LLM-7B-v2.1-chat](https://huggingface.co/yentinglin/Taiwan-LLM-7B-v2.1-chat) 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: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 500
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.2.0+cu118
- Datasets 2.16.1
- Tokenizers 0.15.1 | {"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "yentinglin/Taiwan-LLM-7B-v2.1-chat", "model-index": [{"name": "results", "results": []}]} | null | twjHong/results | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:yentinglin/Taiwan-LLM-7B-v2.1-chat",
"license:apache-2.0",
"region:us"
] | 2024-02-11T20:08:19+00:00 | [] | [] | TAGS
#peft #safetensors #trl #sft #generated_from_trainer #base_model-yentinglin/Taiwan-LLM-7B-v2.1-chat #license-apache-2.0 #region-us
|
# results
This model is a fine-tuned version of yentinglin/Taiwan-LLM-7B-v2.1-chat 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: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 500
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.2.0+cu118
- Datasets 2.16.1
- Tokenizers 0.15.1 | [
"# results\n\nThis model is a fine-tuned version of yentinglin/Taiwan-LLM-7B-v2.1-chat on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 1\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- training_steps: 500",
"### Training results",
"### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu118\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
"TAGS\n#peft #safetensors #trl #sft #generated_from_trainer #base_model-yentinglin/Taiwan-LLM-7B-v2.1-chat #license-apache-2.0 #region-us \n",
"# results\n\nThis model is a fine-tuned version of yentinglin/Taiwan-LLM-7B-v2.1-chat on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 1\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- training_steps: 500",
"### Training results",
"### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu118\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
] | [
56,
36,
6,
12,
8,
3,
88,
4,
39
] | [
"passage: TAGS\n#peft #safetensors #trl #sft #generated_from_trainer #base_model-yentinglin/Taiwan-LLM-7B-v2.1-chat #license-apache-2.0 #region-us \n# results\n\nThis model is a fine-tuned version of yentinglin/Taiwan-LLM-7B-v2.1-chat on an unknown dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 1\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- training_steps: 500### Training results### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu118\n- Datasets 2.16.1\n- Tokenizers 0.15.1"
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null | null | peft |
# Model Card for Model ID
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## Model Details
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- PEFT 0.8.2 | {"library_name": "peft", "base_model": "meta-llama/Llama-2-7b-chat-hf"} | null | NBA55/llama2-7B-diversity-improved-dataset-epoch_4-updated | [
"peft",
"arxiv:1910.09700",
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"1910.09700"
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#peft #arxiv-1910.09700 #base_model-meta-llama/Llama-2-7b-chat-hf #region-us
|
# Model Card for Model ID
## Model Details
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## Uses
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### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
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] |
null | null | transformers |
# Model card for MisterUkrainianDPO
DPO Iteration of [MisterUkrainian](https://huggingface.co/Radu1999/MisterUkrainian)
## Instruction format
In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[/INST]` tokens.
E.g.
```
text = "[INST]Відповідайте лише буквою правильної відповіді: Елементи експресіонізму наявні у творі: A. «Камінний хрест», B. «Інститутка», C. «Маруся», D. «Людина»[/INST]"
```
This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method:
## Model Architecture
This instruction model is based on Mistral-7B-v0.2, a transformer model with the following architecture choices:
- Grouped-Query Attention
- Sliding-Window Attention
- Byte-fallback BPE tokenizer
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Radu1999/MisterUkrainianDPO"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.bfloat16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
## Author
Radu Chivereanu | {"license": "apache-2.0", "library_name": "transformers"} | text-generation | Radu1999/MisterUkrainianDPO | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-11T20:20:56+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #conversational #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model card for MisterUkrainianDPO
DPO Iteration of MisterUkrainian
## Instruction format
In order to leverage instruction fine-tuning, your prompt should be surrounded by '[INST]' and '[/INST]' tokens.
E.g.
This format is available as a chat template via the 'apply_chat_template()' method:
## Model Architecture
This instruction model is based on Mistral-7B-v0.2, a transformer model with the following architecture choices:
- Grouped-Query Attention
- Sliding-Window Attention
- Byte-fallback BPE tokenizer
## Usage
## Author
Radu Chivereanu | [
"# Model card for MisterUkrainianDPO\n\nDPO Iteration of MisterUkrainian",
"## Instruction format\n\nIn order to leverage instruction fine-tuning, your prompt should be surrounded by '[INST]' and '[/INST]' tokens.\n\nE.g.\n\n\nThis format is available as a chat template via the 'apply_chat_template()' method:",
"## Model Architecture\nThis instruction model is based on Mistral-7B-v0.2, a transformer model with the following architecture choices:\n- Grouped-Query Attention\n- Sliding-Window Attention\n- Byte-fallback BPE tokenizer",
"## Usage",
"## Author\n\nRadu Chivereanu"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #conversational #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model card for MisterUkrainianDPO\n\nDPO Iteration of MisterUkrainian",
"## Instruction format\n\nIn order to leverage instruction fine-tuning, your prompt should be surrounded by '[INST]' and '[/INST]' tokens.\n\nE.g.\n\n\nThis format is available as a chat template via the 'apply_chat_template()' method:",
"## Model Architecture\nThis instruction model is based on Mistral-7B-v0.2, a transformer model with the following architecture choices:\n- Grouped-Query Attention\n- Sliding-Window Attention\n- Byte-fallback BPE tokenizer",
"## Usage",
"## Author\n\nRadu Chivereanu"
] | [
59,
22,
67,
56,
3,
6
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #conversational #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model card for MisterUkrainianDPO\n\nDPO Iteration of MisterUkrainian## Instruction format\n\nIn order to leverage instruction fine-tuning, your prompt should be surrounded by '[INST]' and '[/INST]' tokens.\n\nE.g.\n\n\nThis format is available as a chat template via the 'apply_chat_template()' method:## Model Architecture\nThis instruction model is based on Mistral-7B-v0.2, a transformer model with the following architecture choices:\n- Grouped-Query Attention\n- Sliding-Window Attention\n- Byte-fallback BPE tokenizer## Usage## Author\n\nRadu Chivereanu"
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null | null | transformers |
<!-- 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. -->
# 400STEPS_1e6rate_Mistral_SFT
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2867
## 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-06
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 400
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.3194 | 0.12 | 60 | 0.3358 |
| 0.3325 | 0.23 | 120 | 0.3437 |
| 0.2965 | 0.35 | 180 | 0.2997 |
| 0.2927 | 0.47 | 240 | 0.2926 |
| 0.2932 | 0.59 | 300 | 0.2878 |
| 0.2889 | 0.7 | 360 | 0.2867 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.0.0+cu117
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "mistralai/Mistral-7B-v0.1", "model-index": [{"name": "400STEPS_1e6rate_Mistral_SFT", "results": []}]} | text-generation | tsavage68/400STEPS_1e6rate_Mistral_SFT_zeroshot | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"trl",
"sft",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-11T20:24:52+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #trl #sft #generated_from_trainer #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| 400STEPS\_1e6rate\_Mistral\_SFT
===============================
This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2867
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-06
* train\_batch\_size: 4
* eval\_batch\_size: 1
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 100
* training\_steps: 400
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.0.0+cu117
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-06\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 400",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.0+cu117\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #trl #sft #generated_from_trainer #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-06\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 400",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.0+cu117\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
84,
144,
4,
33
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #trl #sft #generated_from_trainer #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-06\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 400### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.0+cu117\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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null | null | sentence-transformers |
# DivyaMereddy007/RecipeBert1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('DivyaMereddy007/RecipeBert1')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('DivyaMereddy007/RecipeBert1')
model = AutoModel.from_pretrained('DivyaMereddy007/RecipeBert1')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=DivyaMereddy007/RecipeBert1)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 1197 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 4,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 478.8,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, '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})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | {"library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | sentence-similarity | DivyaMereddy007/RecipeBert1 | [
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | 2024-02-11T20:25:20+00:00 | [] | [] | TAGS
#sentence-transformers #safetensors #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# DivyaMereddy007/RecipeBert1
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can use the model like this:
## Usage (HuggingFace Transformers)
Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL
## Training
The model was trained with the parameters:
DataLoader:
'URL.dataloader.DataLoader' of length 1197 with parameters:
Loss:
'sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss'
Parameters of the fit()-Method:
## Full Model Architecture
## Citing & Authors
| [
"# DivyaMereddy007/RecipeBert1\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.",
"## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL",
"## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 1197 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss' \n\nParameters of the fit()-Method:",
"## Full Model Architecture",
"## Citing & Authors"
] | [
"TAGS\n#sentence-transformers #safetensors #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# DivyaMereddy007/RecipeBert1\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.",
"## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL",
"## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 1197 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss' \n\nParameters of the fit()-Method:",
"## Full Model Architecture",
"## Citing & Authors"
] | [
43,
55,
38,
64,
29,
78,
5,
6
] | [
"passage: TAGS\n#sentence-transformers #safetensors #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n# DivyaMereddy007/RecipeBert1\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 1197 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss' \n\nParameters of the fit()-Method:## Full Model Architecture## Citing & Authors"
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null | null | transformers |
Mistral 7b finetuned. This is only for test purposes.
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New: Create and edit this model card directly on the website! | {"language": ["en"], "license": "apache-2.0", "metrics": ["accuracy"], "pipeline_tag": "text-generation"} | text-generation | max-2022/test_mistral2 | [
"transformers",
"safetensors",
"mistral",
"feature-extraction",
"text-generation",
"conversational",
"en",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-11T20:25:28+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #mistral #feature-extraction #text-generation #conversational #en #license-apache-2.0 #endpoints_compatible #text-generation-inference #region-us
|
Mistral 7b finetuned. This is only for test purposes.
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New: Create and edit this model card directly on the website! | [] | [
"TAGS\n#transformers #safetensors #mistral #feature-extraction #text-generation #conversational #en #license-apache-2.0 #endpoints_compatible #text-generation-inference #region-us \n"
] | [
59
] | [
"passage: TAGS\n#transformers #safetensors #mistral #feature-extraction #text-generation #conversational #en #license-apache-2.0 #endpoints_compatible #text-generation-inference #region-us \n"
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null | null | diffusers | ### private-dif Dreambooth model trained by Ashdraj with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
| {"license": "creativeml-openrail-m", "tags": ["text-to-image", "stable-diffusion"]} | text-to-image | Ashdraj/private-dif | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2024-02-11T20:32:53+00:00 | [] | [] | TAGS
#diffusers #safetensors #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us
| ### private-dif Dreambooth model trained by Ashdraj with TheLastBen's fast-DreamBooth notebook
Test the concept via A1111 Colab fast-Colab-A1111
Sample pictures of this concept:
| [
"### private-dif Dreambooth model trained by Ashdraj with TheLastBen's fast-DreamBooth notebook\n\n\nTest the concept via A1111 Colab fast-Colab-A1111\n\nSample pictures of this concept:"
] | [
"TAGS\n#diffusers #safetensors #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n",
"### private-dif Dreambooth model trained by Ashdraj with TheLastBen's fast-DreamBooth notebook\n\n\nTest the concept via A1111 Colab fast-Colab-A1111\n\nSample pictures of this concept:"
] | [
61,
51
] | [
"passage: TAGS\n#diffusers #safetensors #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n### private-dif Dreambooth model trained by Ashdraj with TheLastBen's fast-DreamBooth notebook\n\n\nTest the concept via A1111 Colab fast-Colab-A1111\n\nSample pictures of this concept:"
] | [
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null | null | transformers |
<!-- 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. -->
# 400STEPS_05beta_1e7rate_Meditron7B
This model is a fine-tuned version of [epfl-llm/meditron-7b](https://huggingface.co/epfl-llm/meditron-7b) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6864
- Rewards/chosen: 0.0004
- Rewards/rejected: -0.0144
- Rewards/accuracies: 0.4945
- Rewards/margins: 0.0148
- Logps/rejected: -27.8226
- Logps/chosen: -26.4806
- Logits/rejected: -0.6110
- Logits/chosen: -0.6109
## 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-07
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 400
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.6941 | 0.1 | 50 | 0.6931 | -0.0003 | -0.0011 | 0.4044 | 0.0008 | -27.7959 | -26.4820 | -0.6106 | -0.6104 |
| 0.6927 | 0.2 | 100 | 0.6912 | -0.0047 | -0.0093 | 0.4769 | 0.0046 | -27.8123 | -26.4908 | -0.6105 | -0.6104 |
| 0.6838 | 0.29 | 150 | 0.6896 | -0.0023 | -0.0105 | 0.5077 | 0.0082 | -27.8146 | -26.4860 | -0.6101 | -0.6100 |
| 0.6906 | 0.39 | 200 | 0.6886 | -0.0007 | -0.0107 | 0.4989 | 0.0100 | -27.8151 | -26.4828 | -0.6109 | -0.6108 |
| 0.6789 | 0.49 | 250 | 0.6877 | -0.0035 | -0.0154 | 0.5121 | 0.0119 | -27.8245 | -26.4884 | -0.6111 | -0.6110 |
| 0.6853 | 0.59 | 300 | 0.6852 | 0.0012 | -0.0160 | 0.5297 | 0.0172 | -27.8257 | -26.4791 | -0.6112 | -0.6111 |
| 0.6805 | 0.68 | 350 | 0.6877 | -0.0039 | -0.0162 | 0.4725 | 0.0122 | -27.8260 | -26.4893 | -0.6112 | -0.6110 |
| 0.6936 | 0.78 | 400 | 0.6864 | 0.0004 | -0.0144 | 0.4945 | 0.0148 | -27.8226 | -26.4806 | -0.6110 | -0.6109 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.0.0+cu117
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "llama2", "tags": ["trl", "dpo", "generated_from_trainer"], "base_model": "epfl-llm/meditron-7b", "model-index": [{"name": "400STEPS_05beta_1e7rate_Meditron7B", "results": []}]} | text-generation | tsavage68/400STEPS_05beta_1e7rate_Meditron7B_zerozhot | [
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"dpo",
"generated_from_trainer",
"base_model:epfl-llm/meditron-7b",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-11T20:34:55+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #trl #dpo #generated_from_trainer #base_model-epfl-llm/meditron-7b #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| 400STEPS\_05beta\_1e7rate\_Meditron7B
=====================================
This model is a fine-tuned version of epfl-llm/meditron-7b on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6864
* Rewards/chosen: 0.0004
* Rewards/rejected: -0.0144
* Rewards/accuracies: 0.4945
* Rewards/margins: 0.0148
* Logps/rejected: -27.8226
* Logps/chosen: -26.4806
* Logits/rejected: -0.6110
* Logits/chosen: -0.6109
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-07
* train\_batch\_size: 4
* eval\_batch\_size: 1
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 100
* training\_steps: 400
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.0.0+cu117
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.0+cu117\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-07\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 400",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.0+cu117\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
82,
145,
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"passage: TAGS\n#transformers #safetensors #llama #text-generation #trl #dpo #generated_from_trainer #base_model-epfl-llm/meditron-7b #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-07\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 400### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.0+cu117\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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null | null | transformers |
<!-- 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_distilbert_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: 0.2757
- Accuracy: 0.8952
- F1: 0.8952
- Precision: 0.8952
- Recall: 0.8953
## 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 | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.3145 | 1.0 | 1000 | 0.2756 | 0.8842 | 0.8841 | 0.8846 | 0.8839 |
| 0.2429 | 2.0 | 2000 | 0.2531 | 0.8932 | 0.8932 | 0.8933 | 0.8936 |
| 0.203 | 3.0 | 3000 | 0.2757 | 0.8952 | 0.8952 | 0.8952 | 0.8953 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "my_distilbert_model", "results": []}]} | text-classification | kumbi500/my_distilbert_model | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-11T20:38:07+00:00 | [] | [] | TAGS
#transformers #safetensors #distilbert #text-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| my\_distilbert\_model
=====================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2757
* Accuracy: 0.8952
* F1: 0.8952
* Precision: 0.8952
* Recall: 0.8953
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
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #safetensors #distilbert #text-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
68,
98,
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"passage: TAGS\n#transformers #safetensors #distilbert #text-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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null | null | peft |
# 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. -->
- **Developed by:** [Jlonge4]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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<!-- Provide the basic links for the model. -->
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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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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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### 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
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## Training Details
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
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### Testing Data, Factors & Metrics
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#### Metrics
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[More Information Needed]
### Results
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#### Summary
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<!-- Relevant interpretability work for the model goes here -->
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## 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]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
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[More Information Needed]
### Framework versions
- PEFT 0.8.2 | {"language": ["en"], "license": "apache-2.0", "library_name": "peft", "datasets": ["yelp_review_full"], "metrics": ["accuracy"], "base_model": "distilbert-base-uncased"} | null | Jlonge4/distilbert-yelp-review-multiclass | [
"peft",
"safetensors",
"en",
"dataset:yelp_review_full",
"arxiv:1910.09700",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
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#peft #safetensors #en #dataset-yelp_review_full #arxiv-1910.09700 #base_model-distilbert-base-uncased #license-apache-2.0 #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by: [Jlonge4]
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
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null | null | null |
<div align="center">
<img src="data/abs_m_light_mode.svg" alt="abs(m)" width="25%">
</div> | {"license": "gpl-3.0"} | null | WH-KI-KG/abs_m | [
"onnx",
"license:gpl-3.0",
"region:us"
] | 2024-02-11T20:46:02+00:00 | [] | [] | TAGS
#onnx #license-gpl-3.0 #region-us
|
<div align="center">
<img src="data/abs_m_light_mode.svg" alt="abs(m)" width="25%">
</div> | [] | [
"TAGS\n#onnx #license-gpl-3.0 #region-us \n"
] | [
18
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null | null | transformers |
# Model Card for LLaVa-Phi-2-3B-GGUF
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
Quantized version of [llava-phi-2-3b](https://huggingface.co/marianna13/llava-phi-2-3b). Quantization was done using [llama.cpp](https://github.com/ggerganov/llama.cpp/tree/master/examples/llava)
- **Developed by:** [LAION](https://laion.ai/), [SkunkworksAI](https://huggingface.co/SkunkworksAI) & [Ontocord](https://www.ontocord.ai/)
- **Model type:** LLaVA is an open-source chatbot trained by fine-tuning Phi-2 on GPT-generated multimodal instruction-following data.
It is an auto-regressive language model, based on the transformer architecture
- **Finetuned from model:** [Phi-2](https://huggingface.co/microsoft/phi-2)
- **License:** MIT
### Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** [BakLLaVa](https://github.com/SkunkworksAI/BakLLaVA)
- **LLama.cpp:** [GitHub](https://github.com/ggerganov/llama.cpp)
## Usage
```
make & ./llava-cli -m ../ggml-model-f16.gguf --mmproj ../mmproj-model-f16.gguf --image /path/to/image.jpg
```
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Benchmarks
| Model | Parameters |SQA | GQA | TextVQA | POPE |
| --- | --- | --- | --- | --- | --- |
| [LLaVA-1.5](https://huggingface.co/liuhaotian/llava-v1.5-7b) | 7.3B | 68.0| **62.0** | **58.3** | 85.3 |
| [MC-LLaVA-3B](https://huggingface.co/visheratin/MC-LLaVA-3b) | 3B | - | 49.6 | 38.59 | - |
| [LLaVA-Phi](https://arxiv.org/pdf/2401.02330.pdf) | 3B | 68.4 | - | 48.6 | 85.0 |
| [moondream1](https://huggingface.co/vikhyatk/moondream1) | 1.6B | - | 56.3 | 39.8 | - |
| **llava-phi-2-3b** | 3B | **69.0** | 51.2 | 47.0 | **86.0** |
### Image Captioning (MS COCO)
| Model | BLEU_1 | BLEU_2 | BLEU_3 | BLEU_4 | METEOR | ROUGE_L | CIDEr | SPICE |
| -------------------------------------------------------- | ------ | ------ | ------ | ------ | ------ | ------- | ----- | ----- |
| llava-1.5-7b | 75.8 | 59.8 | 45 | 33.3 | 29.4 | 57.7 | 108.8 | 23.5 |
| **llava-phi-2-3b** | 67.7 | 50.5 | 35.7 | 24.2 | 27.0 | 52.4 | 85.0 | 20.7 |
| {"language": ["en"], "license": "mit", "library_name": "transformers", "datasets": ["liuhaotian/LLaVA-Instruct-150K", "liuhaotian/LLaVA-Pretrain"]} | null | marianna13/llava-phi-2-3b-GGUF | [
"transformers",
"gguf",
"en",
"dataset:liuhaotian/LLaVA-Instruct-150K",
"dataset:liuhaotian/LLaVA-Pretrain",
"arxiv:2401.02330",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2024-02-11T20:47:52+00:00 | [
"2401.02330"
] | [
"en"
] | TAGS
#transformers #gguf #en #dataset-liuhaotian/LLaVA-Instruct-150K #dataset-liuhaotian/LLaVA-Pretrain #arxiv-2401.02330 #license-mit #endpoints_compatible #region-us
| Model Card for LLaVa-Phi-2-3B-GGUF
==================================
Model Details
-------------
### Model Description
Quantized version of llava-phi-2-3b. Quantization was done using URL
* Developed by: LAION, SkunkworksAI & Ontocord
* Model type: LLaVA is an open-source chatbot trained by fine-tuning Phi-2 on GPT-generated multimodal instruction-following data.
It is an auto-regressive language model, based on the transformer architecture
* Finetuned from model: Phi-2
* License: MIT
### Model Sources
* Repository: BakLLaVa
* URL: GitHub
Usage
-----
Evaluation
----------
### Benchmarks
### Image Captioning (MS COCO)
| [
"### Model Description\n\n\nQuantized version of llava-phi-2-3b. Quantization was done using URL\n\n\n* Developed by: LAION, SkunkworksAI & Ontocord\n* Model type: LLaVA is an open-source chatbot trained by fine-tuning Phi-2 on GPT-generated multimodal instruction-following data.\nIt is an auto-regressive language model, based on the transformer architecture\n* Finetuned from model: Phi-2\n* License: MIT",
"### Model Sources\n\n\n* Repository: BakLLaVa\n* URL: GitHub\n\n\nUsage\n-----\n\n\nEvaluation\n----------",
"### Benchmarks",
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"### Model Description\n\n\nQuantized version of llava-phi-2-3b. Quantization was done using URL\n\n\n* Developed by: LAION, SkunkworksAI & Ontocord\n* Model type: LLaVA is an open-source chatbot trained by fine-tuning Phi-2 on GPT-generated multimodal instruction-following data.\nIt is an auto-regressive language model, based on the transformer architecture\n* Finetuned from model: Phi-2\n* License: MIT",
"### Model Sources\n\n\n* Repository: BakLLaVa\n* URL: GitHub\n\n\nUsage\n-----\n\n\nEvaluation\n----------",
"### Benchmarks",
"### Image Captioning (MS COCO)"
] | [
69,
109,
28,
5,
11
] | [
"passage: TAGS\n#transformers #gguf #en #dataset-liuhaotian/LLaVA-Instruct-150K #dataset-liuhaotian/LLaVA-Pretrain #arxiv-2401.02330 #license-mit #endpoints_compatible #region-us \n### Model Description\n\n\nQuantized version of llava-phi-2-3b. Quantization was done using URL\n\n\n* Developed by: LAION, SkunkworksAI & Ontocord\n* Model type: LLaVA is an open-source chatbot trained by fine-tuning Phi-2 on GPT-generated multimodal instruction-following data.\nIt is an auto-regressive language model, based on the transformer architecture\n* Finetuned from model: Phi-2\n* License: MIT### Model Sources\n\n\n* Repository: BakLLaVa\n* URL: GitHub\n\n\nUsage\n-----\n\n\nEvaluation\n----------### Benchmarks### Image Captioning (MS COCO)"
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null | null | peft |
# 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. -->
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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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### Framework versions
- PEFT 0.7.2.dev0 | {"library_name": "peft", "base_model": "google/flan-t5-small"} | null | HeydarS/flan-t5-small_peft_v15 | [
"peft",
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"arxiv:1910.09700",
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"1910.09700"
] | [] | TAGS
#peft #safetensors #arxiv-1910.09700 #base_model-google/flan-t5-small #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
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- Model type:
- Language(s) (NLP):
- License:
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### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
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APA:
## Glossary [optional]
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null | null | transformers |
<!-- 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. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0613
- Precision: 0.9367
- Recall: 0.9514
- F1: 0.9440
- Accuracy: 0.9866
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0805 | 1.0 | 1756 | 0.0721 | 0.9109 | 0.9347 | 0.9227 | 0.9806 |
| 0.0412 | 2.0 | 3512 | 0.0596 | 0.9266 | 0.9475 | 0.9369 | 0.9858 |
| 0.0246 | 3.0 | 5268 | 0.0613 | 0.9367 | 0.9514 | 0.9440 | 0.9866 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "bert-base-cased", "model-index": [{"name": "bert-finetuned-ner", "results": []}]} | token-classification | SamBuchl/bert-finetuned-ner | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"base_model:bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-11T20:52:51+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #bert #token-classification #generated_from_trainer #base_model-bert-base-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0613
* Precision: 0.9367
* Recall: 0.9514
* F1: 0.9440
* Accuracy: 0.9866
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: 3
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.1.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #bert #token-classification #generated_from_trainer #base_model-bert-base-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
68,
98,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #bert #token-classification #generated_from_trainer #base_model-bert-base-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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null | null | null |
<div align="center">
<img src="data/abs_z_light_mode.svg" alt="abs(z)" width="25%">
</div> | {"license": "gpl-3.0"} | null | WH-KI-KG/abs_z | [
"onnx",
"license:gpl-3.0",
"region:us"
] | 2024-02-11T20:59:21+00:00 | [] | [] | TAGS
#onnx #license-gpl-3.0 #region-us
|
<div align="center">
<img src="data/abs_z_light_mode.svg" alt="abs(z)" width="25%">
</div> | [] | [
"TAGS\n#onnx #license-gpl-3.0 #region-us \n"
] | [
18
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null | null | transformers |
<!-- 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. -->
# whisper-en-tiny
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3971
- Wer: 92.0761
## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 120
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 1.8836 | 1.0 | 60 | 1.9107 | 98.8906 |
| 1.0203 | 2.0 | 120 | 1.3971 | 92.0761 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "model-index": [{"name": "whisper-en-tiny", "results": []}]} | automatic-speech-recognition | TheAlchemist/whisper-en-tiny | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-11T21:02:36+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| whisper-en-tiny
===============
This model is a fine-tuned version of openai/whisper-tiny on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3971
* Wer: 92.0761
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: 1
* eval\_batch\_size: 1
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 50
* training\_steps: 120
### Training results
### Framework versions
* Transformers 4.29.2
* Pytorch 2.0.1+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 50\n* training\\_steps: 120",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.29.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.12.0\n* Tokenizers 0.13.3"
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 50\n* training\\_steps: 120",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.29.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.12.0\n* Tokenizers 0.13.3"
] | [
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"passage: TAGS\n#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 50\n* training\\_steps: 120### Training results### Framework versions\n\n\n* Transformers 4.29.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.12.0\n* Tokenizers 0.13.3"
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null | null | transformers | # RPMerge
A merge of several Yi 34B models with a singular goal: 40K+ context, instruct-enhanced storytelling.
Disappointed with some quirks of my previous kitchen sink merges (like token/instruct formats from various models showing up when they shouldn't), I've gone 'back to the basics' and picked a few Vicuna-format only models:
- [DrNicefellow/ChatAllInOne-Yi-34B-200K-V1](https://huggingface.co/DrNicefellow/ChatAllInOne-Yi-34B-200K-V1) and [migtissera/Tess-34B-v1.5b](https://huggingface.co/migtissera/Tess-34B-v1.5b) both have excellent general instruction-following performance.
- [cgato/Thespis-34b-v0.7](https://huggingface.co/cgato/Thespis-34b-v0.7) is trained on the "Username: {Input} / BotName: {Response}" format, to emphasize it in the merge (but not force it). It also seems to work for multi-character stories.
- [Doctor-Shotgun/limarpv3-yi-llama-34b-lora](https://huggingface.co/Doctor-Shotgun/limarpv3-yi-llama-34b-lora) is trained on roleplaying data, but merged at a modest weight to not over emphasize it. This is the only non-vicuna model (being alpaca format), but it doesn't seem to interefere with the Vicuna format or adversely affect long-context perplexity
- [adamo1139/yi-34b-200k-rawrr-dpo-2](https://huggingface.co/adamo1139/yi-34b-200k-rawrr-dpo-2) the base for the limarp lora, this is base Yi gently finetuned to discourage refusals.
- [migtissera/Tess-M-Creative-v1.0](https://huggingface.co/migtissera/Tess-M-Creative-v1.0) and [NousResearch/Nous-Capybara-34B](https://huggingface.co/NousResearch/Nous-Capybara-34B) are both "undertrained" Yi models. I find they excel at raw completion performance (like long novel continuations) while still retaining some Vicuna instruct ability. This may be why some still prefer the original Tess 1.0/Capybara merge.
I consider this a more "focused" merge that previous ones. I will investigate other models (perhaps chatML models?) for a more "factual assistant" focused merge, as well as a coding-focused merge if I can't find one to suit my needs.
## Prompt template: Orca-Vicuna
```
SYSTEM: {system_message}
USER: {prompt}
ASSISTANT:
```
Raw prompting as described here is also effective: https://old.reddit.com/r/LocalLLaMA/comments/18zqy4s/the_secret_to_writing_quality_stories_with_llms/
As well as a very explicit system prompt like this: https://old.reddit.com/r/LocalLLaMA/comments/1aiz6zu/roleplaying_system_prompts/koygiwa/
## Running
Chinese models with large tokenizer vocabularies like Yi need *careful* parameter tuning due to their huge logit sampling "tails." Yi in particular also runs relatively "hot" even at lower temperatures.
I am a huge fan of Kalomaze's quadratic sampling (shown as "smoothing factor" where available), as described here: https://github.com/oobabooga/text-generation-webui/pull/5403
Otherwise, I recommend a lower temperature with 0.1 or higher MinP, a little repetition penalty, and mirostat with a low tau, and no other samplers. See the explanation here: https://github.com/ggerganov/llama.cpp/pull/3841
24GB GPUs can efficiently run Yi-34B-200K models at **40K-90K context** with exllamav2, and performant UIs like [exui](https://github.com/turboderp/exui). I go into more detail in this [post](https://old.reddit.com/r/LocalLLaMA/comments/1896igc/how_i_run_34b_models_at_75k_context_on_24gb_fast/). Empty 16GB GPUs can still run the high context with aggressive quantization.
To load/train this in full-context backends like transformers, you *must* change `max_position_embeddings` in config.json to a lower value than 200,000, otherwise you will OOM! I do not recommend running high context without context-efficient backends that support flash attention + 8 bit kv cache, like exllamav2, litellm, vllm or unsloth.
## Testing Notes
Thanks to ParasiticRogue for this idea of a Vicuna-only merge, see: https://huggingface.co/brucethemoose/jondurbin_bagel-dpo-34b-v0.2-exl2-4bpw-fiction/discussions
See: https://huggingface.co/brucethemoose/Yi-34B-200K-DARE-megamerge-v8#testing-notes
This is a possible base for a storytelling finetune/LASER in the future, once I can bite the bullet and rent some A100s or a MI300.
I have tested this merge with with novel-style continuation (but not much chat-style roleplay), and some assistant-style responses and long context analysis. I haven't seen any refusals so far.
## Merge Details
### Merge Method
This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using /home/alpha/Models/Raw/chargoddard_Yi-34B-200K-Llama as a base.
### Models Merged
The following models were included in the merge:
* /home/alpha/Models/Raw/migtissera_Tess-34B-v1.5b
* /home/alpha/Models/Raw/migtissera_Tess-M-Creative-v1.0
* /home/alpha/Models/Raw/cgato_Thespis-34b-DPO-v0.7
* /home/alpha/Models/Raw/Nous-Capybara-34B
* /home/alpha/Models/Raw/admo_limarp
* /home/alpha/Models/Raw/DrNicefellow_ChatAllInOne-Yi-34B-200K-V1
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: /home/alpha/Models/Raw/chargoddard_Yi-34B-200K-Llama
# No parameters necessary for base model
- model: /home/alpha/Models/Raw/migtissera_Tess-34B-v1.5b
#Emphasize the beginning of Vicuna format models
parameters:
weight: 0.19
density: 0.59
- model: /home/alpha/Models/Raw/Nous-Capybara-34B
parameters:
weight: 0.19
density: 0.55
# Vicuna format
- model: /home/alpha/Models/Raw/migtissera_Tess-M-Creative-v1.0
parameters:
weight: 0.05
density: 0.55
- model: /home/alpha/Models/Raw/DrNicefellow_ChatAllInOne-Yi-34B-200K-V1
parameters:
weight: 0.19
density: 0.55
- model: adamo1139/yi-34b-200k-rawrr-dpo-2+Doctor-Shotgun/limarpv3-yi-llama-34b-lora
parameters:
weight: 0.19
density: 0.48
- model: /home/alpha/Models/Raw/cgato_Thespis-34b-DPO-v0.7
parameters:
weight: 0.19
density: 0.59
merge_method: dare_ties
tokenizer_source: union
base_model: /home/alpha/Models/Raw/chargoddard_Yi-34B-200K-Llama
parameters:
int8_mask: true
dtype: bfloat16
```
## Self Promotion
I'm part of a AI startup called Holocene AI!
We're new, busy, and still setting things up. But if you have any business inquiries, want a job, or just want some consultation, feel free to shoot me an email. We have expertise in RAG applications and llama/embeddings model finetuning, and absolutely *none* of the nonsense of scammy AI startups.
Contact me at: [email protected]
I also set up a Ko-Fi! I want to run some (personal) training/LASERing as well, at 100K context or so. If you'd like to buy me 10 minutes on an A100 (or 5 seconds on an MI300X), I'd appreciate it: https://ko-fi.com/alphaatlas
***
Vanilla Quantization by [nold](https://huggingface.co/nold), Original Model [brucethemoose/Yi-34B-200K-RPMerge](https://huggingface.co/brucethemoose/Yi-34B-200K-RPMerge). Created using [llm-quantizer](https://github.com/Nold360/llm-quantizer) Pipeline - 0e95dcd401087b713c2eca7c89ff8108e61969f0
| {"language": ["en"], "license": "other", "library_name": "transformers", "tags": ["mergekit", "merge", "Yi", "exllama", "exllamav2", "exl2"], "license_name": "yi-license", "license_link": "https://huggingface.co/01-ai/Yi-34B/blob/main/LICENSE", "base_model": []} | null | nold/Yi-34B-200K-RPMerge-GGUF | [
"transformers",
"gguf",
"mergekit",
"merge",
"Yi",
"exllama",
"exllamav2",
"exl2",
"en",
"arxiv:2311.03099",
"arxiv:2306.01708",
"license:other",
"endpoints_compatible",
"region:us"
] | 2024-02-11T21:06:10+00:00 | [
"2311.03099",
"2306.01708"
] | [
"en"
] | TAGS
#transformers #gguf #mergekit #merge #Yi #exllama #exllamav2 #exl2 #en #arxiv-2311.03099 #arxiv-2306.01708 #license-other #endpoints_compatible #region-us
| # RPMerge
A merge of several Yi 34B models with a singular goal: 40K+ context, instruct-enhanced storytelling.
Disappointed with some quirks of my previous kitchen sink merges (like token/instruct formats from various models showing up when they shouldn't), I've gone 'back to the basics' and picked a few Vicuna-format only models:
- DrNicefellow/ChatAllInOne-Yi-34B-200K-V1 and migtissera/Tess-34B-v1.5b both have excellent general instruction-following performance.
- cgato/Thespis-34b-v0.7 is trained on the "Username: {Input} / BotName: {Response}" format, to emphasize it in the merge (but not force it). It also seems to work for multi-character stories.
- Doctor-Shotgun/limarpv3-yi-llama-34b-lora is trained on roleplaying data, but merged at a modest weight to not over emphasize it. This is the only non-vicuna model (being alpaca format), but it doesn't seem to interefere with the Vicuna format or adversely affect long-context perplexity
- adamo1139/yi-34b-200k-rawrr-dpo-2 the base for the limarp lora, this is base Yi gently finetuned to discourage refusals.
- migtissera/Tess-M-Creative-v1.0 and NousResearch/Nous-Capybara-34B are both "undertrained" Yi models. I find they excel at raw completion performance (like long novel continuations) while still retaining some Vicuna instruct ability. This may be why some still prefer the original Tess 1.0/Capybara merge.
I consider this a more "focused" merge that previous ones. I will investigate other models (perhaps chatML models?) for a more "factual assistant" focused merge, as well as a coding-focused merge if I can't find one to suit my needs.
## Prompt template: Orca-Vicuna
Raw prompting as described here is also effective: URL
As well as a very explicit system prompt like this: URL
## Running
Chinese models with large tokenizer vocabularies like Yi need *careful* parameter tuning due to their huge logit sampling "tails." Yi in particular also runs relatively "hot" even at lower temperatures.
I am a huge fan of Kalomaze's quadratic sampling (shown as "smoothing factor" where available), as described here: URL
Otherwise, I recommend a lower temperature with 0.1 or higher MinP, a little repetition penalty, and mirostat with a low tau, and no other samplers. See the explanation here: URL
24GB GPUs can efficiently run Yi-34B-200K models at 40K-90K context with exllamav2, and performant UIs like exui. I go into more detail in this post. Empty 16GB GPUs can still run the high context with aggressive quantization.
To load/train this in full-context backends like transformers, you *must* change 'max_position_embeddings' in URL to a lower value than 200,000, otherwise you will OOM! I do not recommend running high context without context-efficient backends that support flash attention + 8 bit kv cache, like exllamav2, litellm, vllm or unsloth.
## Testing Notes
Thanks to ParasiticRogue for this idea of a Vicuna-only merge, see: URL
See: URL
This is a possible base for a storytelling finetune/LASER in the future, once I can bite the bullet and rent some A100s or a MI300.
I have tested this merge with with novel-style continuation (but not much chat-style roleplay), and some assistant-style responses and long context analysis. I haven't seen any refusals so far.
## Merge Details
### Merge Method
This model was merged using the DARE TIES merge method using /home/alpha/Models/Raw/chargoddard_Yi-34B-200K-Llama as a base.
### Models Merged
The following models were included in the merge:
* /home/alpha/Models/Raw/migtissera_Tess-34B-v1.5b
* /home/alpha/Models/Raw/migtissera_Tess-M-Creative-v1.0
* /home/alpha/Models/Raw/cgato_Thespis-34b-DPO-v0.7
* /home/alpha/Models/Raw/Nous-Capybara-34B
* /home/alpha/Models/Raw/admo_limarp
* /home/alpha/Models/Raw/DrNicefellow_ChatAllInOne-Yi-34B-200K-V1
### Configuration
The following YAML configuration was used to produce this model:
## Self Promotion
I'm part of a AI startup called Holocene AI!
We're new, busy, and still setting things up. But if you have any business inquiries, want a job, or just want some consultation, feel free to shoot me an email. We have expertise in RAG applications and llama/embeddings model finetuning, and absolutely *none* of the nonsense of scammy AI startups.
Contact me at: URL@URL
I also set up a Ko-Fi! I want to run some (personal) training/LASERing as well, at 100K context or so. If you'd like to buy me 10 minutes on an A100 (or 5 seconds on an MI300X), I'd appreciate it: URL
*
Vanilla Quantization by nold, Original Model brucethemoose/Yi-34B-200K-RPMerge. Created using llm-quantizer Pipeline - 0e95dcd401087b713c2eca7c89ff8108e61969f0
| [
"# RPMerge\nA merge of several Yi 34B models with a singular goal: 40K+ context, instruct-enhanced storytelling.\n\nDisappointed with some quirks of my previous kitchen sink merges (like token/instruct formats from various models showing up when they shouldn't), I've gone 'back to the basics' and picked a few Vicuna-format only models:\n\n- DrNicefellow/ChatAllInOne-Yi-34B-200K-V1 and migtissera/Tess-34B-v1.5b both have excellent general instruction-following performance.\n\n- cgato/Thespis-34b-v0.7 is trained on the \"Username: {Input} / BotName: {Response}\" format, to emphasize it in the merge (but not force it). It also seems to work for multi-character stories.\n\n- Doctor-Shotgun/limarpv3-yi-llama-34b-lora is trained on roleplaying data, but merged at a modest weight to not over emphasize it. This is the only non-vicuna model (being alpaca format), but it doesn't seem to interefere with the Vicuna format or adversely affect long-context perplexity\n\n- adamo1139/yi-34b-200k-rawrr-dpo-2 the base for the limarp lora, this is base Yi gently finetuned to discourage refusals.\n\n- migtissera/Tess-M-Creative-v1.0 and NousResearch/Nous-Capybara-34B are both \"undertrained\" Yi models. I find they excel at raw completion performance (like long novel continuations) while still retaining some Vicuna instruct ability. This may be why some still prefer the original Tess 1.0/Capybara merge.\n\nI consider this a more \"focused\" merge that previous ones. I will investigate other models (perhaps chatML models?) for a more \"factual assistant\" focused merge, as well as a coding-focused merge if I can't find one to suit my needs.",
"## Prompt template: Orca-Vicuna\n\nRaw prompting as described here is also effective: URL\n\nAs well as a very explicit system prompt like this: URL",
"## Running\n\nChinese models with large tokenizer vocabularies like Yi need *careful* parameter tuning due to their huge logit sampling \"tails.\" Yi in particular also runs relatively \"hot\" even at lower temperatures.\n\nI am a huge fan of Kalomaze's quadratic sampling (shown as \"smoothing factor\" where available), as described here: URL\n\nOtherwise, I recommend a lower temperature with 0.1 or higher MinP, a little repetition penalty, and mirostat with a low tau, and no other samplers. See the explanation here: URL\n\n24GB GPUs can efficiently run Yi-34B-200K models at 40K-90K context with exllamav2, and performant UIs like exui. I go into more detail in this post. Empty 16GB GPUs can still run the high context with aggressive quantization.\n\nTo load/train this in full-context backends like transformers, you *must* change 'max_position_embeddings' in URL to a lower value than 200,000, otherwise you will OOM! I do not recommend running high context without context-efficient backends that support flash attention + 8 bit kv cache, like exllamav2, litellm, vllm or unsloth.",
"## Testing Notes\n\nThanks to ParasiticRogue for this idea of a Vicuna-only merge, see: URL\n\nSee: URL\n\nThis is a possible base for a storytelling finetune/LASER in the future, once I can bite the bullet and rent some A100s or a MI300. \n\nI have tested this merge with with novel-style continuation (but not much chat-style roleplay), and some assistant-style responses and long context analysis. I haven't seen any refusals so far.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the DARE TIES merge method using /home/alpha/Models/Raw/chargoddard_Yi-34B-200K-Llama as a base.",
"### Models Merged\n\nThe following models were included in the merge:\n* /home/alpha/Models/Raw/migtissera_Tess-34B-v1.5b\n* /home/alpha/Models/Raw/migtissera_Tess-M-Creative-v1.0\n* /home/alpha/Models/Raw/cgato_Thespis-34b-DPO-v0.7\n* /home/alpha/Models/Raw/Nous-Capybara-34B\n* /home/alpha/Models/Raw/admo_limarp\n* /home/alpha/Models/Raw/DrNicefellow_ChatAllInOne-Yi-34B-200K-V1",
"### Configuration\n\nThe following YAML configuration was used to produce this model:",
"## Self Promotion\n\nI'm part of a AI startup called Holocene AI!\n\nWe're new, busy, and still setting things up. But if you have any business inquiries, want a job, or just want some consultation, feel free to shoot me an email. We have expertise in RAG applications and llama/embeddings model finetuning, and absolutely *none* of the nonsense of scammy AI startups.\n\nContact me at: URL@URL\n\nI also set up a Ko-Fi! I want to run some (personal) training/LASERing as well, at 100K context or so. If you'd like to buy me 10 minutes on an A100 (or 5 seconds on an MI300X), I'd appreciate it: URL\n\n*\n\nVanilla Quantization by nold, Original Model brucethemoose/Yi-34B-200K-RPMerge. Created using llm-quantizer Pipeline - 0e95dcd401087b713c2eca7c89ff8108e61969f0"
] | [
"TAGS\n#transformers #gguf #mergekit #merge #Yi #exllama #exllamav2 #exl2 #en #arxiv-2311.03099 #arxiv-2306.01708 #license-other #endpoints_compatible #region-us \n",
"# RPMerge\nA merge of several Yi 34B models with a singular goal: 40K+ context, instruct-enhanced storytelling.\n\nDisappointed with some quirks of my previous kitchen sink merges (like token/instruct formats from various models showing up when they shouldn't), I've gone 'back to the basics' and picked a few Vicuna-format only models:\n\n- DrNicefellow/ChatAllInOne-Yi-34B-200K-V1 and migtissera/Tess-34B-v1.5b both have excellent general instruction-following performance.\n\n- cgato/Thespis-34b-v0.7 is trained on the \"Username: {Input} / BotName: {Response}\" format, to emphasize it in the merge (but not force it). It also seems to work for multi-character stories.\n\n- Doctor-Shotgun/limarpv3-yi-llama-34b-lora is trained on roleplaying data, but merged at a modest weight to not over emphasize it. This is the only non-vicuna model (being alpaca format), but it doesn't seem to interefere with the Vicuna format or adversely affect long-context perplexity\n\n- adamo1139/yi-34b-200k-rawrr-dpo-2 the base for the limarp lora, this is base Yi gently finetuned to discourage refusals.\n\n- migtissera/Tess-M-Creative-v1.0 and NousResearch/Nous-Capybara-34B are both \"undertrained\" Yi models. I find they excel at raw completion performance (like long novel continuations) while still retaining some Vicuna instruct ability. This may be why some still prefer the original Tess 1.0/Capybara merge.\n\nI consider this a more \"focused\" merge that previous ones. I will investigate other models (perhaps chatML models?) for a more \"factual assistant\" focused merge, as well as a coding-focused merge if I can't find one to suit my needs.",
"## Prompt template: Orca-Vicuna\n\nRaw prompting as described here is also effective: URL\n\nAs well as a very explicit system prompt like this: URL",
"## Running\n\nChinese models with large tokenizer vocabularies like Yi need *careful* parameter tuning due to their huge logit sampling \"tails.\" Yi in particular also runs relatively \"hot\" even at lower temperatures.\n\nI am a huge fan of Kalomaze's quadratic sampling (shown as \"smoothing factor\" where available), as described here: URL\n\nOtherwise, I recommend a lower temperature with 0.1 or higher MinP, a little repetition penalty, and mirostat with a low tau, and no other samplers. See the explanation here: URL\n\n24GB GPUs can efficiently run Yi-34B-200K models at 40K-90K context with exllamav2, and performant UIs like exui. I go into more detail in this post. Empty 16GB GPUs can still run the high context with aggressive quantization.\n\nTo load/train this in full-context backends like transformers, you *must* change 'max_position_embeddings' in URL to a lower value than 200,000, otherwise you will OOM! I do not recommend running high context without context-efficient backends that support flash attention + 8 bit kv cache, like exllamav2, litellm, vllm or unsloth.",
"## Testing Notes\n\nThanks to ParasiticRogue for this idea of a Vicuna-only merge, see: URL\n\nSee: URL\n\nThis is a possible base for a storytelling finetune/LASER in the future, once I can bite the bullet and rent some A100s or a MI300. \n\nI have tested this merge with with novel-style continuation (but not much chat-style roleplay), and some assistant-style responses and long context analysis. I haven't seen any refusals so far.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the DARE TIES merge method using /home/alpha/Models/Raw/chargoddard_Yi-34B-200K-Llama as a base.",
"### Models Merged\n\nThe following models were included in the merge:\n* /home/alpha/Models/Raw/migtissera_Tess-34B-v1.5b\n* /home/alpha/Models/Raw/migtissera_Tess-M-Creative-v1.0\n* /home/alpha/Models/Raw/cgato_Thespis-34b-DPO-v0.7\n* /home/alpha/Models/Raw/Nous-Capybara-34B\n* /home/alpha/Models/Raw/admo_limarp\n* /home/alpha/Models/Raw/DrNicefellow_ChatAllInOne-Yi-34B-200K-V1",
"### Configuration\n\nThe following YAML configuration was used to produce this model:",
"## Self Promotion\n\nI'm part of a AI startup called Holocene AI!\n\nWe're new, busy, and still setting things up. But if you have any business inquiries, want a job, or just want some consultation, feel free to shoot me an email. We have expertise in RAG applications and llama/embeddings model finetuning, and absolutely *none* of the nonsense of scammy AI startups.\n\nContact me at: URL@URL\n\nI also set up a Ko-Fi! I want to run some (personal) training/LASERing as well, at 100K context or so. If you'd like to buy me 10 minutes on an A100 (or 5 seconds on an MI300X), I'd appreciate it: URL\n\n*\n\nVanilla Quantization by nold, Original Model brucethemoose/Yi-34B-200K-RPMerge. Created using llm-quantizer Pipeline - 0e95dcd401087b713c2eca7c89ff8108e61969f0"
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"passage: TAGS\n#transformers #gguf #mergekit #merge #Yi #exllama #exllamav2 #exl2 #en #arxiv-2311.03099 #arxiv-2306.01708 #license-other #endpoints_compatible #region-us \n",
"passage: # RPMerge\nA merge of several Yi 34B models with a singular goal: 40K+ context, instruct-enhanced storytelling.\n\nDisappointed with some quirks of my previous kitchen sink merges (like token/instruct formats from various models showing up when they shouldn't), I've gone 'back to the basics' and picked a few Vicuna-format only models:\n\n- DrNicefellow/ChatAllInOne-Yi-34B-200K-V1 and migtissera/Tess-34B-v1.5b both have excellent general instruction-following performance.\n\n- cgato/Thespis-34b-v0.7 is trained on the \"Username: {Input} / BotName: {Response}\" format, to emphasize it in the merge (but not force it). It also seems to work for multi-character stories.\n\n- Doctor-Shotgun/limarpv3-yi-llama-34b-lora is trained on roleplaying data, but merged at a modest weight to not over emphasize it. This is the only non-vicuna model (being alpaca format), but it doesn't seem to interefere with the Vicuna format or adversely affect long-context perplexity\n\n- adamo1139/yi-34b-200k-rawrr-dpo-2 the base for the limarp lora, this is base Yi gently finetuned to discourage refusals.\n\n- migtissera/Tess-M-Creative-v1.0 and NousResearch/Nous-Capybara-34B are both \"undertrained\" Yi models. I find they excel at raw completion performance (like long novel continuations) while still retaining some Vicuna instruct ability. This may be why some still prefer the original Tess 1.0/Capybara merge.\n\nI consider this a more \"focused\" merge that previous ones. I will investigate other models (perhaps chatML models?) for a more \"factual assistant\" focused merge, as well as a coding-focused merge if I can't find one to suit my needs.## Prompt template: Orca-Vicuna\n\nRaw prompting as described here is also effective: URL\n\nAs well as a very explicit system prompt like this: URL## Running\n\nChinese models with large tokenizer vocabularies like Yi need *careful* parameter tuning due to their huge logit sampling \"tails.\" Yi in particular also runs relatively \"hot\" even at lower temperatures.\n\nI am a huge fan of Kalomaze's quadratic sampling (shown as \"smoothing factor\" where available), as described here: URL\n\nOtherwise, I recommend a lower temperature with 0.1 or higher MinP, a little repetition penalty, and mirostat with a low tau, and no other samplers. See the explanation here: URL\n\n24GB GPUs can efficiently run Yi-34B-200K models at 40K-90K context with exllamav2, and performant UIs like exui. I go into more detail in this post. Empty 16GB GPUs can still run the high context with aggressive quantization.\n\nTo load/train this in full-context backends like transformers, you *must* change 'max_position_embeddings' in URL to a lower value than 200,000, otherwise you will OOM! I do not recommend running high context without context-efficient backends that support flash attention + 8 bit kv cache, like exllamav2, litellm, vllm or unsloth.## Testing Notes\n\nThanks to ParasiticRogue for this idea of a Vicuna-only merge, see: URL\n\nSee: URL\n\nThis is a possible base for a storytelling finetune/LASER in the future, once I can bite the bullet and rent some A100s or a MI300. \n\nI have tested this merge with with novel-style continuation (but not much chat-style roleplay), and some assistant-style responses and long context analysis. I haven't seen any refusals so far.## Merge Details### Merge Method\n\nThis model was merged using the DARE TIES merge method using /home/alpha/Models/Raw/chargoddard_Yi-34B-200K-Llama as a base."
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | pvkothalkar/whisper-large-v2-kuadult-100steps-12ep | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
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## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
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### Training Procedure
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- Training regime:
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## Evaluation
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
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null | null | transformers |
# Model Card for Model ID
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"transformers",
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"bert",
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# Model Card for Model ID
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## Uses
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### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
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- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
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null | null | diffusers |
# Model Card for Model ID
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| {"library_name": "diffusers"} | null | readingrocket/dllekitt_002 | [
"diffusers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | 2024-02-11T21:18:42+00:00 | [
"1910.09700"
] | [] | TAGS
#diffusers #safetensors #arxiv-1910.09700 #endpoints_compatible #diffusers-StableDiffusionXLPipeline #region-us
|
# Model Card for Model ID
## Model Details
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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.
## Training Details
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a diffusers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#diffusers #safetensors #arxiv-1910.09700 #endpoints_compatible #diffusers-StableDiffusionXLPipeline #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a diffusers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
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"passage: TAGS\n#diffusers #safetensors #arxiv-1910.09700 #endpoints_compatible #diffusers-StableDiffusionXLPipeline #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a diffusers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
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null | null | transformers |
# Bibtex classification using RoBERTa
## Model Description
This model is a text classification tool designed to predict the likelihood of a given context paper being cited by a query paper. It processes concatenated titles of context and query papers and outputs a binary prediction: `1` indicates a potential citation relationship (though not necessary), and `0` suggests no such relationship.
### Intended Use
- **Primary Use**: To extract a subset of bibtex from ACL Anthology to make it < 50 MB.
### Model Training
- **Data Description**: The model was trained on a ACL Anthology dataset [cestwc/anthology](https://huggingface.co/datasets/cestwc/anthology) comprising pairs of paper titles. Each pair was annotated to indicate whether the context paper could potentially be cited by the query paper.
### Performance
- **Metrics**: [Include performance metrics like accuracy, precision, recall, F1-score, etc.]
## How to Use
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model_name = "cestwc/roberta-base-bib"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def predict_citation(context_title, query_title):
inputs = tokenizer.encode_plus(f"{context_title} </s> {query_title}", return_tensors="pt")
outputs = model(**inputs)
prediction = outputs.logits.argmax(-1).item()
return "include" if prediction == 1 else "not include"
# Example
context_title = "Evaluating and Enhancing the Robustness of Neural Network-based Dependency Parsing Models with Adversarial Examples"
query_title = "Assessing Hidden Risks of LLMs: An Empirical Study on Robustness, Consistency, and Credibility"
print(predict_citation(context_title, query_title))
| {"datasets": ["cestwc/anthology"], "metrics": ["accuracy", "f1"], "pipeline_tag": "text-classification", "widget": [{"text": "Evaluating and Enhancing the Robustness of Neural Network-based Dependency Parsing Models with Adversarial Examples </s> Assessing Hidden Risks of LLMs: An Empirical Study on Robustness, Consistency, and Credibility", "example_title": "Example 1"}, {"text": "Incongruent Headlines: Yet Another Way to Mislead Your Readers </s> Emotion Cause Extraction - A Review of Various Methods and Corpora", "example_title": "Example 2"}]} | text-classification | cestwc/roberta-base-bib | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"dataset:cestwc/anthology",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-11T21:20:33+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #dataset-cestwc/anthology #autotrain_compatible #endpoints_compatible #region-us
|
# Bibtex classification using RoBERTa
## Model Description
This model is a text classification tool designed to predict the likelihood of a given context paper being cited by a query paper. It processes concatenated titles of context and query papers and outputs a binary prediction: '1' indicates a potential citation relationship (though not necessary), and '0' suggests no such relationship.
### Intended Use
- Primary Use: To extract a subset of bibtex from ACL Anthology to make it < 50 MB.
### Model Training
- Data Description: The model was trained on a ACL Anthology dataset cestwc/anthology comprising pairs of paper titles. Each pair was annotated to indicate whether the context paper could potentially be cited by the query paper.
### Performance
- Metrics: [Include performance metrics like accuracy, precision, recall, F1-score, etc.]
## How to Use
'''python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model_name = "cestwc/roberta-base-bib"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def predict_citation(context_title, query_title):
inputs = tokenizer.encode_plus(f"{context_title} </s> {query_title}", return_tensors="pt")
outputs = model(inputs)
prediction = URL(-1).item()
return "include" if prediction == 1 else "not include"
# Example
context_title = "Evaluating and Enhancing the Robustness of Neural Network-based Dependency Parsing Models with Adversarial Examples"
query_title = "Assessing Hidden Risks of LLMs: An Empirical Study on Robustness, Consistency, and Credibility"
print(predict_citation(context_title, query_title))
| [
"# Bibtex classification using RoBERTa",
"## Model Description\nThis model is a text classification tool designed to predict the likelihood of a given context paper being cited by a query paper. It processes concatenated titles of context and query papers and outputs a binary prediction: '1' indicates a potential citation relationship (though not necessary), and '0' suggests no such relationship.",
"### Intended Use\n- Primary Use: To extract a subset of bibtex from ACL Anthology to make it < 50 MB.",
"### Model Training\n- Data Description: The model was trained on a ACL Anthology dataset cestwc/anthology comprising pairs of paper titles. Each pair was annotated to indicate whether the context paper could potentially be cited by the query paper.",
"### Performance\n- Metrics: [Include performance metrics like accuracy, precision, recall, F1-score, etc.]",
"## How to Use\n'''python\nfrom transformers import AutoModelForSequenceClassification, AutoTokenizer\n\nmodel_name = \"cestwc/roberta-base-bib\"\ntokenizer = AutoTokenizer.from_pretrained(model_name)\nmodel = AutoModelForSequenceClassification.from_pretrained(model_name)\n\ndef predict_citation(context_title, query_title):\n inputs = tokenizer.encode_plus(f\"{context_title} </s> {query_title}\", return_tensors=\"pt\")\n outputs = model(inputs)\n prediction = URL(-1).item()\n return \"include\" if prediction == 1 else \"not include\"",
"# Example\ncontext_title = \"Evaluating and Enhancing the Robustness of Neural Network-based Dependency Parsing Models with Adversarial Examples\"\nquery_title = \"Assessing Hidden Risks of LLMs: An Empirical Study on Robustness, Consistency, and Credibility\"\nprint(predict_citation(context_title, query_title))"
] | [
"TAGS\n#transformers #pytorch #roberta #text-classification #dataset-cestwc/anthology #autotrain_compatible #endpoints_compatible #region-us \n",
"# Bibtex classification using RoBERTa",
"## Model Description\nThis model is a text classification tool designed to predict the likelihood of a given context paper being cited by a query paper. It processes concatenated titles of context and query papers and outputs a binary prediction: '1' indicates a potential citation relationship (though not necessary), and '0' suggests no such relationship.",
"### Intended Use\n- Primary Use: To extract a subset of bibtex from ACL Anthology to make it < 50 MB.",
"### Model Training\n- Data Description: The model was trained on a ACL Anthology dataset cestwc/anthology comprising pairs of paper titles. Each pair was annotated to indicate whether the context paper could potentially be cited by the query paper.",
"### Performance\n- Metrics: [Include performance metrics like accuracy, precision, recall, F1-score, etc.]",
"## How to Use\n'''python\nfrom transformers import AutoModelForSequenceClassification, AutoTokenizer\n\nmodel_name = \"cestwc/roberta-base-bib\"\ntokenizer = AutoTokenizer.from_pretrained(model_name)\nmodel = AutoModelForSequenceClassification.from_pretrained(model_name)\n\ndef predict_citation(context_title, query_title):\n inputs = tokenizer.encode_plus(f\"{context_title} </s> {query_title}\", return_tensors=\"pt\")\n outputs = model(inputs)\n prediction = URL(-1).item()\n return \"include\" if prediction == 1 else \"not include\"",
"# Example\ncontext_title = \"Evaluating and Enhancing the Robustness of Neural Network-based Dependency Parsing Models with Adversarial Examples\"\nquery_title = \"Assessing Hidden Risks of LLMs: An Empirical Study on Robustness, Consistency, and Credibility\"\nprint(predict_citation(context_title, query_title))"
] | [
48,
10,
82,
32,
61,
33,
169,
95
] | [
"passage: TAGS\n#transformers #pytorch #roberta #text-classification #dataset-cestwc/anthology #autotrain_compatible #endpoints_compatible #region-us \n# Bibtex classification using RoBERTa## Model Description\nThis model is a text classification tool designed to predict the likelihood of a given context paper being cited by a query paper. It processes concatenated titles of context and query papers and outputs a binary prediction: '1' indicates a potential citation relationship (though not necessary), and '0' suggests no such relationship.### Intended Use\n- Primary Use: To extract a subset of bibtex from ACL Anthology to make it < 50 MB.### Model Training\n- Data Description: The model was trained on a ACL Anthology dataset cestwc/anthology comprising pairs of paper titles. Each pair was annotated to indicate whether the context paper could potentially be cited by the query paper.### Performance\n- Metrics: [Include performance metrics like accuracy, precision, recall, F1-score, etc.]## How to Use\n'''python\nfrom transformers import AutoModelForSequenceClassification, AutoTokenizer\n\nmodel_name = \"cestwc/roberta-base-bib\"\ntokenizer = AutoTokenizer.from_pretrained(model_name)\nmodel = AutoModelForSequenceClassification.from_pretrained(model_name)\n\ndef predict_citation(context_title, query_title):\n inputs = tokenizer.encode_plus(f\"{context_title} </s> {query_title}\", return_tensors=\"pt\")\n outputs = model(inputs)\n prediction = URL(-1).item()\n return \"include\" if prediction == 1 else \"not include\""
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null | null | transformers |
Text recognition (ocr) model for [surya](https://github.com/VikParuchuri/surya). See repo for details. | {"license": "cc-by-nc-sa-4.0"} | null | vikp/surya_rec | [
"transformers",
"safetensors",
"vision-encoder-decoder",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] | 2024-02-11T21:20:39+00:00 | [] | [] | TAGS
#transformers #safetensors #vision-encoder-decoder #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us
|
Text recognition (ocr) model for surya. See repo for details. | [] | [
"TAGS\n#transformers #safetensors #vision-encoder-decoder #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us \n"
] | [
43
] | [
"passage: TAGS\n#transformers #safetensors #vision-encoder-decoder #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us \n"
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] |
null | null | null |
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
| {"tags": ["CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "cart_pole_policy_search", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"type": "mean_reward", "value": "500.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | faran332/cart_pole_policy_search | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | 2024-02-11T21:20:59+00:00 | [] | [] | TAGS
#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing CartPole-v1
This is a trained model of a Reinforce agent playing CartPole-v1 .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL
| [
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
"TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
39,
54
] | [
"passage: TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL"
] | [
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | tokoin/mistralai-Code-Instruct | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | 2024-02-11T21:23:14+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
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This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
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### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
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- Cloud Provider:
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- Carbon Emitted:
## Technical Specifications [optional]
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null | null | transformers | <img src="https://huggingface.co/Mabeck/Heidrun-Mistral-7B-chat/resolve/main/heidrun.jpeg" alt="Heidrun Logo" width="400">
# Model description
Heidrun-Mistral-7B-base is a generative text model based on [Mistral-7B](https://huggingface.co/mistralai/Mistral-7B-v0.1). It has been further pretrained on a subset of the Danish corpus from [Oscar](https://huggingface.co/datasets/oscar).
The dataset was first cleaned to remove most inappropriate texts. Please note that Oscar contains explicit content, and not all may have been removed.
The model was mainly trained on shorter sentences (<3000 words).
For inference or chatting please check out [Heidrun-Mistral-7B-chat](https://huggingface.co/Mabeck/Heidrun-Mistral-7B-chat).
# Uploaded model
- **Developed by:** Mabeck
- **Finetuned from model :** mistralai/Mistral-7B-v0.1
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) | {"language": ["en", "da"], "license": "mit", "tags": ["text-generation-inference", "transformers", "unsloth", "mistral", "trl"], "datasets": ["oscar"], "base_model": "mistralai/Mistral-7B-v0.1"} | text-generation | Mabeck/Heidrun-Mistral-7B-base | [
"transformers",
"pytorch",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"en",
"da",
"dataset:oscar",
"base_model:mistralai/Mistral-7B-v0.1",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-11T21:27:03+00:00 | [] | [
"en",
"da"
] | TAGS
#transformers #pytorch #safetensors #mistral #text-generation #text-generation-inference #unsloth #trl #en #da #dataset-oscar #base_model-mistralai/Mistral-7B-v0.1 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| <img src="URL alt="Heidrun Logo" width="400">
# Model description
Heidrun-Mistral-7B-base is a generative text model based on Mistral-7B. It has been further pretrained on a subset of the Danish corpus from Oscar.
The dataset was first cleaned to remove most inappropriate texts. Please note that Oscar contains explicit content, and not all may have been removed.
The model was mainly trained on shorter sentences (<3000 words).
For inference or chatting please check out Heidrun-Mistral-7B-chat.
# Uploaded model
- Developed by: Mabeck
- Finetuned from model : mistralai/Mistral-7B-v0.1
This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.
<img src="URL width="200"/> | [
"# Model description\n\nHeidrun-Mistral-7B-base is a generative text model based on Mistral-7B. It has been further pretrained on a subset of the Danish corpus from Oscar.\n\nThe dataset was first cleaned to remove most inappropriate texts. Please note that Oscar contains explicit content, and not all may have been removed.\nThe model was mainly trained on shorter sentences (<3000 words).\n\nFor inference or chatting please check out Heidrun-Mistral-7B-chat.",
"# Uploaded model\n\n- Developed by: Mabeck\n- Finetuned from model : mistralai/Mistral-7B-v0.1\n\nThis mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] | [
"TAGS\n#transformers #pytorch #safetensors #mistral #text-generation #text-generation-inference #unsloth #trl #en #da #dataset-oscar #base_model-mistralai/Mistral-7B-v0.1 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model description\n\nHeidrun-Mistral-7B-base is a generative text model based on Mistral-7B. It has been further pretrained on a subset of the Danish corpus from Oscar.\n\nThe dataset was first cleaned to remove most inappropriate texts. Please note that Oscar contains explicit content, and not all may have been removed.\nThe model was mainly trained on shorter sentences (<3000 words).\n\nFor inference or chatting please check out Heidrun-Mistral-7B-chat.",
"# Uploaded model\n\n- Developed by: Mabeck\n- Finetuned from model : mistralai/Mistral-7B-v0.1\n\nThis mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] | [
89,
114,
69
] | [
"passage: TAGS\n#transformers #pytorch #safetensors #mistral #text-generation #text-generation-inference #unsloth #trl #en #da #dataset-oscar #base_model-mistralai/Mistral-7B-v0.1 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n# Model description\n\nHeidrun-Mistral-7B-base is a generative text model based on Mistral-7B. It has been further pretrained on a subset of the Danish corpus from Oscar.\n\nThe dataset was first cleaned to remove most inappropriate texts. Please note that Oscar contains explicit content, and not all may have been removed.\nThe model was mainly trained on shorter sentences (<3000 words).\n\nFor inference or chatting please check out Heidrun-Mistral-7B-chat.# Uploaded model\n\n- Developed by: Mabeck\n- Finetuned from model : mistralai/Mistral-7B-v0.1\n\nThis mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
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null | null | transformers |
<!-- 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. -->
# 500STEPS_1e5rate_Mistral_SFT
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3056
## 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: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.4023 | 0.1 | 50 | 0.3896 |
| 0.4307 | 0.2 | 100 | 0.5254 |
| 0.4229 | 0.29 | 150 | 0.3821 |
| 0.3898 | 0.39 | 200 | 0.4135 |
| 0.368 | 0.49 | 250 | 0.3505 |
| 0.3431 | 0.59 | 300 | 0.3352 |
| 0.3225 | 0.68 | 350 | 0.3216 |
| 0.3085 | 0.78 | 400 | 0.3110 |
| 0.2743 | 0.88 | 450 | 0.3061 |
| 0.3065 | 0.98 | 500 | 0.3056 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.0.0+cu117
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "mistralai/Mistral-7B-v0.1", "model-index": [{"name": "500STEPS_1e5rate_Mistral_SFT", "results": []}]} | text-generation | tsavage68/500STEPS_1e5rate_Mistral_SFT_zeroshot | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"trl",
"sft",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-11T21:30:46+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #trl #sft #generated_from_trainer #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| 500STEPS\_1e5rate\_Mistral\_SFT
===============================
This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3056
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: 4
* eval\_batch\_size: 1
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 100
* training\_steps: 500
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.0.0+cu117
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 500",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.0+cu117\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #trl #sft #generated_from_trainer #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 500",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.0+cu117\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
84,
144,
4,
33
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #trl #sft #generated_from_trainer #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 500### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.0+cu117\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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null | null | transformers |
<!-- 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. -->
# 400STEPS_1e7rate_01beta_T5
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6483
- Rewards/chosen: -0.0026
- Rewards/rejected: -0.1019
- Rewards/accuracies: 0.6593
- Rewards/margins: 0.0994
- Logps/rejected: -15.7387
- Logps/chosen: -12.9908
- Logits/rejected: -3.1652
- Logits/chosen: -3.1650
## 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-07
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 400
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.6916 | 0.1 | 50 | 0.6908 | 0.0048 | 0.0002 | 0.5670 | 0.0047 | -14.7176 | -12.9168 | -3.1591 | -3.1588 |
| 0.6821 | 0.2 | 100 | 0.6764 | 0.0187 | -0.0159 | 0.6681 | 0.0346 | -14.8782 | -12.7778 | -3.1625 | -3.1622 |
| 0.6647 | 0.29 | 150 | 0.6629 | 0.0225 | -0.0422 | 0.6659 | 0.0648 | -15.1414 | -12.7399 | -3.1625 | -3.1623 |
| 0.6536 | 0.39 | 200 | 0.6552 | 0.0148 | -0.0679 | 0.6505 | 0.0827 | -15.3987 | -12.8175 | -3.1657 | -3.1654 |
| 0.6354 | 0.49 | 250 | 0.6509 | 0.0022 | -0.0909 | 0.6593 | 0.0931 | -15.6282 | -12.9431 | -3.1646 | -3.1643 |
| 0.6468 | 0.59 | 300 | 0.6484 | -0.0022 | -0.1013 | 0.6527 | 0.0991 | -15.7319 | -12.9869 | -3.1653 | -3.1650 |
| 0.6549 | 0.68 | 350 | 0.6481 | -0.0021 | -0.1019 | 0.6571 | 0.0998 | -15.7386 | -12.9865 | -3.1652 | -3.1650 |
| 0.6684 | 0.78 | 400 | 0.6483 | -0.0026 | -0.1019 | 0.6593 | 0.0994 | -15.7387 | -12.9908 | -3.1652 | -3.1650 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.0.0+cu117
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["trl", "dpo", "generated_from_trainer"], "base_model": "mistralai/Mistral-7B-v0.1", "model-index": [{"name": "400STEPS_1e7rate_01beta_T5", "results": []}]} | text-generation | tsavage68/400STEPS_1e7rate_01beta_Mistral | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"trl",
"dpo",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-11T21:41:04+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #trl #dpo #generated_from_trainer #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| 400STEPS\_1e7rate\_01beta\_T5
=============================
This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6483
* Rewards/chosen: -0.0026
* Rewards/rejected: -0.1019
* Rewards/accuracies: 0.6593
* Rewards/margins: 0.0994
* Logps/rejected: -15.7387
* Logps/chosen: -12.9908
* Logits/rejected: -3.1652
* Logits/chosen: -3.1650
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-07
* train\_batch\_size: 4
* eval\_batch\_size: 1
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 100
* training\_steps: 400
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.0.0+cu117
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.0+cu117\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-07\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 400",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.0+cu117\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
84,
145,
4,
33
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"passage: TAGS\n#transformers #safetensors #mistral #text-generation #trl #dpo #generated_from_trainer #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-07\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 400### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.0+cu117\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | aidonuts/catacombs-001 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-11T21:42:47+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
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## Uses
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### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
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- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
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#### Hardware
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[optional]
BibTeX:
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## Glossary [optional]
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## Model Card Contact
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"passage: TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | tomaszki/nous-twenty-six | [
"transformers",
"safetensors",
"llama",
"text-generation",
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"arxiv:1910.09700",
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] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
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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:
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## Uses
### Direct Use
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### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
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## Technical Specifications [optional]
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### Compute Infrastructure
#### Hardware
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BibTeX:
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## Glossary [optional]
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## Model Card Contact
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] |
null | null | null | GGUF importance matrix (imatrix) quants for https://huggingface.co/abacusai/Smaug-72B-v0.1
The importance matrix was trained for 100K tokens (200 batches of 512 tokens) using wiki.train.raw.
Llama-2 conversation template and system prompt set to the [Qwen system prompt](https://github.com/QwenLM/Qwen/blob/main/examples/system_prompt.md).
| Layers | Context | Template |
| --- | --- | --- |
| <pre>80</pre> | <pre>32768</pre> | <pre>[INST] \<\<SYS\>\><br>{instructions}<br>\<\</SYS\>\><br><br>{prompt} [/INST]<br>{response}</pre> | | {"license": "other", "license_name": "tongyi-qianwen-license-agreement", "license_link": "https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT", "pipeline_tag": "text-generation"} | text-generation | dranger003/Smaug-72B-v0.1-iMat.GGUF | [
"gguf",
"text-generation",
"license:other",
"region:us"
] | 2024-02-11T21:58:33+00:00 | [] | [] | TAGS
#gguf #text-generation #license-other #region-us
| GGUF importance matrix (imatrix) quants for URL
The importance matrix was trained for 100K tokens (200 batches of 512 tokens) using URL.
Llama-2 conversation template and system prompt set to the Qwen system prompt.
Layers:
```
80
```
, Context:
```
32768
```
, Template:
```
[INST] <<SYS>>
{instructions}
<</SYS>>
{prompt} [/INST]
{response}
```
| [] | [
"TAGS\n#gguf #text-generation #license-other #region-us \n"
] | [
19
] | [
"passage: TAGS\n#gguf #text-generation #license-other #region-us \n"
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null | null | transformers |
# Model Card for Model ID
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[More Information Needed] | {"license": "apache-2.0", "library_name": "transformers"} | text-generation | yam-peleg/Experiment9-7B | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"license:apache-2.0",
"autotrain_compatible",
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"text-generation-inference",
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"1910.09700"
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#transformers #safetensors #mistral #text-generation #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
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## Uses
### Direct Use
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### Out-of-Scope Use
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### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
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## Technical Specifications [optional]
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### Compute Infrastructure
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[optional]
BibTeX:
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## Model Card Contact
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null | null | diffusers | This is a Microsoft Olive optimized ONNX version of the model found here: https://huggingface.co/stablediffusionapi/juggernaut-xl-v8 | {"library_name": "diffusers", "tags": ["unpaint", "stable_diffusion_model", "stable-diffusion", "onnx"], "pipeline_tag": "text-to-image", "model_description": [{"repo": "stablediffusionapi/juggernaut-xl-v8"}]} | text-to-image | axodoxian/juggernaut_xl_onnx | [
"diffusers",
"onnx",
"unpaint",
"stable_diffusion_model",
"stable-diffusion",
"text-to-image",
"diffusers:ORTStableDiffusionXLPipeline",
"region:us"
] | 2024-02-11T22:04:13+00:00 | [] | [] | TAGS
#diffusers #onnx #unpaint #stable_diffusion_model #stable-diffusion #text-to-image #diffusers-ORTStableDiffusionXLPipeline #region-us
| This is a Microsoft Olive optimized ONNX version of the model found here: URL | [] | [
"TAGS\n#diffusers #onnx #unpaint #stable_diffusion_model #stable-diffusion #text-to-image #diffusers-ORTStableDiffusionXLPipeline #region-us \n"
] | [
55
] | [
"passage: TAGS\n#diffusers #onnx #unpaint #stable_diffusion_model #stable-diffusion #text-to-image #diffusers-ORTStableDiffusionXLPipeline #region-us \n"
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null | null | transformers |
<!-- 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. -->
# hw1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) 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: 2e-05
- train_batch_size: 45
- eval_batch_size: 45
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "hw1", "results": []}]} | text-classification | mudit1903/hw1 | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-11T22:08:25+00:00 | [] | [] | TAGS
#transformers #safetensors #distilbert #text-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# hw1
This model is a fine-tuned version of distilbert-base-uncased 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: 2e-05
- train_batch_size: 45
- eval_batch_size: 45
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| [
"# hw1\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 45\n- eval_batch_size: 45\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #safetensors #distilbert #text-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# hw1\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 45\n- eval_batch_size: 45\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
] | [
68,
31,
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90,
33
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"passage: TAGS\n#transformers #safetensors #distilbert #text-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# hw1\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 45\n- eval_batch_size: 45\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
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] |
null | null | stable-baselines3 |
# **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
...
```
| {"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": "262.83 +/- 16.22", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | jainamk/LunarLander-v2 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-11T22:09:12+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
39,
41,
17
] | [
"passage: TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
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null | null | null |
<!-- 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. -->
# esm2_t12_35M_qlora_glycosylation_sites_2024-02-11_22-11-09
This model is a fine-tuned version of [facebook/esm2_t12_35M_UR50D](https://huggingface.co/facebook/esm2_t12_35M_UR50D) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1117
- Accuracy: 0.9968
- Precision: 0.4831
- Recall: 0.9671
- F1: 0.6443
- Auc: 0.9820
- Mcc: 0.6823
## 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: 0.0003701568055793089
- train_batch_size: 36
- eval_batch_size: 36
- seed: 8893
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Auc | Mcc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|:------:|
| 0.1789 | 1.0 | 295 | 0.1102 | 0.9962 | 0.4391 | 0.9638 | 0.6034 | 0.9801 | 0.6492 |
| 0.0145 | 2.0 | 590 | 0.1105 | 0.9967 | 0.4776 | 0.9663 | 0.6393 | 0.9816 | 0.6782 |
| 0.0115 | 3.0 | 885 | 0.1117 | 0.9968 | 0.4831 | 0.9671 | 0.6443 | 0.9820 | 0.6823 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "base_model": "facebook/esm2_t12_35M_UR50D", "model-index": [{"name": "esm2_t12_35M_qlora_glycosylation_sites_2024-02-11_22-11-09", "results": []}]} | null | nidhinthomas/esm2_t12_35M_qlora_glycosylation_sites | [
"safetensors",
"generated_from_trainer",
"base_model:facebook/esm2_t12_35M_UR50D",
"license:mit",
"region:us"
] | 2024-02-11T22:11:09+00:00 | [] | [] | TAGS
#safetensors #generated_from_trainer #base_model-facebook/esm2_t12_35M_UR50D #license-mit #region-us
| esm2\_t12\_35M\_qlora\_glycosylation\_sites\_2024-02-11\_22-11-09
=================================================================
This model is a fine-tuned version of facebook/esm2\_t12\_35M\_UR50D on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1117
* Accuracy: 0.9968
* Precision: 0.4831
* Recall: 0.9671
* F1: 0.6443
* Auc: 0.9820
* Mcc: 0.6823
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: 0.0003701568055793089
* train\_batch\_size: 36
* eval\_batch\_size: 36
* seed: 8893
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* num\_epochs: 3
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003701568055793089\n* train\\_batch\\_size: 36\n* eval\\_batch\\_size: 36\n* seed: 8893\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
"TAGS\n#safetensors #generated_from_trainer #base_model-facebook/esm2_t12_35M_UR50D #license-mit #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003701568055793089\n* train\\_batch\\_size: 36\n* eval\\_batch\\_size: 36\n* seed: 8893\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
43,
120,
4,
33
] | [
"passage: TAGS\n#safetensors #generated_from_trainer #base_model-facebook/esm2_t12_35M_UR50D #license-mit #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003701568055793089\n* train\\_batch\\_size: 36\n* eval\\_batch\\_size: 36\n* seed: 8893\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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null | null | null |
# **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="AstridsN/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"])
```
| {"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}]}]}]} | reinforcement-learning | AstridsN/q-FrozenLake-v1-4x4-noSlippery | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2024-02-11T22:12:49+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing1 FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage"
] | [
40,
39
] | [
"passage: TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage"
] | [
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null | null | transformers |
<!-- 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. -->
# wav2vec2-small-xls-r-nhi-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_16_1 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: 0.0003
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice_16_1"], "base_model": "facebook/wav2vec2-xls-r-300m", "model-index": [{"name": "wav2vec2-small-xls-r-nhi-colab", "results": []}]} | automatic-speech-recognition | plesniar/wav2vec2-small-xls-r-nhi-colab | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice_16_1",
"base_model:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-11T22:19:26+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_16_1 #base_model-facebook/wav2vec2-xls-r-300m #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-small-xls-r-nhi-colab
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice_16_1 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: 0.0003
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| [
"# wav2vec2-small-xls-r-nhi-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice_16_1 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0003\n- train_batch_size: 4\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 16\n- total_train_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 30\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_16_1 #base_model-facebook/wav2vec2-xls-r-300m #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-small-xls-r-nhi-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice_16_1 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0003\n- train_batch_size: 4\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 16\n- total_train_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 30\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
] | [
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"passage: TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_16_1 #base_model-facebook/wav2vec2-xls-r-300m #license-apache-2.0 #endpoints_compatible #region-us \n# wav2vec2-small-xls-r-nhi-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice_16_1 dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0003\n- train_batch_size: 4\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 16\n- total_train_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 30\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
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] |
null | null | null |
# **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="AstridsN/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"])
```
| {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "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}]}]}]} | reinforcement-learning | AstridsN/Taxi-v3 | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2024-02-11T22:19:30+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing1 Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage"
] | [
32,
33
] | [
"passage: TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage"
] | [
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null | null | peft |
<!-- 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. -->
# dolphin-yoffa-7b
This model is a fine-tuned version of [cognitivecomputations/dolphin-2.6-mistral-7b](https://huggingface.co/cognitivecomputations/dolphin-2.6-mistral-7b) on the generator 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: 3
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 6
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1 | {"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "cognitivecomputations/dolphin-2.6-mistral-7b", "model-index": [{"name": "dolphin-yoffa-7b", "results": []}]} | null | theyoffa/dolphin-yoffa-7b | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:cognitivecomputations/dolphin-2.6-mistral-7b",
"license:apache-2.0",
"region:us"
] | 2024-02-11T22:23:47+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-cognitivecomputations/dolphin-2.6-mistral-7b #license-apache-2.0 #region-us
|
# dolphin-yoffa-7b
This model is a fine-tuned version of cognitivecomputations/dolphin-2.6-mistral-7b on the generator 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: 3
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 6
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1 | [
"# dolphin-yoffa-7b\n\nThis model is a fine-tuned version of cognitivecomputations/dolphin-2.6-mistral-7b on the generator dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 3\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 6\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: constant\n- lr_scheduler_warmup_ratio: 0.03\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
] | [
"TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-cognitivecomputations/dolphin-2.6-mistral-7b #license-apache-2.0 #region-us \n",
"# dolphin-yoffa-7b\n\nThis model is a fine-tuned version of cognitivecomputations/dolphin-2.6-mistral-7b on the generator dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 3\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 6\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: constant\n- lr_scheduler_warmup_ratio: 0.03\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
] | [
66,
40,
6,
12,
8,
3,
128,
4,
39
] | [
"passage: TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-cognitivecomputations/dolphin-2.6-mistral-7b #license-apache-2.0 #region-us \n# dolphin-yoffa-7b\n\nThis model is a fine-tuned version of cognitivecomputations/dolphin-2.6-mistral-7b on the generator dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 3\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 6\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: constant\n- lr_scheduler_warmup_ratio: 0.03\n- num_epochs: 3### Training results### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
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null | null | transformers |
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| {"library_name": "transformers", "tags": []} | null | elucidator8918/apigen-prototype-0.2 | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | 2024-02-11T22:26:28+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
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:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
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"passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | djomo/MISTRALllux2000-7b-v7 | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #mistral #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
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- Developed by:
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## Uses
### Direct Use
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### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
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## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
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APA:
## Glossary [optional]
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null | null | null | # torchtune research repo: token coloring (colorful llama)
Playground to try out [token coloring](https://docs.google.com/document/d/1Win9vhddD-pu5P3SsG7E-dzN5oQl5DYWW1DhO7sBOgI/edit#heading=h.oqq00pt8expe) with TorchTune.
The repo was generated using the alpha version of [torchtune](https://github.com/pytorch-labs/torchtune).
Brief notes:
- The starting recipe is based on the Alpaca Llama2 7B full finetune recipe (switched to bf16).
- I copied a lot of functionality (like the actual model definition, dataset, etc) from torchtune repository directly since I needed to make changes.
- I reduced the flexiblity of the recipe (e.g. cannot specify the model or tokenizer) and increased it in other ways (e.g. can pass in a dataset path directly).
- I added intermediate checkpointing (i.e. every `n` steps) and automatically upload the checkpoint to HuggingFace Hub.
- Assumes `output/` is used to store model outputs and `model/` is used to store the base model checkpoints.
## Getting started
The below instructions can be copy-pasted as is on to a running instance. They assume that the `HF_TOKEN` environment variable is set with a valid token.
```bash
# for RunPod
cd /workspace
git clone [email protected]:pytorch-labs/torchtune.git
cd torchtune
pip install -e .
cd /workspace
git clone [email protected]:laurencer/torchtune-colorful-llama.git
cd torchtune-colorful-llama
# for wandb support
pip install wandb
```
```bash
mkdir -p model/
tune download --repo-id meta-llama/Llama-2-7b --output-dir model/
```
```bash
tune convert_checkpoint --checkpoint-path model/consolidated.00.pth --output-path model/llama2_native.tune
```
```bash
mkdir -p output/
# tune --nnodes 1 --nproc_per_node 1 ./full_finetune.py --config basic_config.yaml
nohup tune --nnodes 1 --nproc_per_node 1 ./full_finetune.py --config basic_config.yaml 2>&1 > training_log_$(date "+%Y.%m.%d_%H.%M.%S").log &
sleep 1
tail -f training_log_*.log
``` | {} | null | laurencer/Llama7b-Alpaca-Tune-4epochs-WithReplacementColoring-partial | [
"region:us"
] | 2024-02-11T22:27:36+00:00 | [] | [] | TAGS
#region-us
| # torchtune research repo: token coloring (colorful llama)
Playground to try out token coloring with TorchTune.
The repo was generated using the alpha version of torchtune.
Brief notes:
- The starting recipe is based on the Alpaca Llama2 7B full finetune recipe (switched to bf16).
- I copied a lot of functionality (like the actual model definition, dataset, etc) from torchtune repository directly since I needed to make changes.
- I reduced the flexiblity of the recipe (e.g. cannot specify the model or tokenizer) and increased it in other ways (e.g. can pass in a dataset path directly).
- I added intermediate checkpointing (i.e. every 'n' steps) and automatically upload the checkpoint to HuggingFace Hub.
- Assumes 'output/' is used to store model outputs and 'model/' is used to store the base model checkpoints.
## Getting started
The below instructions can be copy-pasted as is on to a running instance. They assume that the 'HF_TOKEN' environment variable is set with a valid token.
| [
"# torchtune research repo: token coloring (colorful llama)\n\nPlayground to try out token coloring with TorchTune.\n\nThe repo was generated using the alpha version of torchtune.\n\nBrief notes:\n\n- The starting recipe is based on the Alpaca Llama2 7B full finetune recipe (switched to bf16).\n- I copied a lot of functionality (like the actual model definition, dataset, etc) from torchtune repository directly since I needed to make changes.\n- I reduced the flexiblity of the recipe (e.g. cannot specify the model or tokenizer) and increased it in other ways (e.g. can pass in a dataset path directly).\n- I added intermediate checkpointing (i.e. every 'n' steps) and automatically upload the checkpoint to HuggingFace Hub.\n- Assumes 'output/' is used to store model outputs and 'model/' is used to store the base model checkpoints.",
"## Getting started\n\nThe below instructions can be copy-pasted as is on to a running instance. They assume that the 'HF_TOKEN' environment variable is set with a valid token."
] | [
"TAGS\n#region-us \n",
"# torchtune research repo: token coloring (colorful llama)\n\nPlayground to try out token coloring with TorchTune.\n\nThe repo was generated using the alpha version of torchtune.\n\nBrief notes:\n\n- The starting recipe is based on the Alpaca Llama2 7B full finetune recipe (switched to bf16).\n- I copied a lot of functionality (like the actual model definition, dataset, etc) from torchtune repository directly since I needed to make changes.\n- I reduced the flexiblity of the recipe (e.g. cannot specify the model or tokenizer) and increased it in other ways (e.g. can pass in a dataset path directly).\n- I added intermediate checkpointing (i.e. every 'n' steps) and automatically upload the checkpoint to HuggingFace Hub.\n- Assumes 'output/' is used to store model outputs and 'model/' is used to store the base model checkpoints.",
"## Getting started\n\nThe below instructions can be copy-pasted as is on to a running instance. They assume that the 'HF_TOKEN' environment variable is set with a valid token."
] | [
6,
225,
40
] | [
"passage: TAGS\n#region-us \n# torchtune research repo: token coloring (colorful llama)\n\nPlayground to try out token coloring with TorchTune.\n\nThe repo was generated using the alpha version of torchtune.\n\nBrief notes:\n\n- The starting recipe is based on the Alpaca Llama2 7B full finetune recipe (switched to bf16).\n- I copied a lot of functionality (like the actual model definition, dataset, etc) from torchtune repository directly since I needed to make changes.\n- I reduced the flexiblity of the recipe (e.g. cannot specify the model or tokenizer) and increased it in other ways (e.g. can pass in a dataset path directly).\n- I added intermediate checkpointing (i.e. every 'n' steps) and automatically upload the checkpoint to HuggingFace Hub.\n- Assumes 'output/' is used to store model outputs and 'model/' is used to store the base model checkpoints.## Getting started\n\nThe below instructions can be copy-pasted as is on to a running instance. They assume that the 'HF_TOKEN' environment variable is set with a valid token."
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null | null | transformers | This is just the encoder from [google/t5-v1_1-xl](https://huggingface.co/google/t5-v1_1-xl) in fp16 format. | {"license": "apache-2.0"} | null | ostris/t5-v1_1-xl | [
"transformers",
"safetensors",
"t5",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-11T22:30:42+00:00 | [] | [] | TAGS
#transformers #safetensors #t5 #license-apache-2.0 #endpoints_compatible #text-generation-inference #region-us
| This is just the encoder from google/t5-v1_1-xl in fp16 format. | [] | [
"TAGS\n#transformers #safetensors #t5 #license-apache-2.0 #endpoints_compatible #text-generation-inference #region-us \n"
] | [
42
] | [
"passage: TAGS\n#transformers #safetensors #t5 #license-apache-2.0 #endpoints_compatible #text-generation-inference #region-us \n"
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null | null | transformers |
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| {"library_name": "transformers", "tags": []} | null | DevanshSinha/mistral-7b-newsqa-bits1 | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | 2024-02-11T22:34:29+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
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:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
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"passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
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null | null | null |
<!-- 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. -->
# llama-2-7b-english-riddles-espanol-reasoning-v1
This model is a fine-tuned version of [NousResearch/llama-2-7b-chat-hf](https://huggingface.co/NousResearch/llama-2-7b-chat-hf) 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: 0.0001
- train_batch_size: 6
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 5
### Framework versions
- Transformers 4.31.0
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.13.3
| {"tags": ["generated_from_trainer"], "base_model": "NousResearch/llama-2-7b-chat-hf", "model-index": [{"name": "llama-2-7b-english-riddles-espanol-reasoning-v1", "results": []}]} | null | DrishtiSharma/llama-2-7b-english-riddles-espanol-reasoning-v1 | [
"safetensors",
"generated_from_trainer",
"base_model:NousResearch/llama-2-7b-chat-hf",
"region:us"
] | 2024-02-11T22:36:15+00:00 | [] | [] | TAGS
#safetensors #generated_from_trainer #base_model-NousResearch/llama-2-7b-chat-hf #region-us
|
# llama-2-7b-english-riddles-espanol-reasoning-v1
This model is a fine-tuned version of NousResearch/llama-2-7b-chat-hf 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: 0.0001
- train_batch_size: 6
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 5
### Framework versions
- Transformers 4.31.0
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.13.3
| [
"# llama-2-7b-english-riddles-espanol-reasoning-v1\n\nThis model is a fine-tuned version of NousResearch/llama-2-7b-chat-hf on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0001\n- train_batch_size: 6\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- num_epochs: 5",
"### Framework versions\n\n- Transformers 4.31.0\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.13.3"
] | [
"TAGS\n#safetensors #generated_from_trainer #base_model-NousResearch/llama-2-7b-chat-hf #region-us \n",
"# llama-2-7b-english-riddles-espanol-reasoning-v1\n\nThis model is a fine-tuned version of NousResearch/llama-2-7b-chat-hf on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0001\n- train_batch_size: 6\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- num_epochs: 5",
"### Framework versions\n\n- Transformers 4.31.0\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.13.3"
] | [
38,
55,
6,
12,
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3,
90,
33
] | [
"passage: TAGS\n#safetensors #generated_from_trainer #base_model-NousResearch/llama-2-7b-chat-hf #region-us \n# llama-2-7b-english-riddles-espanol-reasoning-v1\n\nThis model is a fine-tuned version of NousResearch/llama-2-7b-chat-hf on an unknown dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0001\n- train_batch_size: 6\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- num_epochs: 5### Framework versions\n\n- Transformers 4.31.0\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.13.3"
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] |
null | null | transformers |
MambaSan-370m-instruct 🐍
MambaSan-instruct is the first chat Japanese language model based on a state-space model architecture (Mamba).
The model is based on Albert Gu's and Tri Dao's work Mamba: Linear-Time Sequence Modeling with Selective State Spaces (paper) as well as their model implementation. This work was also inspired by heavenq's mamba-chat implementation in English.
Mamba-Chat is based on MambaSan-370m and was fine-tuned on 31,7k examples samples of the SkelterLabsInc/JaQuAD dataset. To learn more, you can:
- Take a look at the model on [Huggingface](https://huggingface.co/loiccabannes/MambaSan-370m-instruct) 🤗
- Talk to Mamba-Chat on [Google Colab](https://colab.research.google.com/drive/1ZqHOC_RHU8ilAKreUMc_WNbo_melmNJX?usp=sharing)
The Code used for pretraining and finetuning will soon be published on my github: https://github.com/lcabannes
Citation
bibtex
@misc{lcabannes2024MambaSan-370m-instruct,
title = {MambaSan-370m-instruct},
author = {Loïc Cabannes},
year = {2024},
howpublished = {HuggingFace},
url = {https://huggingface.co/loiccabannes/MambaSan-370m-instruct/}
}
| {"language": ["ja"], "license": "apache-2.0", "datasets": ["SkelterLabsInc/JaQuAD"]} | null | loiccabannes/MambaSan-370m-instruct | [
"transformers",
"pytorch",
"ja",
"dataset:SkelterLabsInc/JaQuAD",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-11T22:40:33+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #ja #dataset-SkelterLabsInc/JaQuAD #license-apache-2.0 #endpoints_compatible #region-us
|
MambaSan-370m-instruct
MambaSan-instruct is the first chat Japanese language model based on a state-space model architecture (Mamba).
The model is based on Albert Gu's and Tri Dao's work Mamba: Linear-Time Sequence Modeling with Selective State Spaces (paper) as well as their model implementation. This work was also inspired by heavenq's mamba-chat implementation in English.
Mamba-Chat is based on MambaSan-370m and was fine-tuned on 31,7k examples samples of the SkelterLabsInc/JaQuAD dataset. To learn more, you can:
- Take a look at the model on Huggingface
- Talk to Mamba-Chat on Google Colab
The Code used for pretraining and finetuning will soon be published on my github: URL
Citation
bibtex
@misc{lcabannes2024MambaSan-370m-instruct,
title = {MambaSan-370m-instruct},
author = {Loïc Cabannes},
year = {2024},
howpublished = {HuggingFace},
url = {URL
}
| [] | [
"TAGS\n#transformers #pytorch #ja #dataset-SkelterLabsInc/JaQuAD #license-apache-2.0 #endpoints_compatible #region-us \n"
] | [
46
] | [
"passage: TAGS\n#transformers #pytorch #ja #dataset-SkelterLabsInc/JaQuAD #license-apache-2.0 #endpoints_compatible #region-us \n"
] | [
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null | null | null | # 原神のナヒーダをエミュレートするLLMです。
## llama2ベースのELYZA-japanese-Llama-2-13b-instructをqloraを用いて自作データセットを用いてファインチューニングしました。
ベースモデル↓
https://huggingface.co/elyza/ELYZA-japanese-Llama-2-13b-instruct
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0
| {} | null | gitpullpull/nahida_lora_jp | [
"region:us"
] | 2024-02-11T22:41:34+00:00 | [] | [] | TAGS
#region-us
| # 原神のナヒーダをエミュレートするLLMです。
## llama2ベースのELYZA-japanese-Llama-2-13b-instructをqloraを用いて自作データセットを用いてファインチューニングしました。
ベースモデル↓
URL
---
library_name: peft
---
## Training procedure
The following 'bitsandbytes' quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0
| [
"# 原神のナヒーダをエミュレートするLLMです。",
"## llama2ベースのELYZA-japanese-Llama-2-13b-instructをqloraを用いて自作データセットを用いてファインチューニングしました。\n\nベースモデル↓\n\nURL\n\n\n---\nlibrary_name: peft\n---",
"## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16",
"### Framework versions\n\n\n- PEFT 0.4.0"
] | [
"TAGS\n#region-us \n",
"# 原神のナヒーダをエミュレートするLLMです。",
"## llama2ベースのELYZA-japanese-Llama-2-13b-instructをqloraを用いて自作データセットを用いてファインチューニングしました。\n\nベースモデル↓\n\nURL\n\n\n---\nlibrary_name: peft\n---",
"## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16",
"### Framework versions\n\n\n- PEFT 0.4.0"
] | [
6,
20,
54,
154,
11
] | [
"passage: TAGS\n#region-us \n# 原神のナヒーダをエミュレートするLLMです。## llama2ベースのELYZA-japanese-Llama-2-13b-instructをqloraを用いて自作データセットを用いてファインチューニングしました。\n\nベースモデル↓\n\nURL\n\n\n---\nlibrary_name: peft\n---## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16### Framework versions\n\n\n- PEFT 0.4.0"
] | [
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null | null | transformers | ## Trendyol-LLM-7b-base-v0.1-GGUF models
----
## Description
This repo contains all types of GGUF formatted model files for [Trendyol-LLM-7b-base-v0.1](https://huggingface.co/Trendyol/Trendyol-LLM-7b-base-v0.1).
<img src="https://huggingface.co/Trendyol/Trendyol-LLM-7b-base-v0.1/resolve/main/llama-tr-image.jpeg"
alt="drawing" width="400"/>
## Quantized LLM models and methods
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [Trendyol-LLM-7b-base-v0.1.Q2_K.gguf](https://huggingface.co/tolgadev/Trendyol-LLM-7b-base-v0.1-GGUF/blob/main/trendyol-llm-7b-base-v0.1.Q2_K.gguf) | Q2_K | 2 | 2.59 GB| 4.88 GB | smallest, significant quality loss - not recommended for most purposes |
| [Trendyol-LLM-7b-base-v0.1.Q3_K_S.gguf](https://huggingface.co/tolgadev/Trendyol-LLM-7b-base-v0.1-GGUF/blob/main/trendyol-llm-7b-base-v0.1.Q3_K_S.gguf) | Q3_K_S | 3 | 3.01 GB| 5.56 GB | very small, high quality loss |
| [Trendyol-LLM-7b-base-v0.1.Q3_K_M.gguf](https://huggingface.co/tolgadev/Trendyol-LLM-7b-base-v0.1-GGUF/blob/main/trendyol-llm-7b-base-v0.1.Q3_K_M.gguf) | Q3_K_M | 3 | 3.36 GB| 5.91 GB | very small, high quality loss |
| [Trendyol-LLM-7b-base-v0.1.Q3_K_L.gguf](https://huggingface.co/tolgadev/Trendyol-LLM-7b-base-v0.1-GGUF/blob/main/trendyol-llm-7b-base-v0.1.Q3_K_L.gguf) | Q3_K_L | 3 | 3.66 GB| 6.20 GB | small, substantial quality loss |
| [Trendyol-LLM-7b-base-v0.1.Q4_0.gguf](https://huggingface.co/tolgadev/Trendyol-LLM-7b-base-v0.1-GGUF/blob/main/trendyol-llm-7b-base-v0.1.Q4_0.gguf) | Q4_0 | 4 | 3.9 GB| 6.45 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [Trendyol-LLM-7b-base-v0.1.Q4_K_S.gguf](https://huggingface.co/tolgadev/Trendyol-LLM-7b-base-v0.1-GGUF/blob/main/trendyol-llm-7b-base-v0.1.Q4_K_S.gguf) | Q4_K_S | 4 | 3.93 GB| 6.48 GB | small, greater quality loss |
| [Trendyol-LLM-7b-base-v0.1.Q4_K_M.gguf](https://huggingface.co/tolgadev/Trendyol-LLM-7b-base-v0.1-GGUF/blob/main/trendyol-llm-7b-base-v0.1.Q4_K_M.gguf) | Q4_K_M | 4 | 4.15 GB| 6.69 GB | medium, balanced quality - recommended |
| [Trendyol-LLM-7b-base-v0.1.Q5_0.gguf](https://huggingface.co/tolgadev/Trendyol-LLM-7b-base-v0.1-GGUF/blob/main/trendyol-llm-7b-base-v0.1.Q5_0.gguf) | Q5_0 | 5 | 4.73 GB| 7.15 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [Trendyol-LLM-7b-base-v0.1.Q5_K_S.gguf](https://huggingface.co/tolgadev/Trendyol-LLM-7b-base-v0.1-GGUF/blob/main/trendyol-llm-7b-base-v0.1.Q5_K_S.gguf) | Q5_K_S | 5 | 4.75 GB| 7.27 GB | large, low quality loss - recommended |
| [Trendyol-LLM-7b-base-v0.1.Q5_K_M.gguf](https://huggingface.co/tolgadev/Trendyol-LLM-7b-base-v0.1-GGUF/blob/main/trendyol-llm-7b-base-v0.1.Q5_K_M.gguf) | Q5_K_M | 5 | 4.86 GB| 7.40 GB | large, very low quality loss - recommended |
| [Trendyol-LLM-7b-base-v0.1.Q6_K.gguf](https://huggingface.co/tolgadev/Trendyol-LLM-7b-base-v0.1-GGUF/blob/main/trendyol-llm-7b-base-v0.1.Q6_K.gguf) | Q6_K | 6 | 5.61 GB| 8.15 GB | very large, extremely low quality loss |
| [Trendyol-LLM-7b-base-v0.1.Q8_0.gguf](https://huggingface.co/tolgadev/Trendyol-LLM-7b-base-v0.1-GGUF/blob/main/trendyol-llm-7b-base-v0.1.Q8_0.gguf) | Q8_0 | 8 | 7.27 GB| 9.81 GB | very large, extremely low quality loss - not recommended |
The names of the quantization methods follow the naming convention: "q" + the number of bits + the variant used (detailed below). Here is a list of all the models and their corresponding use cases, based on model cards made by [TheBloke](https://huggingface.co/TheBloke/):
* `q2_k`: Uses Q4_K for the attention.vw and feed_forward.w2 tensors, Q2_K for the other tensors.
* `q3_k_l`: Uses Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K
* `q3_k_m`: Uses Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K
* `q3_k_s`: Uses Q3_K for all tensors
* `q4_0`: Original quant method, 4-bit.
* `q4_1`: Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models.
* `q4_k_m`: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K
* `q4_k_s`: Uses Q4_K for all tensors
* `q5_0`: Higher accuracy, higher resource usage and slower inference.
* `q5_1`: Even higher accuracy, resource usage and slower inference.
* `q5_k_m`: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K
* `q5_k_s`: Uses Q5_K for all tensors
* `q6_k`: Uses Q8_K for all tensors
* `q8_0`: Almost indistinguishable from float16. High resource use and slow. Not recommended for most users.
**TheBloke recommends using Q5_K_M** as it preserves most of the model's performance.
Alternatively, you can use Q4_K_M if you want to save some memory.
In general, K_M versions are better than K_S versions.
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
- LM Studio
- LoLLMS Web UI
- Faraday.dev
## Special thanks to [TheBloke on Huggingface](https://huggingface.co/TheBloke) and [Maxime Labonne on Github](https://github.com/mlabonne/llm-course)
-----
## Model Details
<img src="https://huggingface.co/Trendyol/Trendyol-LLM-7b-base-v0.1/resolve/main/llama-tr-image.jpeg"
alt="drawing" width="400"/>
# **Trendyol LLM**
Trendyol LLM is a generative model that is based on LLaMa2 7B model. This is the repository for the base model.
## Model Details
**Model Developers** Trendyol
**Variations** base and chat variations.
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture** Trendyol LLM is an auto-regressive language model (based on LLaMa2 7b) that uses an optimized transformer architecture. The base version is fine-tuned on 10 billion tokens with the following trainables by using LoRA:
- **lr**=2e-4
- **lora_rank**=64
- **lora_alpha**=128
- **lora_trainable**=q_proj,v_proj,k_proj,o_proj,gate_proj,down_proj,up_proj
- **modules_to_save**=embed_tokens,lm_head
- **lora_dropout**=0.05
- **fp16**=True
- **max_seq_length**=1024
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/peft/lora_diagram.png"
alt="drawing" width="600"/>
## Usage
```python
from transformers import AutoModelForCausalLM, LlamaTokenizer, pipeline
model_id = "Trendyol/Trendyol-LLM-7b-base-v0.1"
tokenizer = LlamaTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id,
device_map='auto',
load_in_8bit=True)
sampling_params = dict(do_sample=True, temperature=0.3, top_k=50, top_p=0.9)
pipe = pipeline("text-generation",
model=model,
tokenizer=tokenizer,
device_map="auto",
max_new_tokens=1024,
return_full_text=True,
repetition_penalty=1.1
)
def generate_output(user_query):
outputs = pipe(user_query,
**sampling_params
)
return outputs[0]["generated_text"]
user_query = "Ders çalışmanın en iyi 5 yolu:"
response = generate_output(user_query)
```
## Limitations, Risks, Bias, and Ethical Considerations
### Limitations and Known Biases
- **Primary Function and Application:** Trendyol LLM, an autoregressive language model, is primarily designed to predict the next token in a text string. While often used for various applications, it is important to note that it has not undergone extensive real-world application testing. Its effectiveness and reliability across diverse scenarios remain largely unverified.
- **Language Comprehension and Generation:** The model is primarily trained in standard English and Turkish. Its performance in understanding and generating slang, informal language, or other languages may be limited, leading to potential errors or misinterpretations.
- **Generation of False Information:** Users should be aware that Trendyol LLM may produce inaccurate or misleading information. Outputs should be considered as starting points or suggestions rather than definitive answers.
### Risks and Ethical Considerations
- **Potential for Harmful Use:** There is a risk that Trendyol LLM could be used to generate offensive or harmful language. We strongly discourage its use for any such purposes and emphasize the need for application-specific safety and fairness evaluations before deployment.
- **Unintended Content and Bias:** The model was trained on a large corpus of text data, which was not explicitly checked for offensive content or existing biases. Consequently, it may inadvertently produce content that reflects these biases or inaccuracies.
- **Toxicity:** Despite efforts to select appropriate training data, the model is capable of generating harmful content, especially when prompted explicitly. We encourage the open-source community to engage in developing strategies to minimize such risks.
### Recommendations for Safe and Ethical Usage
- **Human Oversight:** We recommend incorporating a human curation layer or using filters to manage and improve the quality of outputs, especially in public-facing applications. This approach can help mitigate the risk of generating objectionable content unexpectedly.
- **Application-Specific Testing:** Developers intending to use Trendyol LLM should conduct thorough safety testing and optimization tailored to their specific applications. This is crucial, as the model’s responses can be unpredictable and may occasionally be biased, inaccurate, or offensive.
- **Responsible Development and Deployment:** It is the responsibility of developers and users of Trendyol LLM to ensure its ethical and safe application. We urge users to be mindful of the model's limitations and to employ appropriate safeguards to prevent misuse or harmful consequences. | {"language": ["tr", "en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["trendyol", "llama-2", "turkish"], "model_name": "Trendyol-LLM-7b-base-v0.1", "model_creator": "Trendyol", "base_model": "Trendyol/Trendyol-LLM-7b-base-v0.1", "pipeline_tag": "text-generation", "model_type": "llama", "inference": false, "quantized_by": "tolgadev"} | text-generation | tolgadev/Trendyol-LLM-7b-base-v0.1-GGUF | [
"transformers",
"gguf",
"llama",
"text-generation",
"trendyol",
"llama-2",
"turkish",
"tr",
"en",
"base_model:Trendyol/Trendyol-LLM-7b-base-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-11T22:46:39+00:00 | [] | [
"tr",
"en"
] | TAGS
#transformers #gguf #llama #text-generation #trendyol #llama-2 #turkish #tr #en #base_model-Trendyol/Trendyol-LLM-7b-base-v0.1 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us
| Trendyol-LLM-7b-base-v0.1-GGUF models
-------------------------------------
---
Description
-----------
This repo contains all types of GGUF formatted model files for Trendyol-LLM-7b-base-v0.1.
<img src="URL
alt="drawing" width="400"/>
Quantized LLM models and methods
--------------------------------
The names of the quantization methods follow the naming convention: "q" + the number of bits + the variant used (detailed below). Here is a list of all the models and their corresponding use cases, based on model cards made by TheBloke:
* 'q2\_k': Uses Q4\_K for the URL and feed\_forward.w2 tensors, Q2\_K for the other tensors.
* 'q3\_k\_l': Uses Q5\_K for the URL, URL, and feed\_forward.w2 tensors, else Q3\_K
* 'q3\_k\_m': Uses Q4\_K for the URL, URL, and feed\_forward.w2 tensors, else Q3\_K
* 'q3\_k\_s': Uses Q3\_K for all tensors
* 'q4\_0': Original quant method, 4-bit.
* 'q4\_1': Higher accuracy than q4\_0 but not as high as q5\_0. However has quicker inference than q5 models.
* 'q4\_k\_m': Uses Q6\_K for half of the URL and feed\_forward.w2 tensors, else Q4\_K
* 'q4\_k\_s': Uses Q4\_K for all tensors
* 'q5\_0': Higher accuracy, higher resource usage and slower inference.
* 'q5\_1': Even higher accuracy, resource usage and slower inference.
* 'q5\_k\_m': Uses Q6\_K for half of the URL and feed\_forward.w2 tensors, else Q5\_K
* 'q5\_k\_s': Uses Q5\_K for all tensors
* 'q6\_k': Uses Q8\_K for all tensors
* 'q8\_0': Almost indistinguishable from float16. High resource use and slow. Not recommended for most users.
TheBloke recommends using Q5\_K\_M as it preserves most of the model's performance.
Alternatively, you can use Q4\_K\_M if you want to save some memory.
In general, K\_M versions are better than K\_S versions.
How to download GGUF files
--------------------------
Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
* LM Studio
* LoLLMS Web UI
* URL
Special thanks to TheBloke on Huggingface and Maxime Labonne on Github
----------------------------------------------------------------------
---
Model Details
-------------
<img src="URL
alt="drawing" width="400"/>
Trendyol LLM
============
Trendyol LLM is a generative model that is based on LLaMa2 7B model. This is the repository for the base model.
Model Details
-------------
Model Developers Trendyol
Variations base and chat variations.
Input Models input text only.
Output Models generate text only.
Model Architecture Trendyol LLM is an auto-regressive language model (based on LLaMa2 7b) that uses an optimized transformer architecture. The base version is fine-tuned on 10 billion tokens with the following trainables by using LoRA:
* lr=2e-4
* lora\_rank=64
* lora\_alpha=128
* lora\_trainable=q\_proj,v\_proj,k\_proj,o\_proj,gate\_proj,down\_proj,up\_proj
* modules\_to\_save=embed\_tokens,lm\_head
* lora\_dropout=0.05
* fp16=True
* max\_seq\_length=1024
<img src="URL
alt="drawing" width="600"/>
Usage
-----
Limitations, Risks, Bias, and Ethical Considerations
----------------------------------------------------
### Limitations and Known Biases
* Primary Function and Application: Trendyol LLM, an autoregressive language model, is primarily designed to predict the next token in a text string. While often used for various applications, it is important to note that it has not undergone extensive real-world application testing. Its effectiveness and reliability across diverse scenarios remain largely unverified.
* Language Comprehension and Generation: The model is primarily trained in standard English and Turkish. Its performance in understanding and generating slang, informal language, or other languages may be limited, leading to potential errors or misinterpretations.
* Generation of False Information: Users should be aware that Trendyol LLM may produce inaccurate or misleading information. Outputs should be considered as starting points or suggestions rather than definitive answers.
### Risks and Ethical Considerations
* Potential for Harmful Use: There is a risk that Trendyol LLM could be used to generate offensive or harmful language. We strongly discourage its use for any such purposes and emphasize the need for application-specific safety and fairness evaluations before deployment.
* Unintended Content and Bias: The model was trained on a large corpus of text data, which was not explicitly checked for offensive content or existing biases. Consequently, it may inadvertently produce content that reflects these biases or inaccuracies.
* Toxicity: Despite efforts to select appropriate training data, the model is capable of generating harmful content, especially when prompted explicitly. We encourage the open-source community to engage in developing strategies to minimize such risks.
### Recommendations for Safe and Ethical Usage
* Human Oversight: We recommend incorporating a human curation layer or using filters to manage and improve the quality of outputs, especially in public-facing applications. This approach can help mitigate the risk of generating objectionable content unexpectedly.
* Application-Specific Testing: Developers intending to use Trendyol LLM should conduct thorough safety testing and optimization tailored to their specific applications. This is crucial, as the model’s responses can be unpredictable and may occasionally be biased, inaccurate, or offensive.
* Responsible Development and Deployment: It is the responsibility of developers and users of Trendyol LLM to ensure its ethical and safe application. We urge users to be mindful of the model's limitations and to employ appropriate safeguards to prevent misuse or harmful consequences.
| [
"### Limitations and Known Biases\n\n\n* Primary Function and Application: Trendyol LLM, an autoregressive language model, is primarily designed to predict the next token in a text string. While often used for various applications, it is important to note that it has not undergone extensive real-world application testing. Its effectiveness and reliability across diverse scenarios remain largely unverified.\n* Language Comprehension and Generation: The model is primarily trained in standard English and Turkish. Its performance in understanding and generating slang, informal language, or other languages may be limited, leading to potential errors or misinterpretations.\n* Generation of False Information: Users should be aware that Trendyol LLM may produce inaccurate or misleading information. Outputs should be considered as starting points or suggestions rather than definitive answers.",
"### Risks and Ethical Considerations\n\n\n* Potential for Harmful Use: There is a risk that Trendyol LLM could be used to generate offensive or harmful language. We strongly discourage its use for any such purposes and emphasize the need for application-specific safety and fairness evaluations before deployment.\n* Unintended Content and Bias: The model was trained on a large corpus of text data, which was not explicitly checked for offensive content or existing biases. Consequently, it may inadvertently produce content that reflects these biases or inaccuracies.\n* Toxicity: Despite efforts to select appropriate training data, the model is capable of generating harmful content, especially when prompted explicitly. We encourage the open-source community to engage in developing strategies to minimize such risks.",
"### Recommendations for Safe and Ethical Usage\n\n\n* Human Oversight: We recommend incorporating a human curation layer or using filters to manage and improve the quality of outputs, especially in public-facing applications. This approach can help mitigate the risk of generating objectionable content unexpectedly.\n* Application-Specific Testing: Developers intending to use Trendyol LLM should conduct thorough safety testing and optimization tailored to their specific applications. This is crucial, as the model’s responses can be unpredictable and may occasionally be biased, inaccurate, or offensive.\n* Responsible Development and Deployment: It is the responsibility of developers and users of Trendyol LLM to ensure its ethical and safe application. We urge users to be mindful of the model's limitations and to employ appropriate safeguards to prevent misuse or harmful consequences."
] | [
"TAGS\n#transformers #gguf #llama #text-generation #trendyol #llama-2 #turkish #tr #en #base_model-Trendyol/Trendyol-LLM-7b-base-v0.1 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n",
"### Limitations and Known Biases\n\n\n* Primary Function and Application: Trendyol LLM, an autoregressive language model, is primarily designed to predict the next token in a text string. While often used for various applications, it is important to note that it has not undergone extensive real-world application testing. Its effectiveness and reliability across diverse scenarios remain largely unverified.\n* Language Comprehension and Generation: The model is primarily trained in standard English and Turkish. Its performance in understanding and generating slang, informal language, or other languages may be limited, leading to potential errors or misinterpretations.\n* Generation of False Information: Users should be aware that Trendyol LLM may produce inaccurate or misleading information. Outputs should be considered as starting points or suggestions rather than definitive answers.",
"### Risks and Ethical Considerations\n\n\n* Potential for Harmful Use: There is a risk that Trendyol LLM could be used to generate offensive or harmful language. We strongly discourage its use for any such purposes and emphasize the need for application-specific safety and fairness evaluations before deployment.\n* Unintended Content and Bias: The model was trained on a large corpus of text data, which was not explicitly checked for offensive content or existing biases. Consequently, it may inadvertently produce content that reflects these biases or inaccuracies.\n* Toxicity: Despite efforts to select appropriate training data, the model is capable of generating harmful content, especially when prompted explicitly. We encourage the open-source community to engage in developing strategies to minimize such risks.",
"### Recommendations for Safe and Ethical Usage\n\n\n* Human Oversight: We recommend incorporating a human curation layer or using filters to manage and improve the quality of outputs, especially in public-facing applications. This approach can help mitigate the risk of generating objectionable content unexpectedly.\n* Application-Specific Testing: Developers intending to use Trendyol LLM should conduct thorough safety testing and optimization tailored to their specific applications. This is crucial, as the model’s responses can be unpredictable and may occasionally be biased, inaccurate, or offensive.\n* Responsible Development and Deployment: It is the responsibility of developers and users of Trendyol LLM to ensure its ethical and safe application. We urge users to be mindful of the model's limitations and to employ appropriate safeguards to prevent misuse or harmful consequences."
] | [
81,
191,
195,
205
] | [
"passage: TAGS\n#transformers #gguf #llama #text-generation #trendyol #llama-2 #turkish #tr #en #base_model-Trendyol/Trendyol-LLM-7b-base-v0.1 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n### Limitations and Known Biases\n\n\n* Primary Function and Application: Trendyol LLM, an autoregressive language model, is primarily designed to predict the next token in a text string. While often used for various applications, it is important to note that it has not undergone extensive real-world application testing. Its effectiveness and reliability across diverse scenarios remain largely unverified.\n* Language Comprehension and Generation: The model is primarily trained in standard English and Turkish. Its performance in understanding and generating slang, informal language, or other languages may be limited, leading to potential errors or misinterpretations.\n* Generation of False Information: Users should be aware that Trendyol LLM may produce inaccurate or misleading information. Outputs should be considered as starting points or suggestions rather than definitive answers.### Risks and Ethical Considerations\n\n\n* Potential for Harmful Use: There is a risk that Trendyol LLM could be used to generate offensive or harmful language. We strongly discourage its use for any such purposes and emphasize the need for application-specific safety and fairness evaluations before deployment.\n* Unintended Content and Bias: The model was trained on a large corpus of text data, which was not explicitly checked for offensive content or existing biases. Consequently, it may inadvertently produce content that reflects these biases or inaccuracies.\n* Toxicity: Despite efforts to select appropriate training data, the model is capable of generating harmful content, especially when prompted explicitly. We encourage the open-source community to engage in developing strategies to minimize such risks."
] | [
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null | null | transformers |
<!-- 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. -->
# qa_model
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) 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: 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 50 | 3.4426 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "xlnet-base-cased", "model-index": [{"name": "qa_model", "results": []}]} | question-answering | tanmeh/qa_model | [
"transformers",
"tensorboard",
"safetensors",
"xlnet",
"question-answering",
"generated_from_trainer",
"base_model:xlnet-base-cased",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2024-02-11T22:51:42+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #xlnet #question-answering #generated_from_trainer #base_model-xlnet-base-cased #license-mit #endpoints_compatible #region-us
| qa\_model
=========
This model is a fine-tuned version of xlnet-base-cased 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: 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: 1
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.1.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #xlnet #question-answering #generated_from_trainer #base_model-xlnet-base-cased #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
59,
98,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #xlnet #question-answering #generated_from_trainer #base_model-xlnet-base-cased #license-mit #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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null | null | transformers |
# Uploaded model
- **Developed by:** 922CA
- **License:** apache-2.0
- **Finetuned from model :** SeaLLMs/SeaLLM-7B-v2
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)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "mistral", "gguf"], "base_model": "SeaLLMs/SeaLLM-7B-v2"} | null | 922CA/tagamistral-7b-v1-gguf | [
"transformers",
"gguf",
"mistral",
"text-generation-inference",
"unsloth",
"en",
"base_model:SeaLLMs/SeaLLM-7B-v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-11T22:52:37+00:00 | [] | [
"en"
] | TAGS
#transformers #gguf #mistral #text-generation-inference #unsloth #en #base_model-SeaLLMs/SeaLLM-7B-v2 #license-apache-2.0 #endpoints_compatible #region-us
|
# Uploaded model
- Developed by: 922CA
- License: apache-2.0
- Finetuned from model : SeaLLMs/SeaLLM-7B-v2
This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.
<img src="URL width="200"/>
| [
"# Uploaded model\n\n- Developed by: 922CA\n- License: apache-2.0\n- Finetuned from model : SeaLLMs/SeaLLM-7B-v2\n\nThis mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] | [
"TAGS\n#transformers #gguf #mistral #text-generation-inference #unsloth #en #base_model-SeaLLMs/SeaLLM-7B-v2 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Uploaded model\n\n- Developed by: 922CA\n- License: apache-2.0\n- Finetuned from model : SeaLLMs/SeaLLM-7B-v2\n\nThis mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] | [
66,
80
] | [
"passage: TAGS\n#transformers #gguf #mistral #text-generation-inference #unsloth #en #base_model-SeaLLMs/SeaLLM-7B-v2 #license-apache-2.0 #endpoints_compatible #region-us \n# Uploaded model\n\n- Developed by: 922CA\n- License: apache-2.0\n- Finetuned from model : SeaLLMs/SeaLLM-7B-v2\n\nThis mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
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null | null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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### Framework versions
- PEFT 0.8.2 | {"license": "apache-2.0", "library_name": "peft", "base_model": "moreh/MoMo-72B-LoRA-V1.4"} | null | SF-Foundation/Ein-72B-v0.12 | [
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#peft #safetensors #arxiv-1910.09700 #base_model-moreh/MoMo-72B-LoRA-V1.4 #license-apache-2.0 #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
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- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
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] |
null | null | stable-baselines3 |
# **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
...
```
| {"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": "220.67 +/- 43.43", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | GccX11/ppo-LunarLander-v2 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-11T22:58:19+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
39,
41,
17
] | [
"passage: TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
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null | null | transformers |
<!-- 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_fine_tuned_model
This model is a fine-tuned version of [joelniklaus/legal-swiss-roberta-large](https://huggingface.co/joelniklaus/legal-swiss-roberta-large) on the swiss_judgment_prediction dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4358
- Accuracy: 0.8314
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4692 | 1.0 | 2217 | 0.4277 | 0.8305 |
| 0.4261 | 2.0 | 4434 | 0.4358 | 0.8314 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "cc", "tags": ["generated_from_trainer"], "datasets": ["swiss_judgment_prediction"], "metrics": ["accuracy"], "base_model": "joelniklaus/legal-swiss-roberta-large", "model-index": [{"name": "my_fine_tuned_model", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "swiss_judgment_prediction", "type": "swiss_judgment_prediction", "config": "de", "split": "test", "args": "de"}, "metrics": [{"type": "accuracy", "value": 0.831362467866324, "name": "Accuracy"}]}]}]} | text-classification | mhmmterts/my_fine_tuned_model | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:swiss_judgment_prediction",
"base_model:joelniklaus/legal-swiss-roberta-large",
"license:cc",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-11T22:59:58+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #dataset-swiss_judgment_prediction #base_model-joelniklaus/legal-swiss-roberta-large #license-cc #model-index #autotrain_compatible #endpoints_compatible #region-us
| my\_fine\_tuned\_model
======================
This model is a fine-tuned version of joelniklaus/legal-swiss-roberta-large on the swiss\_judgment\_prediction dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4358
* Accuracy: 0.8314
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: 2
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.1.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
94,
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"passage: TAGS\n#transformers #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #dataset-swiss_judgment_prediction #base_model-joelniklaus/legal-swiss-roberta-large #license-cc #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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null | null | transformers | # merged2
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the passthrough merge method.
### Models Merged
The following models were included in the merge:
* /home/ubuntu/nvm/mathdpo
### Configuration
The following YAML configuration was used to produce this model:
```yaml
dtype: bfloat16
merge_method: passthrough
slices:
- sources:
- layer_range: [0, 7]
model:
model:
path: /home/ubuntu/nvm/mathdpo
- sources:
- layer_range: [9, 12]
model:
model:
path: /home/ubuntu/nvm/mathdpo
- sources:
- layer_range: [14, 17]
model:
model:
path: /home/ubuntu/nvm/mathdpo
- sources:
- layer_range: [19, 32]
model:
model:
path: /home/ubuntu/nvm/mathdpo
``` | {"license": "cc-by-nc-2.0", "library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": []} | text-generation | rizla/rizla-11 | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"mergekit",
"merge",
"license:cc-by-nc-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-11T23:04:54+00:00 | [] | [] | TAGS
#transformers #safetensors #mixtral #text-generation #mergekit #merge #license-cc-by-nc-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # merged2
This is a merge of pre-trained language models created using mergekit.
## Merge Details
### Merge Method
This model was merged using the passthrough merge method.
### Models Merged
The following models were included in the merge:
* /home/ubuntu/nvm/mathdpo
### Configuration
The following YAML configuration was used to produce this model:
| [
"# merged2\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the passthrough merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* /home/ubuntu/nvm/mathdpo",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
"TAGS\n#transformers #safetensors #mixtral #text-generation #mergekit #merge #license-cc-by-nc-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# merged2\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the passthrough merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* /home/ubuntu/nvm/mathdpo",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
65,
20,
4,
17,
28,
17
] | [
"passage: TAGS\n#transformers #safetensors #mixtral #text-generation #mergekit #merge #license-cc-by-nc-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# merged2\n\nThis is a merge of pre-trained language models created using mergekit.## Merge Details### Merge Method\n\nThis model was merged using the passthrough merge method.### Models Merged\n\nThe following models were included in the merge:\n* /home/ubuntu/nvm/mathdpo### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
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null | null | transformers |
# Model Card for Model ID
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|
# Model Card for Model ID
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## Uses
### Direct Use
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### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
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### Compute Infrastructure
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## Glossary [optional]
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null | null | transformers |
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[More Information Needed]
## Model Card Contact
[More Information Needed]
| {"library_name": "transformers", "tags": []} | text2text-generation | Professor/davlan-small-doublequant | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
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"1910.09700"
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#transformers #safetensors #t5 #text2text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us
|
# Model Card for Model ID
## Model Details
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## Training Details
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## Evaluation
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
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### Compute Infrastructure
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[optional]
BibTeX:
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] |
null | null | transformers | ---
<h1 align='center' style='font-size: 36px; font-weight: bold;'>Sparrow</h1>
<h3 align='center' style='font-size: 24px;'>Blazzing Fast Tiny Vision Language Model</h3>
<p align="center">
<img src="https://cdn-uploads.huggingface.co/production/uploads/650c7fbb8ffe1f53bdbe1aec/DTjDSq2yG-5Cqnk6giPFq.jpeg" width="50%" height="auto"/>
</p>
<p align='center', style='font-size: 16px;' >A Custom 3B parameter Model Enhanced for Educational Contexts: This specialized model integrates slide-text pairs from machine learning classes, leveraging a unique training approach. It connects a frozen pre-trained vision encoder (SigLip) with a frozen language model (Phi-2) through an innovative projector. The model employs attention mechanisms and language modeling loss to deeply understand and generate educational content, specifically tailored to the context of machine learning education. Built by <a href="https://www.linkedin.com/in/manishkumarthota/">@Manish</a> The model is released for research purposes only, commercial use is not allowed. </p>
## How to use
**Install dependencies**
```bash
pip install transformers # latest version is ok, but we recommend v4.31.0
pip install -q pillow accelerate einops
```
You can use the following code for model inference. The format of text instruction is similar to [LLaVA](https://github.com/haotian-liu/LLaVA).
```Python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from PIL import Image
torch.set_default_device("cuda")
#Create model
model = AutoModelForCausalLM.from_pretrained(
"ManishThota/Sparrow",
torch_dtype=torch.float16,
device_map="auto",
trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("ManishThota/Sparrow", trust_remote_code=True)
#function to generate the answer
def predict(question, image_path):
#Set inputs
text = f"A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\n{question}? ASSISTANT:"
image = Image.open(image_path)
input_ids = tokenizer(text, return_tensors='pt').input_ids.to('cuda')
image_tensor = model.image_preprocess(image)
#Generate the answer
output_ids = model.generate(
input_ids,
max_new_tokens=25,
images=image_tensor,
use_cache=True)[0]
return tokenizer.decode(output_ids[input_ids.shape[1]:], skip_special_tokens=True).strip()
``` | {"license": "creativeml-openrail-m"} | text-generation | ManishThota/Sparrow | [
"transformers",
"pytorch",
"imp",
"text-generation",
"custom_code",
"license:creativeml-openrail-m",
"autotrain_compatible",
"has_space",
"region:us"
] | 2024-02-11T23:18:32+00:00 | [] | [] | TAGS
#transformers #pytorch #imp #text-generation #custom_code #license-creativeml-openrail-m #autotrain_compatible #has_space #region-us
| ---
<h1 align='center' style='font-size: 36px; font-weight: bold;'>Sparrow</h1>
<h3 align='center' style='font-size: 24px;'>Blazzing Fast Tiny Vision Language Model</h3>
<p align="center">
<img src="URL width="50%" height="auto"/>
</p>
<p align='center', style='font-size: 16px;' >A Custom 3B parameter Model Enhanced for Educational Contexts: This specialized model integrates slide-text pairs from machine learning classes, leveraging a unique training approach. It connects a frozen pre-trained vision encoder (SigLip) with a frozen language model (Phi-2) through an innovative projector. The model employs attention mechanisms and language modeling loss to deeply understand and generate educational content, specifically tailored to the context of machine learning education. Built by <a href="URL The model is released for research purposes only, commercial use is not allowed. </p>
## How to use
Install dependencies
You can use the following code for model inference. The format of text instruction is similar to LLaVA.
| [
"## How to use\n\n\nInstall dependencies\n\n\nYou can use the following code for model inference. The format of text instruction is similar to LLaVA."
] | [
"TAGS\n#transformers #pytorch #imp #text-generation #custom_code #license-creativeml-openrail-m #autotrain_compatible #has_space #region-us \n",
"## How to use\n\n\nInstall dependencies\n\n\nYou can use the following code for model inference. The format of text instruction is similar to LLaVA."
] | [
49,
31
] | [
"passage: TAGS\n#transformers #pytorch #imp #text-generation #custom_code #license-creativeml-openrail-m #autotrain_compatible #has_space #region-us \n## How to use\n\n\nInstall dependencies\n\n\nYou can use the following code for model inference. The format of text instruction is similar to LLaVA."
] | [
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null | null | transformers |
## Exllama v2 Quantizations of MBX-7B-v3-DPO
Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.13">turboderp's ExLlamaV2 v0.0.13</a> for quantization.
<b>The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)</b>
Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
Original model: https://huggingface.co/macadeliccc/MBX-7B-v3-DPO
| Branch | Bits | lm_head bits | VRAM (4k) | VRAM (16k) | VRAM (32k) | Description |
| ----- | ---- | ------- | ------ | ------ | ------ | ------------ |
| [8_0](https://huggingface.co/bartowski/MBX-7B-v3-DPO-exl2/tree/8_0) | 8.0 | 8.0 | 8.4 GB | 9.8 GB | 11.8 GB | Maximum quality that ExLlamaV2 can produce, near unquantized performance. |
| [6_5](https://huggingface.co/bartowski/MBX-7B-v3-DPO-exl2/tree/6_5) | 6.5 | 8.0 | 7.2 GB | 8.6 GB | 10.6 GB | Very similar to 8.0, good tradeoff of size vs performance, **recommended**. |
| [5_0](https://huggingface.co/bartowski/MBX-7B-v3-DPO-exl2/tree/5_0) | 5.0 | 6.0 | 6.0 GB | 7.4 GB | 9.4 GB | Slightly lower quality vs 6.5, but usable on 8GB cards. |
| [4_25](https://huggingface.co/bartowski/MBX-7B-v3-DPO-exl2/tree/4_25) | 4.25 | 6.0 | 5.3 GB | 6.7 GB | 8.7 GB | GPTQ equivalent bits per weight, slightly higher quality. |
| [3_5](https://huggingface.co/bartowski/MBX-7B-v3-DPO-exl2/tree/3_5) | 3.5 | 6.0 | 4.7 GB | 6.1 GB | 8.1 GB | Lower quality, only use if you have to. |
## Download instructions
With git:
```shell
git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/MBX-7B-v3-DPO-exl2 MBX-7B-v3-DPO-exl2-6_5
```
With huggingface hub (credit to TheBloke for instructions):
```shell
pip3 install huggingface-hub
```
To download the `main` (only useful if you only care about measurement.json) branch to a folder called `MBX-7B-v3-DPO-exl2`:
```shell
mkdir MBX-7B-v3-DPO-exl2
huggingface-cli download bartowski/MBX-7B-v3-DPO-exl2 --local-dir MBX-7B-v3-DPO-exl2 --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
Linux:
```shell
mkdir MBX-7B-v3-DPO-exl2-6_5
huggingface-cli download bartowski/MBX-7B-v3-DPO-exl2 --revision 6_5 --local-dir MBX-7B-v3-DPO-exl2-6_5 --local-dir-use-symlinks False
```
Windows (which apparently doesn't like _ in folders sometimes?):
```shell
mkdir MBX-7B-v3-DPO-exl2-6.5
huggingface-cli download bartowski/MBX-7B-v3-DPO-exl2 --revision 6_5 --local-dir MBX-7B-v3-DPO-exl2-6.5 --local-dir-use-symlinks False
```
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski | {"license": "cc", "library_name": "transformers", "datasets": ["jondurbin/truthy-dpo-v0.1"], "quantized_by": "bartowski", "pipeline_tag": "text-generation"} | text-generation | bartowski/MBX-7B-v3-DPO-exl2 | [
"transformers",
"text-generation",
"dataset:jondurbin/truthy-dpo-v0.1",
"license:cc",
"endpoints_compatible",
"region:us"
] | 2024-02-11T23:18:47+00:00 | [] | [] | TAGS
#transformers #text-generation #dataset-jondurbin/truthy-dpo-v0.1 #license-cc #endpoints_compatible #region-us
| Exllama v2 Quantizations of MBX-7B-v3-DPO
-----------------------------------------
Using <a href="URL ExLlamaV2 v0.0.13 for quantization.
**The "main" branch only contains the URL, download one of the other branches for the model (see below)**
Each branch contains an individual bits per weight, with the main one containing only the URL for further conversions.
Original model: URL
Download instructions
---------------------
With git:
With huggingface hub (credit to TheBloke for instructions):
To download the 'main' (only useful if you only care about URL) branch to a folder called 'MBX-7B-v3-DPO-exl2':
To download from a different branch, add the '--revision' parameter:
Linux:
Windows (which apparently doesn't like \_ in folders sometimes?):
Want to support my work? Visit my ko-fi page here: URL
| [] | [
"TAGS\n#transformers #text-generation #dataset-jondurbin/truthy-dpo-v0.1 #license-cc #endpoints_compatible #region-us \n"
] | [
43
] | [
"passage: TAGS\n#transformers #text-generation #dataset-jondurbin/truthy-dpo-v0.1 #license-cc #endpoints_compatible #region-us \n"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | feature-extraction | tommymarto/LernnaviBERT_baseline_students_answers_768_bert_seq_len_10 | [
"transformers",
"safetensors",
"bert",
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#transformers #safetensors #bert #feature-extraction #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
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## Uses
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### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
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- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
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#### Testing Data
#### Factors
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
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null | null | transformers |
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| {"library_name": "transformers", "tags": []} | text2text-generation | Professor/davlan-small-nf4 | [
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# Model Card for Model ID
## Model Details
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- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
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] |
null | null | transformers | # Classify text by UNSPSC family
Forked from https://huggingface.co/govspend/unspsc_family_5examples_test2
See https://en.wikipedia.org/wiki/UNSPSC
## Usage
```python
pipe = pipeline("text-classification", model="andruhon/unspsc_family_5examples_test2", tokenizer="bert-base-uncased")
pipe("7oz hammer");
# Would return something like {'label': 'LABEL_105', 'score': 0.339}
# In this case LABEL_105 clearly goes into 27110000 Handtools
```
## License
The original model didn't have license file.
Considering that it's BERT it should have the same license, which I think is Apache 2.0.
Use on your own risk. I'll update this file once I have more info.
| {"language": ["en"], "license": "apache-2.0", "widget": [{"text": "7oz hammer"}, {"text": "Cat6e network cable"}, {"text": "Printer HL-1210W"}]} | text-classification | andruhon/unspsc_family_5examples_test2 | [
"transformers",
"safetensors",
"bert",
"text-classification",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-11T23:28:24+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #bert #text-classification #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| # Classify text by UNSPSC family
Forked from URL
See URL
## Usage
## License
The original model didn't have license file.
Considering that it's BERT it should have the same license, which I think is Apache 2.0.
Use on your own risk. I'll update this file once I have more info.
| [
"# Classify text by UNSPSC family\n\nForked from URL\n\nSee URL",
"## Usage",
"## License\nThe original model didn't have license file. \nConsidering that it's BERT it should have the same license, which I think is Apache 2.0.\nUse on your own risk. I'll update this file once I have more info."
] | [
"TAGS\n#transformers #safetensors #bert #text-classification #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Classify text by UNSPSC family\n\nForked from URL\n\nSee URL",
"## Usage",
"## License\nThe original model didn't have license file. \nConsidering that it's BERT it should have the same license, which I think is Apache 2.0.\nUse on your own risk. I'll update this file once I have more info."
] | [
47,
15,
3,
53
] | [
"passage: TAGS\n#transformers #safetensors #bert #text-classification #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# Classify text by UNSPSC family\n\nForked from URL\n\nSee URL## Usage## License\nThe original model didn't have license file. \nConsidering that it's BERT it should have the same license, which I think is Apache 2.0.\nUse on your own risk. I'll update this file once I have more info."
] | [
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null | null | transformers |
# Model Card for Model ID
Transformers-based Language Model Fine-tuned for Assistant Use Cases
## Model Details
### Model Description
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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:** anup kumar
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## Uses
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### Direct Use
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## Technical Specifications [optional]
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[More Information Needed] | {"language": ["en"], "license": "apache-2.0", "library_name": "transformers"} | text-generation | anupk/akMistral7b | [
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#transformers #safetensors #mistral #text-generation #en #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us
|
# Model Card for Model ID
Transformers-based Language Model Fine-tuned for Assistant Use Cases
## Model Details
### Model Description
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: anup kumar
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]: mistral7b
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
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null | null | transformers |
# Model Card for dfurman/phi-2-scientific-papers-base-v0.1
A base model for scientific papers trained on 70MB (txt file) of research literature.
## Model Details
### Model Description
- **Developed by:** Daniel Furman
- **Model type:** Phi-2
- **Language(s) (NLP):** English
- **License:** Apache 2.0
- **Finetuned from model:** microsoft/phi-2
## Uses
The intended use of this model includes scientific paper next word prediction. It is a base model for the scientific research domain.
### Direct Use
Use for document completion on scientific papers.
### Downstream Use
Finetune for other tasks in scientific literature domain, like Q&A on scientific papers.
### Out-of-Scope Use
Anything outside of scientific research adjacent NLP tasks.
## Bias, Risks, and Limitations
No guardrails are baked into this model. Use at your own risk.
### Compute Info
This model was fine-tuned using the accelerate package on a cluster from RunPod with 4x A100-SXM4-80GB GPUs (99% memory usage across each during training).
| {"license": "apache-2.0", "library_name": "transformers", "pipeline_tag": "text-generation", "base_model": "microsoft/phi-2"} | text-generation | dfurman/phi-2-scientific-papers-base-v0.1 | [
"transformers",
"safetensors",
"phi",
"text-generation",
"custom_code",
"base_model:microsoft/phi-2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-11T23:35:08+00:00 | [] | [] | TAGS
#transformers #safetensors #phi #text-generation #custom_code #base_model-microsoft/phi-2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for dfurman/phi-2-scientific-papers-base-v0.1
A base model for scientific papers trained on 70MB (txt file) of research literature.
## Model Details
### Model Description
- Developed by: Daniel Furman
- Model type: Phi-2
- Language(s) (NLP): English
- License: Apache 2.0
- Finetuned from model: microsoft/phi-2
## Uses
The intended use of this model includes scientific paper next word prediction. It is a base model for the scientific research domain.
### Direct Use
Use for document completion on scientific papers.
### Downstream Use
Finetune for other tasks in scientific literature domain, like Q&A on scientific papers.
### Out-of-Scope Use
Anything outside of scientific research adjacent NLP tasks.
## Bias, Risks, and Limitations
No guardrails are baked into this model. Use at your own risk.
### Compute Info
This model was fine-tuned using the accelerate package on a cluster from RunPod with 4x A100-SXM4-80GB GPUs (99% memory usage across each during training).
| [
"# Model Card for dfurman/phi-2-scientific-papers-base-v0.1\n\nA base model for scientific papers trained on 70MB (txt file) of research literature.",
"## Model Details",
"### Model Description\n\n- Developed by: Daniel Furman\n- Model type: Phi-2\n- Language(s) (NLP): English\n- License: Apache 2.0\n- Finetuned from model: microsoft/phi-2",
"## Uses\n\nThe intended use of this model includes scientific paper next word prediction. It is a base model for the scientific research domain.",
"### Direct Use\n\nUse for document completion on scientific papers.",
"### Downstream Use\n\nFinetune for other tasks in scientific literature domain, like Q&A on scientific papers.",
"### Out-of-Scope Use\n\nAnything outside of scientific research adjacent NLP tasks.",
"## Bias, Risks, and Limitations\n\nNo guardrails are baked into this model. Use at your own risk.",
"### Compute Info\n\nThis model was fine-tuned using the accelerate package on a cluster from RunPod with 4x A100-SXM4-80GB GPUs (99% memory usage across each during training)."
] | [
"TAGS\n#transformers #safetensors #phi #text-generation #custom_code #base_model-microsoft/phi-2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Card for dfurman/phi-2-scientific-papers-base-v0.1\n\nA base model for scientific papers trained on 70MB (txt file) of research literature.",
"## Model Details",
"### Model Description\n\n- Developed by: Daniel Furman\n- Model type: Phi-2\n- Language(s) (NLP): English\n- License: Apache 2.0\n- Finetuned from model: microsoft/phi-2",
"## Uses\n\nThe intended use of this model includes scientific paper next word prediction. It is a base model for the scientific research domain.",
"### Direct Use\n\nUse for document completion on scientific papers.",
"### Downstream Use\n\nFinetune for other tasks in scientific literature domain, like Q&A on scientific papers.",
"### Out-of-Scope Use\n\nAnything outside of scientific research adjacent NLP tasks.",
"## Bias, Risks, and Limitations\n\nNo guardrails are baked into this model. Use at your own risk.",
"### Compute Info\n\nThis model was fine-tuned using the accelerate package on a cluster from RunPod with 4x A100-SXM4-80GB GPUs (99% memory usage across each during training)."
] | [
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"passage: TAGS\n#transformers #safetensors #phi #text-generation #custom_code #base_model-microsoft/phi-2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# Model Card for dfurman/phi-2-scientific-papers-base-v0.1\n\nA base model for scientific papers trained on 70MB (txt file) of research literature.## Model Details### Model Description\n\n- Developed by: Daniel Furman\n- Model type: Phi-2\n- Language(s) (NLP): English\n- License: Apache 2.0\n- Finetuned from model: microsoft/phi-2## Uses\n\nThe intended use of this model includes scientific paper next word prediction. It is a base model for the scientific research domain.### Direct Use\n\nUse for document completion on scientific papers.### Downstream Use\n\nFinetune for other tasks in scientific literature domain, like Q&A on scientific papers.### Out-of-Scope Use\n\nAnything outside of scientific research adjacent NLP tasks.## Bias, Risks, and Limitations\n\nNo guardrails are baked into this model. Use at your own risk.### Compute Info\n\nThis model was fine-tuned using the accelerate package on a cluster from RunPod with 4x A100-SXM4-80GB GPUs (99% memory usage across each during training)."
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null | null | null |
# **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="paragrk1/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"])
```
| {"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}]}]}]} | reinforcement-learning | paragrk1/q-FrozenLake-v1-4x4-noSlippery | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2024-02-11T23:51:06+00:00 | [] | [] | TAGS
#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing1 FrozenLake-v1
This is a trained model of a Q-Learning agent playing FrozenLake-v1 .
## Usage
| [
"# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage"
] | [
"TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage"
] | [
40,
39
] | [
"passage: TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage"
] | [
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null | null | transformers |
# Model Card for Model ID
We prune the Phi-2 (2.7B) model to 35% sparsty (1.8B) and then finetune on 100K 2048 length sequences from the C4 dataset (https://huggingface.co/datasets/c4).
Our pruning algorithm is described in the paper [Everybody Prune Now: Structured Pruning of LLMs with only Forward Passes](https://arxiv.org/abs/2402.05406).
[Code for pruning algorithm can be found here ](https://github.com/ldery/Bonsai/tree/main).
## Model Details
Model is derived from Pruning the [Phi-2 Model](https://huggingface.co/microsoft/phi-2)
### 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:** Lucio Dery, Steven Kolawole, Jean-François Kagy, Virginia Smith, Graham Neubig, Ameet Talwalkar
- **Model type:** Decoder-only
- **Language(s) (NLP):** English
- **License:** MIT
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [https://github.com/ldery/Bonsai/tree/main]
- **Paper [optional]:** [https://arxiv.org/abs/2402.05406]
## Training Details
### Training Data
Finetuned on 100K 2048 length sequences from the C4 dataset (https://huggingface.co/datasets/c4).
### Training Procedure
Full fine-tuning.
#### Training Hyperparameters
Distillation KL-Weight : 0.01
Learning Rate : 1e-4
Batch Size : 128
Optimzer : AdamW
Warmup Steps : 5
### License
The model is licensed under the [MIT license](https://huggingface.co/luciodery/Bonsai-PrunedPhi-1.8B/blob/main/LICENSE).
## 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:** NVIDIA A6000
## Citation
**BibTeX:**
@misc{dery2024everybody,
title={Everybody Prune Now: Structured Pruning of LLMs with only Forward Passes},
author={Lucio Dery and Steven Kolawole and Jean-Francois Kagey and Virginia Smith and Graham Neubig and Ameet Talwalkar},
year={2024},
eprint={2402.05406},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
## Model Card Authors [optional]
Lucio Dery: [email protected]
## Model Card Contact
[email protected] | {"language": ["en"], "license": "mit", "library_name": "transformers", "tags": ["Structured Pruning", "Phi-2", "Memory-efficient Pruning"]} | null | luciodery/Bonsai-PrunedPhi-1.8B | [
"transformers",
"safetensors",
"phi",
"Structured Pruning",
"Phi-2",
"Memory-efficient Pruning",
"custom_code",
"en",
"arxiv:2402.05406",
"arxiv:1910.09700",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2024-02-11T23:52:00+00:00 | [
"2402.05406",
"1910.09700"
] | [
"en"
] | TAGS
#transformers #safetensors #phi #Structured Pruning #Phi-2 #Memory-efficient Pruning #custom_code #en #arxiv-2402.05406 #arxiv-1910.09700 #license-mit #endpoints_compatible #region-us
|
# Model Card for Model ID
We prune the Phi-2 (2.7B) model to 35% sparsty (1.8B) and then finetune on 100K 2048 length sequences from the C4 dataset (URL
Our pruning algorithm is described in the paper Everybody Prune Now: Structured Pruning of LLMs with only Forward Passes.
Code for pruning algorithm can be found here .
## Model Details
Model is derived from Pruning the Phi-2 Model
### Model Description
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: Lucio Dery, Steven Kolawole, Jean-François Kagy, Virginia Smith, Graham Neubig, Ameet Talwalkar
- Model type: Decoder-only
- Language(s) (NLP): English
- License: MIT
### Model Sources [optional]
- Repository: [URL
- Paper [optional]: [URL
## Training Details
### Training Data
Finetuned on 100K 2048 length sequences from the C4 dataset (URL
### Training Procedure
Full fine-tuning.
#### Training Hyperparameters
Distillation KL-Weight : 0.01
Learning Rate : 1e-4
Batch Size : 128
Optimzer : AdamW
Warmup Steps : 5
### License
The model is licensed under the MIT license.
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: NVIDIA A6000
BibTeX:
@misc{dery2024everybody,
title={Everybody Prune Now: Structured Pruning of LLMs with only Forward Passes},
author={Lucio Dery and Steven Kolawole and Jean-Francois Kagey and Virginia Smith and Graham Neubig and Ameet Talwalkar},
year={2024},
eprint={2402.05406},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
## Model Card Authors [optional]
Lucio Dery: ldery@URL
## Model Card Contact
ldery@URL | [
"# Model Card for Model ID\n\nWe prune the Phi-2 (2.7B) model to 35% sparsty (1.8B) and then finetune on 100K 2048 length sequences from the C4 dataset (URL\nOur pruning algorithm is described in the paper Everybody Prune Now: Structured Pruning of LLMs with only Forward Passes. \nCode for pruning algorithm can be found here .",
"## Model Details\nModel is derived from Pruning the Phi-2 Model",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: Lucio Dery, Steven Kolawole, Jean-François Kagy, Virginia Smith, Graham Neubig, Ameet Talwalkar\n- Model type: Decoder-only\n- Language(s) (NLP): English\n- License: MIT",
"### Model Sources [optional]\n\n\n\n- Repository: [URL\n- Paper [optional]: [URL",
"## Training Details",
"### Training Data\n\nFinetuned on 100K 2048 length sequences from the C4 dataset (URL",
"### Training Procedure \n\nFull fine-tuning.",
"#### Training Hyperparameters\n\nDistillation KL-Weight : 0.01\n\nLearning Rate : 1e-4\n\nBatch Size : 128\n\nOptimzer : AdamW\n\nWarmup Steps : 5",
"### License\n\nThe model is licensed under the MIT license.",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: NVIDIA A6000\n\nBibTeX:\n\n@misc{dery2024everybody,\n title={Everybody Prune Now: Structured Pruning of LLMs with only Forward Passes}, \n author={Lucio Dery and Steven Kolawole and Jean-Francois Kagey and Virginia Smith and Graham Neubig and Ameet Talwalkar},\n year={2024},\n eprint={2402.05406},\n archivePrefix={arXiv},\n primaryClass={cs.LG}\n}",
"## Model Card Authors [optional]\n\nLucio Dery: ldery@URL",
"## Model Card Contact\n\nldery@URL"
] | [
"TAGS\n#transformers #safetensors #phi #Structured Pruning #Phi-2 #Memory-efficient Pruning #custom_code #en #arxiv-2402.05406 #arxiv-1910.09700 #license-mit #endpoints_compatible #region-us \n",
"# Model Card for Model ID\n\nWe prune the Phi-2 (2.7B) model to 35% sparsty (1.8B) and then finetune on 100K 2048 length sequences from the C4 dataset (URL\nOur pruning algorithm is described in the paper Everybody Prune Now: Structured Pruning of LLMs with only Forward Passes. \nCode for pruning algorithm can be found here .",
"## Model Details\nModel is derived from Pruning the Phi-2 Model",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: Lucio Dery, Steven Kolawole, Jean-François Kagy, Virginia Smith, Graham Neubig, Ameet Talwalkar\n- Model type: Decoder-only\n- Language(s) (NLP): English\n- License: MIT",
"### Model Sources [optional]\n\n\n\n- Repository: [URL\n- Paper [optional]: [URL",
"## Training Details",
"### Training Data\n\nFinetuned on 100K 2048 length sequences from the C4 dataset (URL",
"### Training Procedure \n\nFull fine-tuning.",
"#### Training Hyperparameters\n\nDistillation KL-Weight : 0.01\n\nLearning Rate : 1e-4\n\nBatch Size : 128\n\nOptimzer : AdamW\n\nWarmup Steps : 5",
"### License\n\nThe model is licensed under the MIT license.",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: NVIDIA A6000\n\nBibTeX:\n\n@misc{dery2024everybody,\n title={Everybody Prune Now: Structured Pruning of LLMs with only Forward Passes}, \n author={Lucio Dery and Steven Kolawole and Jean-Francois Kagey and Virginia Smith and Graham Neubig and Ameet Talwalkar},\n year={2024},\n eprint={2402.05406},\n archivePrefix={arXiv},\n primaryClass={cs.LG}\n}",
"## Model Card Authors [optional]\n\nLucio Dery: ldery@URL",
"## Model Card Contact\n\nldery@URL"
] | [
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"passage: TAGS\n#transformers #safetensors #phi #Structured Pruning #Phi-2 #Memory-efficient Pruning #custom_code #en #arxiv-2402.05406 #arxiv-1910.09700 #license-mit #endpoints_compatible #region-us \n# Model Card for Model ID\n\nWe prune the Phi-2 (2.7B) model to 35% sparsty (1.8B) and then finetune on 100K 2048 length sequences from the C4 dataset (URL\nOur pruning algorithm is described in the paper Everybody Prune Now: Structured Pruning of LLMs with only Forward Passes. \nCode for pruning algorithm can be found here .## Model Details\nModel is derived from Pruning the Phi-2 Model### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: Lucio Dery, Steven Kolawole, Jean-François Kagy, Virginia Smith, Graham Neubig, Ameet Talwalkar\n- Model type: Decoder-only\n- Language(s) (NLP): English\n- License: MIT### Model Sources [optional]\n\n\n\n- Repository: [URL\n- Paper [optional]: [URL## Training Details### Training Data\n\nFinetuned on 100K 2048 length sequences from the C4 dataset (URL### Training Procedure \n\nFull fine-tuning.#### Training Hyperparameters\n\nDistillation KL-Weight : 0.01\n\nLearning Rate : 1e-4\n\nBatch Size : 128\n\nOptimzer : AdamW\n\nWarmup Steps : 5### License\n\nThe model is licensed under the MIT license."
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null | null | peft |
# 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. -->
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## Environmental Impact
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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]
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### Framework versions
- PEFT 0.8.2 | {"library_name": "peft", "base_model": "NousResearch/Llama-2-7b-hf"} | null | najju/LLama2-sign-to-read-psl-13b | [
"peft",
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"1910.09700"
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# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
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- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
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APA:
## Glossary [optional]
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null | null | diffusers | This is a Microsoft Olive optimized ONNX version of the model found here: https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0 | {"library_name": "diffusers", "tags": ["unpaint", "stable_diffusion_model", "stable-diffusion", "onnx"], "pipeline_tag": "text-to-image", "model_description": [{"repo": "stabilityai/stable-diffusion-xl-base-1.0"}]} | text-to-image | axodoxian/stable_diffusion_xl_base_onnx | [
"diffusers",
"onnx",
"unpaint",
"stable_diffusion_model",
"stable-diffusion",
"text-to-image",
"diffusers:ORTStableDiffusionXLPipeline",
"region:us"
] | 2024-02-11T23:58:38+00:00 | [] | [] | TAGS
#diffusers #onnx #unpaint #stable_diffusion_model #stable-diffusion #text-to-image #diffusers-ORTStableDiffusionXLPipeline #region-us
| This is a Microsoft Olive optimized ONNX version of the model found here: URL | [] | [
"TAGS\n#diffusers #onnx #unpaint #stable_diffusion_model #stable-diffusion #text-to-image #diffusers-ORTStableDiffusionXLPipeline #region-us \n"
] | [
55
] | [
"passage: TAGS\n#diffusers #onnx #unpaint #stable_diffusion_model #stable-diffusion #text-to-image #diffusers-ORTStableDiffusionXLPipeline #region-us \n"
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null | null | transformers |
<!-- 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. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2469
- Accuracy: 0.9383
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9843 | 0.99 | 43 | 0.8500 | 0.6948 |
| 0.5335 | 2.0 | 87 | 0.5584 | 0.7825 |
| 0.4263 | 2.99 | 130 | 0.4791 | 0.8117 |
| 0.3308 | 4.0 | 174 | 0.4269 | 0.8344 |
| 0.2882 | 4.99 | 217 | 0.3567 | 0.8636 |
| 0.2517 | 6.0 | 261 | 0.3317 | 0.8701 |
| 0.1908 | 6.99 | 304 | 0.3082 | 0.8815 |
| 0.187 | 8.0 | 348 | 0.3230 | 0.8799 |
| 0.1434 | 8.99 | 391 | 0.3323 | 0.9010 |
| 0.1277 | 10.0 | 435 | 0.2489 | 0.9075 |
| 0.156 | 10.99 | 478 | 0.3246 | 0.8880 |
| 0.0781 | 12.0 | 522 | 0.3121 | 0.9010 |
| 0.1001 | 12.99 | 565 | 0.2708 | 0.9058 |
| 0.0892 | 14.0 | 609 | 0.2582 | 0.9140 |
| 0.0644 | 14.99 | 652 | 0.2486 | 0.9221 |
| 0.0689 | 16.0 | 696 | 0.2465 | 0.9237 |
| 0.0547 | 16.99 | 739 | 0.2402 | 0.9334 |
| 0.0597 | 18.0 | 783 | 0.2534 | 0.9237 |
| 0.0512 | 18.99 | 826 | 0.2400 | 0.9318 |
| 0.041 | 20.0 | 870 | 0.2397 | 0.9286 |
| 0.0376 | 20.99 | 913 | 0.2663 | 0.9269 |
| 0.0412 | 22.0 | 957 | 0.3026 | 0.9221 |
| 0.0423 | 22.99 | 1000 | 0.2678 | 0.9302 |
| 0.0266 | 24.0 | 1044 | 0.2510 | 0.9318 |
| 0.0313 | 24.99 | 1087 | 0.2542 | 0.9334 |
| 0.0207 | 26.0 | 1131 | 0.2743 | 0.9334 |
| 0.0292 | 26.99 | 1174 | 0.2614 | 0.9318 |
| 0.0242 | 28.0 | 1218 | 0.2469 | 0.9383 |
| 0.0201 | 28.99 | 1261 | 0.2534 | 0.9367 |
| 0.0354 | 29.66 | 1290 | 0.2525 | 0.9367 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/swin-tiny-patch4-window7-224", "model-index": [{"name": "swin-tiny-patch4-window7-224-finetuned-eurosat", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9383116883116883, "name": "Accuracy"}]}]}]} | image-classification | 0xhzx/swin-tiny-patch4-window7-224-finetuned-eurosat | [
"transformers",
"tensorboard",
"safetensors",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/swin-tiny-patch4-window7-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-12T00:02:59+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #swin #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/swin-tiny-patch4-window7-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| swin-tiny-patch4-window7-224-finetuned-eurosat
==============================================
This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the imagefolder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2469
* Accuracy: 0.9383
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: 32
* eval\_batch\_size: 32
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 30
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.1.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 30",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #swin #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/swin-tiny-patch4-window7-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 30",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
88,
144,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #swin #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/swin-tiny-patch4-window7-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 30### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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null | null | stable-baselines3 |
# **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 AstridsN -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 AstridsN -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 AstridsN
```
## 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', 8),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 100000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
| {"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": "252.50 +/- 17.92", "name": "mean_reward", "verified": false}]}]}]} | reinforcement-learning | AstridsN/dqn-SpaceInvadersNoFrameskip-v4 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2024-02-12T00:03:31+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL 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: URL
SB3: URL
SB3 Contrib: URL
Install the RL Zoo (with SB3 and SB3-Contrib):
If you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:
## Training (with the RL Zoo)
## Hyperparameters
# Environment Arguments
| [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.",
"## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:",
"## Training (with the RL Zoo)",
"## Hyperparameters",
"# Environment Arguments"
] | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.",
"## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:",
"## Training (with the RL Zoo)",
"## Hyperparameters",
"# Environment Arguments"
] | [
43,
90,
73,
9,
5,
7
] | [
"passage: TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:## Training (with the RL Zoo)## Hyperparameters# Environment Arguments"
] | [
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null | null | peft |
# Model Card for Model ID
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#### 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]
### Framework versions
- PEFT 0.8.2 | {"library_name": "peft", "base_model": "NousResearch/Llama-2-7b-hf"} | null | najju/LLama2-sign-to-read-psl-7b | [
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# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
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### Testing Data, Factors & Metrics
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### Results
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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null | null | transformers |
<!-- 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. -->
# bart-with-pubmed-noise-data-0.1
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1956
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.3905 | 0.04 | 500 | 0.3471 |
| 0.3136 | 0.07 | 1000 | 0.3261 |
| 0.2971 | 0.11 | 1500 | 0.2971 |
| 0.3361 | 0.14 | 2000 | 0.2788 |
| 0.2502 | 0.18 | 2500 | 0.2780 |
| 0.2613 | 0.21 | 3000 | 0.2690 |
| 0.2683 | 0.25 | 3500 | 0.2591 |
| 0.2995 | 0.29 | 4000 | 0.2539 |
| 0.2317 | 0.32 | 4500 | 0.2481 |
| 0.2361 | 0.36 | 5000 | 0.2453 |
| 0.2523 | 0.39 | 5500 | 0.2440 |
| 0.236 | 0.43 | 6000 | 0.2391 |
| 0.2301 | 0.46 | 6500 | 0.2372 |
| 0.2259 | 0.5 | 7000 | 0.2328 |
| 0.2231 | 0.54 | 7500 | 0.2344 |
| 0.2098 | 0.57 | 8000 | 0.2285 |
| 0.2663 | 0.61 | 8500 | 0.2220 |
| 0.2139 | 0.64 | 9000 | 0.2265 |
| 0.2372 | 0.68 | 9500 | 0.2204 |
| 0.1946 | 0.71 | 10000 | 0.2213 |
| 0.1843 | 0.75 | 10500 | 0.2214 |
| 0.1872 | 0.79 | 11000 | 0.2178 |
| 0.2182 | 0.82 | 11500 | 0.2127 |
| 0.2123 | 0.86 | 12000 | 0.2118 |
| 0.1865 | 0.89 | 12500 | 0.2113 |
| 0.1782 | 0.93 | 13000 | 0.2080 |
| 0.1894 | 0.96 | 13500 | 0.2053 |
| 0.1989 | 1.0 | 14000 | 0.2097 |
| 0.1721 | 1.03 | 14500 | 0.2083 |
| 0.1353 | 1.07 | 15000 | 0.2102 |
| 0.164 | 1.11 | 15500 | 0.2140 |
| 0.1541 | 1.14 | 16000 | 0.2086 |
| 0.1421 | 1.18 | 16500 | 0.2112 |
| 0.1752 | 1.21 | 17000 | 0.2085 |
| 0.1452 | 1.25 | 17500 | 0.2105 |
| 0.1836 | 1.28 | 18000 | 0.2066 |
| 0.1444 | 1.32 | 18500 | 0.2083 |
| 0.1473 | 1.36 | 19000 | 0.2090 |
| 0.1723 | 1.39 | 19500 | 0.2084 |
| 0.1328 | 1.43 | 20000 | 0.2042 |
| 0.1842 | 1.46 | 20500 | 0.2032 |
| 0.1934 | 1.5 | 21000 | 0.2031 |
| 0.1412 | 1.53 | 21500 | 0.2008 |
| 0.1302 | 1.57 | 22000 | 0.2003 |
| 0.142 | 1.61 | 22500 | 0.2008 |
| 0.1479 | 1.64 | 23000 | 0.2025 |
| 0.1628 | 1.68 | 23500 | 0.2005 |
| 0.1126 | 1.71 | 24000 | 0.2016 |
| 0.1515 | 1.75 | 24500 | 0.1985 |
| 0.1605 | 1.78 | 25000 | 0.1984 |
| 0.1659 | 1.82 | 25500 | 0.1970 |
| 0.1404 | 1.86 | 26000 | 0.1980 |
| 0.1386 | 1.89 | 26500 | 0.1972 |
| 0.1119 | 1.93 | 27000 | 0.1976 |
| 0.168 | 1.96 | 27500 | 0.1940 |
| 0.1318 | 2.0 | 28000 | 0.1958 |
| 0.1307 | 2.03 | 28500 | 0.1987 |
| 0.1312 | 2.07 | 29000 | 0.2012 |
| 0.1237 | 2.11 | 29500 | 0.2002 |
| 0.1339 | 2.14 | 30000 | 0.2010 |
| 0.1471 | 2.18 | 30500 | 0.1999 |
| 0.1195 | 2.21 | 31000 | 0.1998 |
| 0.1002 | 2.25 | 31500 | 0.2000 |
| 0.1009 | 2.28 | 32000 | 0.2012 |
| 0.1608 | 2.32 | 32500 | 0.1995 |
| 0.1198 | 2.36 | 33000 | 0.2009 |
| 0.1053 | 2.39 | 33500 | 0.1990 |
| 0.1399 | 2.43 | 34000 | 0.2001 |
| 0.1043 | 2.46 | 34500 | 0.1994 |
| 0.1254 | 2.5 | 35000 | 0.1996 |
| 0.0987 | 2.53 | 35500 | 0.1966 |
| 0.119 | 2.57 | 36000 | 0.1974 |
| 0.1167 | 2.61 | 36500 | 0.1983 |
| 0.1119 | 2.64 | 37000 | 0.1974 |
| 0.1391 | 2.68 | 37500 | 0.1973 |
| 0.1036 | 2.71 | 38000 | 0.1971 |
| 0.1203 | 2.75 | 38500 | 0.1976 |
| 0.1498 | 2.78 | 39000 | 0.1976 |
| 0.1037 | 2.82 | 39500 | 0.1975 |
| 0.1141 | 2.85 | 40000 | 0.1961 |
| 0.0935 | 2.89 | 40500 | 0.1960 |
| 0.0985 | 2.93 | 41000 | 0.1963 |
| 0.108 | 2.96 | 41500 | 0.1955 |
| 0.1054 | 3.0 | 42000 | 0.1956 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.2+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "facebook/bart-base", "model-index": [{"name": "bart-with-pubmed-noise-data-0.1", "results": []}]} | text2text-generation | gayanin/bart-with-pubmed-noise-data-0.1 | [
"transformers",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:facebook/bart-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-12T00:06:24+00:00 | [] | [] | TAGS
#transformers #safetensors #bart #text2text-generation #generated_from_trainer #base_model-facebook/bart-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bart-with-pubmed-noise-data-0.1
===============================
This model is a fine-tuned version of facebook/bart-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1956
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: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 10
* num\_epochs: 3
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.1.2+cu121
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
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"### Training results",
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"### Training results",
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] | [
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"passage: TAGS\n#transformers #safetensors #bart #text2text-generation #generated_from_trainer #base_model-facebook/bart-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 10\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.2+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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null | null | transformers |
<!-- 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. -->
# bart-with-woz-noise-data-0.1
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0710
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.2068 | 0.04 | 500 | 0.1735 |
| 0.1785 | 0.09 | 1000 | 0.1508 |
| 0.2136 | 0.13 | 1500 | 0.1359 |
| 0.1249 | 0.18 | 2000 | 0.1281 |
| 0.1114 | 0.22 | 2500 | 0.1180 |
| 0.1327 | 0.26 | 3000 | 0.1153 |
| 0.1603 | 0.31 | 3500 | 0.1065 |
| 0.1422 | 0.35 | 4000 | 0.1032 |
| 0.1166 | 0.39 | 4500 | 0.1019 |
| 0.1266 | 0.44 | 5000 | 0.1001 |
| 0.1087 | 0.48 | 5500 | 0.0996 |
| 0.1284 | 0.53 | 6000 | 0.0967 |
| 0.0919 | 0.57 | 6500 | 0.0938 |
| 0.0924 | 0.61 | 7000 | 0.0927 |
| 0.1124 | 0.66 | 7500 | 0.0913 |
| 0.0843 | 0.7 | 8000 | 0.0920 |
| 0.1012 | 0.74 | 8500 | 0.0881 |
| 0.1058 | 0.79 | 9000 | 0.0867 |
| 0.0894 | 0.83 | 9500 | 0.0867 |
| 0.0858 | 0.88 | 10000 | 0.0828 |
| 0.0991 | 0.92 | 10500 | 0.0867 |
| 0.0471 | 0.96 | 11000 | 0.0867 |
| 0.0663 | 1.01 | 11500 | 0.0833 |
| 0.0743 | 1.05 | 12000 | 0.0843 |
| 0.0821 | 1.09 | 12500 | 0.0835 |
| 0.0826 | 1.14 | 13000 | 0.0812 |
| 0.0943 | 1.18 | 13500 | 0.0809 |
| 0.0708 | 1.23 | 14000 | 0.0813 |
| 0.0902 | 1.27 | 14500 | 0.0791 |
| 0.051 | 1.31 | 15000 | 0.0822 |
| 0.0782 | 1.36 | 15500 | 0.0800 |
| 0.0802 | 1.4 | 16000 | 0.0777 |
| 0.0671 | 1.44 | 16500 | 0.0787 |
| 0.0872 | 1.49 | 17000 | 0.0776 |
| 0.091 | 1.53 | 17500 | 0.0766 |
| 0.0722 | 1.58 | 18000 | 0.0775 |
| 0.0539 | 1.62 | 18500 | 0.0754 |
| 0.067 | 1.66 | 19000 | 0.0754 |
| 0.0372 | 1.71 | 19500 | 0.0758 |
| 0.0838 | 1.75 | 20000 | 0.0763 |
| 0.0496 | 1.79 | 20500 | 0.0736 |
| 0.0542 | 1.84 | 21000 | 0.0744 |
| 0.0435 | 1.88 | 21500 | 0.0746 |
| 0.0568 | 1.93 | 22000 | 0.0731 |
| 0.0521 | 1.97 | 22500 | 0.0713 |
| 0.0377 | 2.01 | 23000 | 0.0743 |
| 0.0277 | 2.06 | 23500 | 0.0747 |
| 0.0587 | 2.1 | 24000 | 0.0742 |
| 0.0345 | 2.14 | 24500 | 0.0748 |
| 0.0364 | 2.19 | 25000 | 0.0761 |
| 0.0524 | 2.23 | 25500 | 0.0737 |
| 0.0407 | 2.28 | 26000 | 0.0736 |
| 0.0425 | 2.32 | 26500 | 0.0730 |
| 0.044 | 2.36 | 27000 | 0.0734 |
| 0.0477 | 2.41 | 27500 | 0.0731 |
| 0.0382 | 2.45 | 28000 | 0.0732 |
| 0.0387 | 2.5 | 28500 | 0.0726 |
| 0.0459 | 2.54 | 29000 | 0.0731 |
| 0.0554 | 2.58 | 29500 | 0.0720 |
| 0.0348 | 2.63 | 30000 | 0.0727 |
| 0.0449 | 2.67 | 30500 | 0.0717 |
| 0.0386 | 2.71 | 31000 | 0.0720 |
| 0.0436 | 2.76 | 31500 | 0.0712 |
| 0.0345 | 2.8 | 32000 | 0.0720 |
| 0.0509 | 2.85 | 32500 | 0.0712 |
| 0.0402 | 2.89 | 33000 | 0.0710 |
| 0.055 | 2.93 | 33500 | 0.0711 |
| 0.0413 | 2.98 | 34000 | 0.0710 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.2+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "facebook/bart-base", "model-index": [{"name": "bart-with-woz-noise-data-0.1", "results": []}]} | text2text-generation | gayanin/bart-with-woz-noise-data-0.1 | [
"transformers",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:facebook/bart-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-12T00:10:03+00:00 | [] | [] | TAGS
#transformers #safetensors #bart #text2text-generation #generated_from_trainer #base_model-facebook/bart-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bart-with-woz-noise-data-0.1
============================
This model is a fine-tuned version of facebook/bart-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0710
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: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 10
* num\_epochs: 3
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.1.2+cu121
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
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"### Training results",
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.2+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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null | null | null |
# **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="paragrk1/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"])
```
| {"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}]}]}]} | reinforcement-learning | paragrk1/q-Taxi-v3 | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2024-02-12T00:10:39+00:00 | [] | [] | TAGS
#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us
|
# Q-Learning Agent playing1 Taxi-v3
This is a trained model of a Q-Learning agent playing Taxi-v3 .
## Usage
| [
"# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage"
] | [
"TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n",
"# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage"
] | [
32,
33
] | [
"passage: TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage"
] | [
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null | null | transformers |
<!-- 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. -->
# speecht5_tts_commonvoice_fa
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the common_voice_13_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4695
## 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.6592 | 1.59 | 250 | 0.5820 |
| 0.642 | 3.18 | 500 | 0.5502 |
| 0.5755 | 4.78 | 750 | 0.5452 |
| 0.5743 | 6.37 | 1000 | 0.5236 |
| 0.5542 | 7.96 | 1250 | 0.5206 |
| 0.5543 | 9.55 | 1500 | 0.5192 |
| 0.5266 | 11.15 | 1750 | 0.5011 |
| 0.5234 | 12.74 | 2000 | 0.4945 |
| 0.5139 | 14.33 | 2250 | 0.4873 |
| 0.5143 | 15.92 | 2500 | 0.4797 |
| 0.5051 | 17.52 | 2750 | 0.4811 |
| 0.4873 | 19.11 | 3000 | 0.4724 |
| 0.4922 | 20.7 | 3250 | 0.4681 |
| 0.4864 | 22.29 | 3500 | 0.4695 |
| 0.4769 | 23.89 | 3750 | 0.4702 |
| 0.4691 | 25.48 | 4000 | 0.4695 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["common_voice_13_0"], "base_model": "microsoft/speecht5_tts", "model-index": [{"name": "speecht5_tts_commonvoice_fa", "results": []}]} | text-to-audio | Farbod710/speecht5_tts_commonvoice_fa | [
"transformers",
"tensorboard",
"safetensors",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"dataset:common_voice_13_0",
"base_model:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2024-02-12T00:13:45+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #speecht5 #text-to-audio #generated_from_trainer #dataset-common_voice_13_0 #base_model-microsoft/speecht5_tts #license-mit #endpoints_compatible #region-us
| speecht5\_tts\_commonvoice\_fa
==============================
This model is a fine-tuned version of microsoft/speecht5\_tts on the common\_voice\_13\_0 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4695
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: 0.0001
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 4000
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 4000\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #speecht5 #text-to-audio #generated_from_trainer #dataset-common_voice_13_0 #base_model-microsoft/speecht5_tts #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 4000\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
] | [
77,
157,
4,
33
] | [
"passage: TAGS\n#transformers #tensorboard #safetensors #speecht5 #text-to-audio #generated_from_trainer #dataset-common_voice_13_0 #base_model-microsoft/speecht5_tts #license-mit #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 4000\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1"
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | question-answering | tareky/my-awesome-model-test | [
"transformers",
"safetensors",
"t5",
"question-answering",
"arxiv:1910.09700",
"endpoints_compatible",
"text-generation-inference",
"region:us"
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #t5 #question-answering #arxiv-1910.09700 #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- License:
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### Model Sources [optional]
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- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
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APA:
## Glossary [optional]
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null | null | transformers |
<!-- 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. -->
# wav2vec2-small-xls-r-nhi-colab2
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_16_1 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice_16_1"], "base_model": "facebook/wav2vec2-xls-r-300m", "model-index": [{"name": "wav2vec2-small-xls-r-nhi-colab2", "results": []}]} | automatic-speech-recognition | plesniar/wav2vec2-small-xls-r-nhi-colab2 | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice_16_1",
"base_model:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-02-12T00:25:30+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_16_1 #base_model-facebook/wav2vec2-xls-r-300m #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-small-xls-r-nhi-colab2
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice_16_1 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| [
"# wav2vec2-small-xls-r-nhi-colab2\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice_16_1 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0003\n- train_batch_size: 16\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 30\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_16_1 #base_model-facebook/wav2vec2-xls-r-300m #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-small-xls-r-nhi-colab2\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice_16_1 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0003\n- train_batch_size: 16\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 30\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
] | [
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140,
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"passage: TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_16_1 #base_model-facebook/wav2vec2-xls-r-300m #license-apache-2.0 #endpoints_compatible #region-us \n# wav2vec2-small-xls-r-nhi-colab2\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice_16_1 dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0003\n- train_batch_size: 16\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 30\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
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null | null | transformers |
<!-- 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. -->
# XLM_RoBERTa-Hate-Speech-Detection-8-langs-new
This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5381
- Micro F1: 0.9252
- Macro F1: 0.9237
- Accuracy: 0.9252
## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
### Framework versions
- Transformers 4.36.1
- Pytorch 2.1.0+cu121
- Datasets 2.13.1
- Tokenizers 0.15.0
| {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "xlm-roberta-large", "model-index": [{"name": "XLM_RoBERTa-Hate-Speech-Detection-8-langs-new", "results": []}]} | text-classification | christinacdl/XLM_RoBERTa-Hate-Speech-Detection-8-langs-new | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:xlm-roberta-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-12T00:27:31+00:00 | [] | [] | TAGS
#transformers #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-xlm-roberta-large #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# XLM_RoBERTa-Hate-Speech-Detection-8-langs-new
This model is a fine-tuned version of xlm-roberta-large on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5381
- Micro F1: 0.9252
- Macro F1: 0.9237
- Accuracy: 0.9252
## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
### Framework versions
- Transformers 4.36.1
- Pytorch 2.1.0+cu121
- Datasets 2.13.1
- Tokenizers 0.15.0
| [
"# XLM_RoBERTa-Hate-Speech-Detection-8-langs-new\n\nThis model is a fine-tuned version of xlm-roberta-large on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.5381\n- Micro F1: 0.9252\n- Macro F1: 0.9237\n- Accuracy: 0.9252",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 4",
"### Training results",
"### Framework versions\n\n- Transformers 4.36.1\n- Pytorch 2.1.0+cu121\n- Datasets 2.13.1\n- Tokenizers 0.15.0"
] | [
"TAGS\n#transformers #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-xlm-roberta-large #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLM_RoBERTa-Hate-Speech-Detection-8-langs-new\n\nThis model is a fine-tuned version of xlm-roberta-large on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.5381\n- Micro F1: 0.9252\n- Macro F1: 0.9237\n- Accuracy: 0.9252",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 4",
"### Training results",
"### Framework versions\n\n- Transformers 4.36.1\n- Pytorch 2.1.0+cu121\n- Datasets 2.13.1\n- Tokenizers 0.15.0"
] | [
66,
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"passage: TAGS\n#transformers #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-xlm-roberta-large #license-mit #autotrain_compatible #endpoints_compatible #region-us \n# XLM_RoBERTa-Hate-Speech-Detection-8-langs-new\n\nThis model is a fine-tuned version of xlm-roberta-large on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.5381\n- Micro F1: 0.9252\n- Macro F1: 0.9237\n- Accuracy: 0.9252## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 4### Training results### Framework versions\n\n- Transformers 4.36.1\n- Pytorch 2.1.0+cu121\n- Datasets 2.13.1\n- Tokenizers 0.15.0"
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null | null | transformers |
<!-- 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. -->
# test-trainer
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) 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: 5e-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: 3.0
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "bert-base-cased", "model-index": [{"name": "test-trainer", "results": []}]} | text-classification | Khaledai/test-trainer | [
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-02-12T00:27:41+00:00 | [] | [] | TAGS
#transformers #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-base-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# test-trainer
This model is a fine-tuned version of bert-base-cased 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: 5e-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: 3.0
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
| [
"# test-trainer\n\nThis model is a fine-tuned version of bert-base-cased on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3.0",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
] | [
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"# test-trainer\n\nThis model is a fine-tuned version of bert-base-cased on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3.0",
"### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
] | [
63,
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"passage: TAGS\n#transformers #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-base-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# test-trainer\n\nThis model is a fine-tuned version of bert-base-cased on an unknown dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3.0### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1"
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null | null | null |
# Model Card for 3D Diffuser Actor
<!-- Provide a quick summary of what the model is/does. -->
A robot manipulation policy that marries diffusion modeling with 3D scene representations.
3D Diffuser Actor is trained and evaluated on [RLBench](https://github.com/stepjam/RLBench) or [CALVIN](https://github.com/mees/calvin) simulation.
We release all code, checkpoints, and details involved in training these models.
## Model Details
The models released are the following:
| Benchmark | Embedding dimension | Diffusion timestep |
|------|------|------|
| [RLBench (PerAct)](https://huggingface.co/katefgroup/3d_diffuser_actor/blob/main/diffuser_actor_peract.pth) | 120 | 100 |
| [RLBench (GNFactor)](https://huggingface.co/katefgroup/3d_diffuser_actor/blob/main/diffuser_actor_gnfactor.pth) | 120| 100 |
| [CALVIN](https://huggingface.co/katefgroup/3d_diffuser_actor/blob/main/diffuser_actor_calvin.pth) | 192 | 25 |
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** Katerina Group at CMU
- **Model type:** a Diffusion model with 3D scene
- **License:** The code and model are released under MIT License
- **Contact:** [email protected]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Project Page:** https://3d-diffuser-actor.github.io
- **Repository:** https://github.com/nickgkan/3d_diffuser_actor.git
- **Paper:** [Link]()
## 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. -->
### Input format
3D Diffuser Actor takes the following inputs:
1. `RGB observations`: a tensor of shape (batch_size, num_cameras, 3, H, W). The pixel values are in the range of [0, 1]
2. `Point cloud observation`: a tensor of shape (batch_size, num_cameras, 3, H, W).
3. `Instruction encodings`: a tensor of shape (batch_size, max_instruction_length, C). In this code base, the embedding dimension `C` is set to 512.
4. `curr_gripper`: a tensor of shape (batch_size, history_length, 7), where the last channel denotes xyz-action (3D) and quarternion (4D).
5. `trajectory_mask`: a tensor of shape (batch_size, trajectory_length), which is only used to indicate the length of each trajectory. To predict keyposes, we just need to set its shape to (batch_size, 1).
6. `gt_trajectory`: a tensor of shape (batch_size, trajectory_length, 7), where the last channel denotes xyz-action (3D) and quarternion (4D). The input is only used during training.
### Output format
The model returns the diffusion loss, when `run_inference=False`, otherwise, it returns pose trajectory of shape (batch_size, trajectory_length, 8) when `run_inference=True`.
### Usage
For training, forward 3D Diffuser Actor with `run_inference=False`
```
> loss = model.forward(gt_trajectory,
trajectory_mask,
rgb_obs,
pcd_obs,
instruction,
curr_gripper,
run_inference=False)
```
For evaluation, forward 3D Diffuser Actor with `run_inference=True`
```
> fake_gt_trajectory = torch.full((1, trajectory_length, 7), 0).to(device)
> trajectory_mask = torch.full((1, trajectory_length), False).to(device)
> trajectory = model.forward(fake_gt_trajectory,
trajectory_mask,
rgb_obs,
pcd_obs,
instruction,
curr_gripper,
run_inference=True)
```
Or you can forward the model with `compute_trajectory` function
```
> trajectory_mask = torch.full((1, trajectory_length), False).to(device)
> trajectory = model.compute_trajectory(trajectory_mask,
rgb_obs,
pcd_obs,
instruction,
curr_gripper)
```
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
Our model trained and evaluated on RLBench simulation with the PerAct setup:
| RLBench (PerAct) | 3D Diffuser Actor | [RVT](https://github.com/NVlabs/RVT) |
| --------------------------------- | -------- | -------- |
| average | 81.3 | 62.9 |
| open drawer | 89.6 | 71.2 |
| slide block | 97.6 | 81.6 |
| sweep to dustpan | 84.0 | 72.0 |
| meat off grill | 96.8 | 88 |
| turn tap | 99.2 | 93.6 |
| put in drawer | 96.0 | 88.0 |
| close jar | 96.0 | 52.0 |
| drag stick | 100.0 | 99.2 |
| stack blocks | 68.3 | 28.8 |
| screw bulbs | 82.4 | 48.0 |
| put in safe | 97.6 | 91.2 |
| place wine | 93.6 | 91.0 |
| put in cupboard | 85.6 | 49.6 |
| sort shape | 44.0 | 36.0 |
| push buttons | 98.4 | 100.0 |
| insert peg | 65.6 | 11.2 |
| stack cups | 47.2 | 26.4 |
| place cups | 24.0 | 4.0 |
Our model trained and evaluated on RLBench simulation with the GNFactor setup:
| RLBench (PerAct) | 3D Diffuser Actor | [GNFactor](https://github.com/YanjieZe/GNFactor) |
| --------------------------------- | -------- | -------- |
| average | 78.4 | 31.7 |
| open drawer | 89.3 | 76.0 |
| sweep to dustpan | 894.7 | 25.0 |
| close jar | 82.7 | 25.3 |
| meat off grill | 88.0 | 57.3 |
| turn tap | 80.0 | 50.7 |
| slide block | 92.0 | 20.0 |
| put in drawer | 77.3 | 0.0 |
| drag stick | 98.7 | 37.3 |
| push buttons | 69.3 | 18.7 |
| stack blocks | 12.0 | 4.0 |
Our model trained and evaluated on CALVIN simulation (train with environment A, B, C and test on D):
| RLBench (PerAct) | 3D Diffuser Actor | [GR-1](https://gr1-manipulation.github.io/) | [SuSIE](https://rail-berkeley.github.io/susie/) |
| --------------------------------- | -------- | -------- | -------- |
| task 1 | 92.2 | 85.4 | 87.0 |
| task 2 | 78.7 | 71.2 | 69.0 |
| task 3 | 63.9 | 59.6 | 49.0 |
| task 4 | 51.2 | 49.7 | 38.0 |
| task 5 | 41.2 | 40.1 | 26.0 |
## 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:**
```
@article{,
title={Action Diffusion with 3D Scene Representations},
author={Ke, Tsung-Wei and Gkanatsios, Nikolaos and Fragkiadaki, Katerina}
journal={Preprint},
year={2024}
}
```
## Model Card Contact
For errors in this model card, contact Nikos or Tsung-Wei, {ngkanats, tsungwek} at andrew dot cmu dot edu. | {"language": ["en"], "license": "mit"} | null | katefgroup/3d_diffuser_actor | [
"en",
"license:mit",
"region:us"
] | 2024-02-12T00:33:42+00:00 | [] | [
"en"
] | TAGS
#en #license-mit #region-us
| Model Card for 3D Diffuser Actor
================================
A robot manipulation policy that marries diffusion modeling with 3D scene representations.
3D Diffuser Actor is trained and evaluated on RLBench or CALVIN simulation.
We release all code, checkpoints, and details involved in training these models.
Model Details
-------------
The models released are the following:
Benchmark: RLBench (PerAct), Embedding dimension: 120, Diffusion timestep: 100
Benchmark: RLBench (GNFactor), Embedding dimension: 120, Diffusion timestep: 100
Benchmark: CALVIN, Embedding dimension: 192, Diffusion timestep: 25
### Model Description
* Developed by: Katerina Group at CMU
* Model type: a Diffusion model with 3D scene
* License: The code and model are released under MIT License
* Contact: ngkanats@URL
### Model Sources [optional]
* Project Page: URL
* Repository: URL
* Paper: Link
Uses
----
### Input format
3D Diffuser Actor takes the following inputs:
1. 'RGB observations': a tensor of shape (batch\_size, num\_cameras, 3, H, W). The pixel values are in the range of [0, 1]
2. 'Point cloud observation': a tensor of shape (batch\_size, num\_cameras, 3, H, W).
3. 'Instruction encodings': a tensor of shape (batch\_size, max\_instruction\_length, C). In this code base, the embedding dimension 'C' is set to 512.
4. 'curr\_gripper': a tensor of shape (batch\_size, history\_length, 7), where the last channel denotes xyz-action (3D) and quarternion (4D).
5. 'trajectory\_mask': a tensor of shape (batch\_size, trajectory\_length), which is only used to indicate the length of each trajectory. To predict keyposes, we just need to set its shape to (batch\_size, 1).
6. 'gt\_trajectory': a tensor of shape (batch\_size, trajectory\_length, 7), where the last channel denotes xyz-action (3D) and quarternion (4D). The input is only used during training.
### Output format
The model returns the diffusion loss, when 'run\_inference=False', otherwise, it returns pose trajectory of shape (batch\_size, trajectory\_length, 8) when 'run\_inference=True'.
### Usage
For training, forward 3D Diffuser Actor with 'run\_inference=False'
For evaluation, forward 3D Diffuser Actor with 'run\_inference=True'
Or you can forward the model with 'compute\_trajectory' function
Evaluation
----------
Our model trained and evaluated on RLBench simulation with the PerAct setup:
RLBench (PerAct): average, 3D Diffuser Actor: 81.3, RVT: 62.9
RLBench (PerAct): open drawer, 3D Diffuser Actor: 89.6, RVT: 71.2
RLBench (PerAct): slide block, 3D Diffuser Actor: 97.6, RVT: 81.6
RLBench (PerAct): sweep to dustpan, 3D Diffuser Actor: 84.0, RVT: 72.0
RLBench (PerAct): meat off grill, 3D Diffuser Actor: 96.8, RVT: 88
RLBench (PerAct): turn tap, 3D Diffuser Actor: 99.2, RVT: 93.6
RLBench (PerAct): put in drawer, 3D Diffuser Actor: 96.0, RVT: 88.0
RLBench (PerAct): close jar, 3D Diffuser Actor: 96.0, RVT: 52.0
RLBench (PerAct): drag stick, 3D Diffuser Actor: 100.0, RVT: 99.2
RLBench (PerAct): stack blocks, 3D Diffuser Actor: 68.3, RVT: 28.8
RLBench (PerAct): screw bulbs, 3D Diffuser Actor: 82.4, RVT: 48.0
RLBench (PerAct): put in safe, 3D Diffuser Actor: 97.6, RVT: 91.2
RLBench (PerAct): place wine, 3D Diffuser Actor: 93.6, RVT: 91.0
RLBench (PerAct): put in cupboard, 3D Diffuser Actor: 85.6, RVT: 49.6
RLBench (PerAct): sort shape, 3D Diffuser Actor: 44.0, RVT: 36.0
RLBench (PerAct): push buttons, 3D Diffuser Actor: 98.4, RVT: 100.0
RLBench (PerAct): insert peg, 3D Diffuser Actor: 65.6, RVT: 11.2
RLBench (PerAct): stack cups, 3D Diffuser Actor: 47.2, RVT: 26.4
RLBench (PerAct): place cups, 3D Diffuser Actor: 24.0, RVT: 4.0
Our model trained and evaluated on RLBench simulation with the GNFactor setup:
RLBench (PerAct): average, 3D Diffuser Actor: 78.4, GNFactor: 31.7
RLBench (PerAct): open drawer, 3D Diffuser Actor: 89.3, GNFactor: 76.0
RLBench (PerAct): sweep to dustpan, 3D Diffuser Actor: 894.7, GNFactor: 25.0
RLBench (PerAct): close jar, 3D Diffuser Actor: 82.7, GNFactor: 25.3
RLBench (PerAct): meat off grill, 3D Diffuser Actor: 88.0, GNFactor: 57.3
RLBench (PerAct): turn tap, 3D Diffuser Actor: 80.0, GNFactor: 50.7
RLBench (PerAct): slide block, 3D Diffuser Actor: 92.0, GNFactor: 20.0
RLBench (PerAct): put in drawer, 3D Diffuser Actor: 77.3, GNFactor: 0.0
RLBench (PerAct): drag stick, 3D Diffuser Actor: 98.7, GNFactor: 37.3
RLBench (PerAct): push buttons, 3D Diffuser Actor: 69.3, GNFactor: 18.7
RLBench (PerAct): stack blocks, 3D Diffuser Actor: 12.0, GNFactor: 4.0
Our model trained and evaluated on CALVIN simulation (train with environment A, B, C and test on D):
[optional]
BibTeX:
Model Card Contact
------------------
For errors in this model card, contact Nikos or Tsung-Wei, {ngkanats, tsungwek} at andrew dot cmu dot edu.
| [
"### Model Description\n\n\n* Developed by: Katerina Group at CMU\n* Model type: a Diffusion model with 3D scene\n* License: The code and model are released under MIT License\n* Contact: ngkanats@URL",
"### Model Sources [optional]\n\n\n* Project Page: URL\n* Repository: URL\n* Paper: Link\n\n\nUses\n----",
"### Input format\n\n\n3D Diffuser Actor takes the following inputs:\n\n\n1. 'RGB observations': a tensor of shape (batch\\_size, num\\_cameras, 3, H, W). The pixel values are in the range of [0, 1]\n2. 'Point cloud observation': a tensor of shape (batch\\_size, num\\_cameras, 3, H, W).\n3. 'Instruction encodings': a tensor of shape (batch\\_size, max\\_instruction\\_length, C). In this code base, the embedding dimension 'C' is set to 512.\n4. 'curr\\_gripper': a tensor of shape (batch\\_size, history\\_length, 7), where the last channel denotes xyz-action (3D) and quarternion (4D).\n5. 'trajectory\\_mask': a tensor of shape (batch\\_size, trajectory\\_length), which is only used to indicate the length of each trajectory. To predict keyposes, we just need to set its shape to (batch\\_size, 1).\n6. 'gt\\_trajectory': a tensor of shape (batch\\_size, trajectory\\_length, 7), where the last channel denotes xyz-action (3D) and quarternion (4D). The input is only used during training.",
"### Output format\n\n\nThe model returns the diffusion loss, when 'run\\_inference=False', otherwise, it returns pose trajectory of shape (batch\\_size, trajectory\\_length, 8) when 'run\\_inference=True'.",
"### Usage\n\n\nFor training, forward 3D Diffuser Actor with 'run\\_inference=False'\n\n\nFor evaluation, forward 3D Diffuser Actor with 'run\\_inference=True'\n\n\nOr you can forward the model with 'compute\\_trajectory' function\n\n\nEvaluation\n----------\n\n\nOur model trained and evaluated on RLBench simulation with the PerAct setup:\n\n\nRLBench (PerAct): average, 3D Diffuser Actor: 81.3, RVT: 62.9\nRLBench (PerAct): open drawer, 3D Diffuser Actor: 89.6, RVT: 71.2\nRLBench (PerAct): slide block, 3D Diffuser Actor: 97.6, RVT: 81.6\nRLBench (PerAct): sweep to dustpan, 3D Diffuser Actor: 84.0, RVT: 72.0\nRLBench (PerAct): meat off grill, 3D Diffuser Actor: 96.8, RVT: 88\nRLBench (PerAct): turn tap, 3D Diffuser Actor: 99.2, RVT: 93.6\nRLBench (PerAct): put in drawer, 3D Diffuser Actor: 96.0, RVT: 88.0\nRLBench (PerAct): close jar, 3D Diffuser Actor: 96.0, RVT: 52.0\nRLBench (PerAct): drag stick, 3D Diffuser Actor: 100.0, RVT: 99.2\nRLBench (PerAct): stack blocks, 3D Diffuser Actor: 68.3, RVT: 28.8\nRLBench (PerAct): screw bulbs, 3D Diffuser Actor: 82.4, RVT: 48.0\nRLBench (PerAct): put in safe, 3D Diffuser Actor: 97.6, RVT: 91.2\nRLBench (PerAct): place wine, 3D Diffuser Actor: 93.6, RVT: 91.0\nRLBench (PerAct): put in cupboard, 3D Diffuser Actor: 85.6, RVT: 49.6\nRLBench (PerAct): sort shape, 3D Diffuser Actor: 44.0, RVT: 36.0\nRLBench (PerAct): push buttons, 3D Diffuser Actor: 98.4, RVT: 100.0\nRLBench (PerAct): insert peg, 3D Diffuser Actor: 65.6, RVT: 11.2\nRLBench (PerAct): stack cups, 3D Diffuser Actor: 47.2, RVT: 26.4\nRLBench (PerAct): place cups, 3D Diffuser Actor: 24.0, RVT: 4.0\n\n\nOur model trained and evaluated on RLBench simulation with the GNFactor setup:\n\n\nRLBench (PerAct): average, 3D Diffuser Actor: 78.4, GNFactor: 31.7\nRLBench (PerAct): open drawer, 3D Diffuser Actor: 89.3, GNFactor: 76.0\nRLBench (PerAct): sweep to dustpan, 3D Diffuser Actor: 894.7, GNFactor: 25.0\nRLBench (PerAct): close jar, 3D Diffuser Actor: 82.7, GNFactor: 25.3\nRLBench (PerAct): meat off grill, 3D Diffuser Actor: 88.0, GNFactor: 57.3\nRLBench (PerAct): turn tap, 3D Diffuser Actor: 80.0, GNFactor: 50.7\nRLBench (PerAct): slide block, 3D Diffuser Actor: 92.0, GNFactor: 20.0\nRLBench (PerAct): put in drawer, 3D Diffuser Actor: 77.3, GNFactor: 0.0\nRLBench (PerAct): drag stick, 3D Diffuser Actor: 98.7, GNFactor: 37.3\nRLBench (PerAct): push buttons, 3D Diffuser Actor: 69.3, GNFactor: 18.7\nRLBench (PerAct): stack blocks, 3D Diffuser Actor: 12.0, GNFactor: 4.0\n\n\nOur model trained and evaluated on CALVIN simulation (train with environment A, B, C and test on D):\n\n\n\n[optional]\n\n\nBibTeX:\n\n\nModel Card Contact\n------------------\n\n\nFor errors in this model card, contact Nikos or Tsung-Wei, {ngkanats, tsungwek} at andrew dot cmu dot edu."
] | [
"TAGS\n#en #license-mit #region-us \n",
"### Model Description\n\n\n* Developed by: Katerina Group at CMU\n* Model type: a Diffusion model with 3D scene\n* License: The code and model are released under MIT License\n* Contact: ngkanats@URL",
"### Model Sources [optional]\n\n\n* Project Page: URL\n* Repository: URL\n* Paper: Link\n\n\nUses\n----",
"### Input format\n\n\n3D Diffuser Actor takes the following inputs:\n\n\n1. 'RGB observations': a tensor of shape (batch\\_size, num\\_cameras, 3, H, W). The pixel values are in the range of [0, 1]\n2. 'Point cloud observation': a tensor of shape (batch\\_size, num\\_cameras, 3, H, W).\n3. 'Instruction encodings': a tensor of shape (batch\\_size, max\\_instruction\\_length, C). In this code base, the embedding dimension 'C' is set to 512.\n4. 'curr\\_gripper': a tensor of shape (batch\\_size, history\\_length, 7), where the last channel denotes xyz-action (3D) and quarternion (4D).\n5. 'trajectory\\_mask': a tensor of shape (batch\\_size, trajectory\\_length), which is only used to indicate the length of each trajectory. To predict keyposes, we just need to set its shape to (batch\\_size, 1).\n6. 'gt\\_trajectory': a tensor of shape (batch\\_size, trajectory\\_length, 7), where the last channel denotes xyz-action (3D) and quarternion (4D). The input is only used during training.",
"### Output format\n\n\nThe model returns the diffusion loss, when 'run\\_inference=False', otherwise, it returns pose trajectory of shape (batch\\_size, trajectory\\_length, 8) when 'run\\_inference=True'.",
"### Usage\n\n\nFor training, forward 3D Diffuser Actor with 'run\\_inference=False'\n\n\nFor evaluation, forward 3D Diffuser Actor with 'run\\_inference=True'\n\n\nOr you can forward the model with 'compute\\_trajectory' function\n\n\nEvaluation\n----------\n\n\nOur model trained and evaluated on RLBench simulation with the PerAct setup:\n\n\nRLBench (PerAct): average, 3D Diffuser Actor: 81.3, RVT: 62.9\nRLBench (PerAct): open drawer, 3D Diffuser Actor: 89.6, RVT: 71.2\nRLBench (PerAct): slide block, 3D Diffuser Actor: 97.6, RVT: 81.6\nRLBench (PerAct): sweep to dustpan, 3D Diffuser Actor: 84.0, RVT: 72.0\nRLBench (PerAct): meat off grill, 3D Diffuser Actor: 96.8, RVT: 88\nRLBench (PerAct): turn tap, 3D Diffuser Actor: 99.2, RVT: 93.6\nRLBench (PerAct): put in drawer, 3D Diffuser Actor: 96.0, RVT: 88.0\nRLBench (PerAct): close jar, 3D Diffuser Actor: 96.0, RVT: 52.0\nRLBench (PerAct): drag stick, 3D Diffuser Actor: 100.0, RVT: 99.2\nRLBench (PerAct): stack blocks, 3D Diffuser Actor: 68.3, RVT: 28.8\nRLBench (PerAct): screw bulbs, 3D Diffuser Actor: 82.4, RVT: 48.0\nRLBench (PerAct): put in safe, 3D Diffuser Actor: 97.6, RVT: 91.2\nRLBench (PerAct): place wine, 3D Diffuser Actor: 93.6, RVT: 91.0\nRLBench (PerAct): put in cupboard, 3D Diffuser Actor: 85.6, RVT: 49.6\nRLBench (PerAct): sort shape, 3D Diffuser Actor: 44.0, RVT: 36.0\nRLBench (PerAct): push buttons, 3D Diffuser Actor: 98.4, RVT: 100.0\nRLBench (PerAct): insert peg, 3D Diffuser Actor: 65.6, RVT: 11.2\nRLBench (PerAct): stack cups, 3D Diffuser Actor: 47.2, RVT: 26.4\nRLBench (PerAct): place cups, 3D Diffuser Actor: 24.0, RVT: 4.0\n\n\nOur model trained and evaluated on RLBench simulation with the GNFactor setup:\n\n\nRLBench (PerAct): average, 3D Diffuser Actor: 78.4, GNFactor: 31.7\nRLBench (PerAct): open drawer, 3D Diffuser Actor: 89.3, GNFactor: 76.0\nRLBench (PerAct): sweep to dustpan, 3D Diffuser Actor: 894.7, GNFactor: 25.0\nRLBench (PerAct): close jar, 3D Diffuser Actor: 82.7, GNFactor: 25.3\nRLBench (PerAct): meat off grill, 3D Diffuser Actor: 88.0, GNFactor: 57.3\nRLBench (PerAct): turn tap, 3D Diffuser Actor: 80.0, GNFactor: 50.7\nRLBench (PerAct): slide block, 3D Diffuser Actor: 92.0, GNFactor: 20.0\nRLBench (PerAct): put in drawer, 3D Diffuser Actor: 77.3, GNFactor: 0.0\nRLBench (PerAct): drag stick, 3D Diffuser Actor: 98.7, GNFactor: 37.3\nRLBench (PerAct): push buttons, 3D Diffuser Actor: 69.3, GNFactor: 18.7\nRLBench (PerAct): stack blocks, 3D Diffuser Actor: 12.0, GNFactor: 4.0\n\n\nOur model trained and evaluated on CALVIN simulation (train with environment A, B, C and test on D):\n\n\n\n[optional]\n\n\nBibTeX:\n\n\nModel Card Contact\n------------------\n\n\nFor errors in this model card, contact Nikos or Tsung-Wei, {ngkanats, tsungwek} at andrew dot cmu dot edu."
] | [
13,
48,
27,
321,
66,
1026
] | [
"passage: TAGS\n#en #license-mit #region-us \n### Model Description\n\n\n* Developed by: Katerina Group at CMU\n* Model type: a Diffusion model with 3D scene\n* License: The code and model are released under MIT License\n* Contact: ngkanats@URL### Model Sources [optional]\n\n\n* Project Page: URL\n* Repository: URL\n* Paper: Link\n\n\nUses\n----### Input format\n\n\n3D Diffuser Actor takes the following inputs:\n\n\n1. 'RGB observations': a tensor of shape (batch\\_size, num\\_cameras, 3, H, W). The pixel values are in the range of [0, 1]\n2. 'Point cloud observation': a tensor of shape (batch\\_size, num\\_cameras, 3, H, W).\n3. 'Instruction encodings': a tensor of shape (batch\\_size, max\\_instruction\\_length, C). In this code base, the embedding dimension 'C' is set to 512.\n4. 'curr\\_gripper': a tensor of shape (batch\\_size, history\\_length, 7), where the last channel denotes xyz-action (3D) and quarternion (4D).\n5. 'trajectory\\_mask': a tensor of shape (batch\\_size, trajectory\\_length), which is only used to indicate the length of each trajectory. To predict keyposes, we just need to set its shape to (batch\\_size, 1).\n6. 'gt\\_trajectory': a tensor of shape (batch\\_size, trajectory\\_length, 7), where the last channel denotes xyz-action (3D) and quarternion (4D). The input is only used during training.### Output format\n\n\nThe model returns the diffusion loss, when 'run\\_inference=False', otherwise, it returns pose trajectory of shape (batch\\_size, trajectory\\_length, 8) when 'run\\_inference=True'."
] | [
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null | null | transformers |
# AIFT-42dot_LLM-PLM-1.3B-ao-instruct-all-v0.91
베이스 모델 : 42dot/42dot_LLM-PLM-1.3B
학습 데이터 : 자체 제작한 Open Orca 스타일 데이터셋 약 48,000건 (중복 제거 및 데이터 분포 조정)
학습 방법 : Full finetuning
epoch : 3
## ko-lm-evaluation-harness(5-shot)
|kobest_boolq|kobest_copa|kobest_hellaswag|pawsx_ko|
|--|--|--|--|
|0.5220797720797721|0.72|0.458|0.563|
## Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.0.0
- Tokenizers 0.15.0 | {"license": "cc-by-nc-4.0"} | text-generation | mu0gum/AIFT-42dot_LLM-PLM-1.3B-ao-instruct-all-v0.91 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-12T00:36:29+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| AIFT-42dot\_LLM-PLM-1.3B-ao-instruct-all-v0.91
==============================================
베이스 모델 : 42dot/42dot\_LLM-PLM-1.3B
학습 데이터 : 자체 제작한 Open Orca 스타일 데이터셋 약 48,000건 (중복 제거 및 데이터 분포 조정)
학습 방법 : Full finetuning
epoch : 3
ko-lm-evaluation-harness(5-shot)
--------------------------------
Framework versions
------------------
* Transformers 4.36.2
* Pytorch 2.1.2+cu121
* Datasets 2.0.0
* Tokenizers 0.15.0
| [] | [
"TAGS\n#transformers #safetensors #llama #text-generation #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
58
] | [
"passage: TAGS\n#transformers #safetensors #llama #text-generation #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
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null | null | transformers |
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| {"library_name": "transformers", "tags": []} | feature-extraction | tommymarto/LernnaviBERT_baseline_students_answers_4096_mistral_seq_len_30 | [
"transformers",
"safetensors",
"bert",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #bert #feature-extraction #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
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## Uses
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## How to Get Started with the Model
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## Training Details
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### Training Procedure
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## Evaluation
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#### Factors
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
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null | null | transformers | # Model Card
## Summary
This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
- Base model: [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` library installed.
```bash
pip install transformers==4.36.1
```
Also make sure you are providing your huggingface token to the pipeline if the model is lying in a private repo.
- Either leave `token=True` in the `pipeline` and login to hugginface_hub by running
```python
import huggingface_hub
huggingface_hub.login(<ACCESS_TOKEN>)
```
- Or directly pass your <ACCESS_TOKEN> to `token` in the `pipeline`
```python
from transformers import pipeline
generate_text = pipeline(
model="Steflime/zephyr-esterno3",
torch_dtype="auto",
trust_remote_code=True,
use_fast=True,
device_map={"": "cuda:0"},
token=True,
)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.0),
repetition_penalty=float(1.0),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
```python
print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])
```
```bash
<|user|>Why is drinking water so healthy?</s><|assistant|>
```
Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`.
```python
from h2oai_pipeline import H2OTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"Steflime/zephyr-esterno3",
use_fast=True,
padding_side="left",
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
"Steflime/zephyr-esterno3",
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.0),
repetition_penalty=float(1.0),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Steflime/zephyr-esterno3" # either local folder or huggingface model name
# Important: The prompt needs to be in the same format the model was trained with.
# You can find an example prompt in the experiment logs.
prompt = "<|user|>How are you?</s><|assistant|>"
tokenizer = AutoTokenizer.from_pretrained(
model_name,
use_fast=True,
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
model.cuda().eval()
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
# generate configuration can be modified to your needs
tokens = model.generate(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.0),
repetition_penalty=float(1.0),
renormalize_logits=True
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)
```
## Quantization and sharding
You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```.
## Model Architecture
```
MistralForCausalLM(
(model): MistralModel(
(embed_tokens): Embedding(32000, 4096, padding_idx=2)
(layers): ModuleList(
(0-31): 32 x MistralDecoderLayer(
(self_attn): MistralAttention(
(q_proj): Linear(in_features=4096, out_features=4096, bias=False)
(k_proj): Linear(in_features=4096, out_features=1024, bias=False)
(v_proj): Linear(in_features=4096, out_features=1024, bias=False)
(o_proj): Linear(in_features=4096, out_features=4096, bias=False)
(rotary_emb): MistralRotaryEmbedding()
)
(mlp): MistralMLP(
(gate_proj): Linear(in_features=4096, out_features=14336, bias=False)
(up_proj): Linear(in_features=4096, out_features=14336, bias=False)
(down_proj): Linear(in_features=14336, out_features=4096, bias=False)
(act_fn): SiLU()
)
(input_layernorm): MistralRMSNorm()
(post_attention_layernorm): MistralRMSNorm()
)
)
(norm): MistralRMSNorm()
)
(lm_head): Linear(in_features=4096, out_features=32000, bias=False)
)
```
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it. | {"language": ["en"], "library_name": "transformers", "tags": ["gpt", "llm", "large language model", "h2o-llmstudio"], "inference": false, "thumbnail": "https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico"} | text-generation | Steflime/zephyrEsterno3 | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"gpt",
"llm",
"large language model",
"h2o-llmstudio",
"conversational",
"en",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-12T00:40:49+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #mistral #text-generation #gpt #llm #large language model #h2o-llmstudio #conversational #en #autotrain_compatible #text-generation-inference #region-us
| # Model Card
## Summary
This model was trained using H2O LLM Studio.
- Base model: HuggingFaceH4/zephyr-7b-beta
## Usage
To use the model with the 'transformers' library on a machine with GPUs, first make sure you have the 'transformers' library installed.
Also make sure you are providing your huggingface token to the pipeline if the model is lying in a private repo.
- Either leave 'token=True' in the 'pipeline' and login to hugginface_hub by running
- Or directly pass your <ACCESS_TOKEN> to 'token' in the 'pipeline'
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
Alternatively, you can download h2oai_pipeline.py, store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the 'transformers' package, this will allow you to set 'trust_remote_code=False'.
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
## Quantization and sharding
You can load the models using quantization by specifying or . Also, sharding on multiple GPUs is possible by setting .
## Model Architecture
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in URL. Visit H2O LLM Studio to learn how to train your own large language models.
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it. | [
"# Model Card",
"## Summary\n\nThis model was trained using H2O LLM Studio.\n- Base model: HuggingFaceH4/zephyr-7b-beta",
"## Usage\n\nTo use the model with the 'transformers' library on a machine with GPUs, first make sure you have the 'transformers' library installed.\n\n\n\nAlso make sure you are providing your huggingface token to the pipeline if the model is lying in a private repo.\n - Either leave 'token=True' in the 'pipeline' and login to hugginface_hub by running\n \n - Or directly pass your <ACCESS_TOKEN> to 'token' in the 'pipeline'\n\n\n\nYou can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:\n\n\n\n\n\nAlternatively, you can download h2oai_pipeline.py, store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the 'transformers' package, this will allow you to set 'trust_remote_code=False'.\n\n\n\n\nYou may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:",
"## Quantization and sharding\n\nYou can load the models using quantization by specifying or . Also, sharding on multiple GPUs is possible by setting .",
"## Model Architecture",
"## Model Configuration\n\nThis model was trained using H2O LLM Studio and with the configuration in URL. Visit H2O LLM Studio to learn how to train your own large language models.",
"## Disclaimer\n\nPlease read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.\n\n- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.\n- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.\n- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.\n- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.\n- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.\n- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.\n\nBy using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it."
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #gpt #llm #large language model #h2o-llmstudio #conversational #en #autotrain_compatible #text-generation-inference #region-us \n",
"# Model Card",
"## Summary\n\nThis model was trained using H2O LLM Studio.\n- Base model: HuggingFaceH4/zephyr-7b-beta",
"## Usage\n\nTo use the model with the 'transformers' library on a machine with GPUs, first make sure you have the 'transformers' library installed.\n\n\n\nAlso make sure you are providing your huggingface token to the pipeline if the model is lying in a private repo.\n - Either leave 'token=True' in the 'pipeline' and login to hugginface_hub by running\n \n - Or directly pass your <ACCESS_TOKEN> to 'token' in the 'pipeline'\n\n\n\nYou can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:\n\n\n\n\n\nAlternatively, you can download h2oai_pipeline.py, store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the 'transformers' package, this will allow you to set 'trust_remote_code=False'.\n\n\n\n\nYou may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:",
"## Quantization and sharding\n\nYou can load the models using quantization by specifying or . Also, sharding on multiple GPUs is possible by setting .",
"## Model Architecture",
"## Model Configuration\n\nThis model was trained using H2O LLM Studio and with the configuration in URL. Visit H2O LLM Studio to learn how to train your own large language models.",
"## Disclaimer\n\nPlease read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.\n\n- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.\n- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.\n- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.\n- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.\n- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.\n- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.\n\nBy using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it."
] | [
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"passage: TAGS\n#transformers #safetensors #mistral #text-generation #gpt #llm #large language model #h2o-llmstudio #conversational #en #autotrain_compatible #text-generation-inference #region-us \n# Model Card## Summary\n\nThis model was trained using H2O LLM Studio.\n- Base model: HuggingFaceH4/zephyr-7b-beta## Usage\n\nTo use the model with the 'transformers' library on a machine with GPUs, first make sure you have the 'transformers' library installed.\n\n\n\nAlso make sure you are providing your huggingface token to the pipeline if the model is lying in a private repo.\n - Either leave 'token=True' in the 'pipeline' and login to hugginface_hub by running\n \n - Or directly pass your <ACCESS_TOKEN> to 'token' in the 'pipeline'\n\n\n\nYou can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:\n\n\n\n\n\nAlternatively, you can download h2oai_pipeline.py, store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the 'transformers' package, this will allow you to set 'trust_remote_code=False'.\n\n\n\n\nYou may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:## Quantization and sharding\n\nYou can load the models using quantization by specifying or . Also, sharding on multiple GPUs is possible by setting .## Model Architecture## Model Configuration\n\nThis model was trained using H2O LLM Studio and with the configuration in URL. Visit H2O LLM Studio to learn how to train your own large language models."
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null | null | transformers |
# Model Card for Model ID
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## Model Details
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| {"library_name": "transformers", "tags": []} | text-generation | FINNUMBER/Yi-Ko-6B-Finch-100-16 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
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## Uses
### Direct Use
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### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
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#### Software
[optional]
BibTeX:
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## Glossary [optional]
## More Information [optional]
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## Model Card Contact
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"passage: TAGS\n#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact"
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null | null | diffusers | This is a Microsoft Olive optimized ONNX version of the model found here: https://huggingface.co/stabilityai/stablediffusionapi/protovision-xl | {"library_name": "diffusers", "tags": ["unpaint", "stable_diffusion_model", "stable-diffusion", "onnx"], "pipeline_tag": "text-to-image", "model_description": [{"repo": "stablediffusionapi/protovision-xl"}]} | text-to-image | axodoxian/protovision_xl_onnx | [
"diffusers",
"onnx",
"unpaint",
"stable_diffusion_model",
"stable-diffusion",
"text-to-image",
"diffusers:ORTStableDiffusionXLPipeline",
"region:us"
] | 2024-02-12T00:45:51+00:00 | [] | [] | TAGS
#diffusers #onnx #unpaint #stable_diffusion_model #stable-diffusion #text-to-image #diffusers-ORTStableDiffusionXLPipeline #region-us
| This is a Microsoft Olive optimized ONNX version of the model found here: URL | [] | [
"TAGS\n#diffusers #onnx #unpaint #stable_diffusion_model #stable-diffusion #text-to-image #diffusers-ORTStableDiffusionXLPipeline #region-us \n"
] | [
55
] | [
"passage: TAGS\n#diffusers #onnx #unpaint #stable_diffusion_model #stable-diffusion #text-to-image #diffusers-ORTStableDiffusionXLPipeline #region-us \n"
] | [
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null | null | transformers |
# Model Card for Model ID
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## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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| {"library_name": "transformers", "tags": []} | text-generation | FINNUMBER/Yi-Ko-6B-Finch-400-16 | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
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:
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- Language(s) (NLP):
- License:
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### Model Sources [optional]
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- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
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APA:
## Glossary [optional]
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | null | PsychicMoon/zephyr-everything-llm-superbowl-200 | [
"transformers",
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"1910.09700"
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#transformers #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
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This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- Repository:
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- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
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] |
null | null | transformers |
# haLLAwa
haLLAwa is a merge of the following models using [mergekit](https://github.com/cg123/mergekit):
* [openchat/openchat-3.5-0106](https://huggingface.co/openchat/openchat-3.5-0106)
* [machinists/Mistral-7B-SQL](https://huggingface.co/machinists/Mistral-7B-SQL)
## 🧩 Configuration
\```yaml
slices:
- sources:
- model: openchat/openchat-3.5-0106
layer_range: [0, 32]
- model: machinists/Mistral-7B-SQL
layer_range: [0, 32]
merge_method: slerp
base_model: openchat/openchat-3.5-0106
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
\``` | {"license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "openchat/openchat-3.5-0106", "machinists/Mistral-7B-SQL"]} | text-generation | AbacusResearch/haLLAwa | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"openchat/openchat-3.5-0106",
"machinists/Mistral-7B-SQL",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-12T00:51:29+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #openchat/openchat-3.5-0106 #machinists/Mistral-7B-SQL #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# haLLAwa
haLLAwa is a merge of the following models using mergekit:
* openchat/openchat-3.5-0106
* machinists/Mistral-7B-SQL
## Configuration
\ | [
"# haLLAwa\n\nhaLLAwa is a merge of the following models using mergekit:\n* openchat/openchat-3.5-0106\n* machinists/Mistral-7B-SQL",
"## Configuration\n\n\\"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #openchat/openchat-3.5-0106 #machinists/Mistral-7B-SQL #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# haLLAwa\n\nhaLLAwa is a merge of the following models using mergekit:\n* openchat/openchat-3.5-0106\n* machinists/Mistral-7B-SQL",
"## Configuration\n\n\\"
] | [
89,
41,
6
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #openchat/openchat-3.5-0106 #machinists/Mistral-7B-SQL #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# haLLAwa\n\nhaLLAwa is a merge of the following models using mergekit:\n* openchat/openchat-3.5-0106\n* machinists/Mistral-7B-SQL## Configuration\n\n\\"
] | [
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] |
null | null | gguf | GGUF importance matrix (imatrix) quants for https://huggingface.co/allenai/tulu-2-dpo-70b
The importance matrix was trained for 100K tokens (200 batches of 512 tokens) using wiki.train.raw.
| Layers | Context | Template |
| --- | --- | --- |
| <pre>80</pre> | <pre>8192</pre> | <pre><\|user\|><br>{prompt}<br><\|assistant\|><br>{response}</pre> | | {"license": "other", "library_name": "gguf", "license_name": "ai2-impact-license-low-risk", "license_link": "https://allenai.org/impact-license", "pipeline_tag": "text-generation"} | text-generation | dranger003/tulu-2-dpo-70b-iMat.GGUF | [
"gguf",
"text-generation",
"license:other",
"region:us"
] | 2024-02-12T00:58:06+00:00 | [] | [] | TAGS
#gguf #text-generation #license-other #region-us
| GGUF importance matrix (imatrix) quants for URL
The importance matrix was trained for 100K tokens (200 batches of 512 tokens) using URL.
Layers:
```
80
```
, Context:
```
8192
```
, Template:
```
<|user|>
{prompt}
<|assistant|>
{response}
```
| [] | [
"TAGS\n#gguf #text-generation #license-other #region-us \n"
] | [
19
] | [
"passage: TAGS\n#gguf #text-generation #license-other #region-us \n"
] | [
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null | null | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | text-generation | Lostkyd/llama-2-7b-docunstruc | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
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"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
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- Developed by:
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## Uses
### Direct Use
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### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
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## Technical Specifications [optional]
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### Compute Infrastructure
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APA:
## Glossary [optional]
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null | null | null | # torchtune research repo: token coloring (colorful llama)
Playground to try out [token coloring](https://docs.google.com/document/d/1Win9vhddD-pu5P3SsG7E-dzN5oQl5DYWW1DhO7sBOgI/edit#heading=h.oqq00pt8expe) with TorchTune.
The repo was generated using the alpha version of [torchtune](https://github.com/pytorch-labs/torchtune).
Brief notes:
- The starting recipe is based on the Alpaca Llama2 7B full finetune recipe (switched to bf16).
- I assume `output/` is used to store model outputs and `model/` is used to store the base model checkpoints.
For the `colorful` recipe:
- I copied a lot of functionality (like the actual model definition, dataset, etc) from torchtune repository directly since I needed to make changes.
- I reduced the flexiblity of the recipe (e.g. cannot specify the model or tokenizer) and increased it in other ways (e.g. can pass in a dataset path directly).
- I added intermediate checkpointing (i.e. every `n` steps) and automatically upload the checkpoint to HuggingFace Hub.
## Getting started
The below instructions can be copy-pasted as is on to a running instance. They assume that the `HF_TOKEN` environment variable is set with a valid token.
```bash
# for RunPod
cd /workspace
git clone [email protected]:pytorch-labs/torchtune.git
cd torchtune
pip install -e .
cd /workspace
git clone [email protected]:laurencer/torchtune-colorful-llama.git
cd torchtune-colorful-llama
# for wandb support
pip install wandb
```
```bash
mkdir -p model/
tune download --repo-id meta-llama/Llama-2-7b --output-dir model/
```
```bash
tune convert_checkpoint --checkpoint-path model/consolidated.00.pth --output-path model/llama2_native.tune
```
```bash
mkdir -p output/
# tune --nnodes 1 --nproc_per_node 1 ./colorful/full_finetune.py --config ./colorful/basic_config.yaml
nohup tune --nnodes 1 --nproc_per_node 1 ./colorful/full_finetune.py --config ./colorful/basic_config.yaml 2>&1 > training_log_$(date "+%Y.%m.%d_%H.%M.%S").log &
sleep 1
tail -f training_log_*.log
```
## Baselines
Two baseline configs are provided in the `baseline` directory.
We forked the original recipe to support customizing the location/path of the Alpaca dataset.
```bash
# tune --nnodes 1 --nproc_per_node 1 ./baseline/full_finetune.py --config ./baseline/baseline_config.yaml
nohup tune --nnodes 1 --nproc_per_node 1 ./baseline/full_finetune.py --config ./baseline/baseline_config.yaml 2>&1 > training_log_$(date "+%Y.%m.%d_%H.%M.%S").log &
sleep 1
tail -f training_log_*.log
```
The adversarial config uses a dataset that is equivalent to 4x the original alpaca cleaned dataset with extra examples that include prompt injection attempts. See [token coloring description](https://docs.google.com/document/d/1Win9vhddD-pu5P3SsG7E-dzN5oQl5DYWW1DhO7sBOgI/edit#heading=h.oqq00pt8expe) for more info.
```bash
# tune --nnodes 1 --nproc_per_node 1 ./baseline/full_finetune.py --config ./baseline/adversarial_config.yaml
nohup tune --nnodes 1 --nproc_per_node 1 ./baseline/full_finetune.py --config ./baseline/adversarial_config.yaml 2>&1 > training_log_$(date "+%Y.%m.%d_%H.%M.%S").log &
sleep 1
tail -f training_log_*.log
```
## Colorful
The `colorful` directory implements the changes required to support token coloring. This includes a custom dataset implementation and training script. | {} | null | laurencer/Colourful-Llama7b-Alpaca-Tune-4epochs | [
"region:us"
] | 2024-02-12T01:16:37+00:00 | [] | [] | TAGS
#region-us
| # torchtune research repo: token coloring (colorful llama)
Playground to try out token coloring with TorchTune.
The repo was generated using the alpha version of torchtune.
Brief notes:
- The starting recipe is based on the Alpaca Llama2 7B full finetune recipe (switched to bf16).
- I assume 'output/' is used to store model outputs and 'model/' is used to store the base model checkpoints.
For the 'colorful' recipe:
- I copied a lot of functionality (like the actual model definition, dataset, etc) from torchtune repository directly since I needed to make changes.
- I reduced the flexiblity of the recipe (e.g. cannot specify the model or tokenizer) and increased it in other ways (e.g. can pass in a dataset path directly).
- I added intermediate checkpointing (i.e. every 'n' steps) and automatically upload the checkpoint to HuggingFace Hub.
## Getting started
The below instructions can be copy-pasted as is on to a running instance. They assume that the 'HF_TOKEN' environment variable is set with a valid token.
## Baselines
Two baseline configs are provided in the 'baseline' directory.
We forked the original recipe to support customizing the location/path of the Alpaca dataset.
The adversarial config uses a dataset that is equivalent to 4x the original alpaca cleaned dataset with extra examples that include prompt injection attempts. See token coloring description for more info.
## Colorful
The 'colorful' directory implements the changes required to support token coloring. This includes a custom dataset implementation and training script. | [
"# torchtune research repo: token coloring (colorful llama)\n\nPlayground to try out token coloring with TorchTune.\n\nThe repo was generated using the alpha version of torchtune.\n\nBrief notes:\n\n- The starting recipe is based on the Alpaca Llama2 7B full finetune recipe (switched to bf16).\n- I assume 'output/' is used to store model outputs and 'model/' is used to store the base model checkpoints.\n\nFor the 'colorful' recipe:\n\n- I copied a lot of functionality (like the actual model definition, dataset, etc) from torchtune repository directly since I needed to make changes.\n- I reduced the flexiblity of the recipe (e.g. cannot specify the model or tokenizer) and increased it in other ways (e.g. can pass in a dataset path directly).\n- I added intermediate checkpointing (i.e. every 'n' steps) and automatically upload the checkpoint to HuggingFace Hub.",
"## Getting started\n\nThe below instructions can be copy-pasted as is on to a running instance. They assume that the 'HF_TOKEN' environment variable is set with a valid token.",
"## Baselines\n\nTwo baseline configs are provided in the 'baseline' directory.\nWe forked the original recipe to support customizing the location/path of the Alpaca dataset.\n\n\n\nThe adversarial config uses a dataset that is equivalent to 4x the original alpaca cleaned dataset with extra examples that include prompt injection attempts. See token coloring description for more info.",
"## Colorful\n\nThe 'colorful' directory implements the changes required to support token coloring. This includes a custom dataset implementation and training script."
] | [
"TAGS\n#region-us \n",
"# torchtune research repo: token coloring (colorful llama)\n\nPlayground to try out token coloring with TorchTune.\n\nThe repo was generated using the alpha version of torchtune.\n\nBrief notes:\n\n- The starting recipe is based on the Alpaca Llama2 7B full finetune recipe (switched to bf16).\n- I assume 'output/' is used to store model outputs and 'model/' is used to store the base model checkpoints.\n\nFor the 'colorful' recipe:\n\n- I copied a lot of functionality (like the actual model definition, dataset, etc) from torchtune repository directly since I needed to make changes.\n- I reduced the flexiblity of the recipe (e.g. cannot specify the model or tokenizer) and increased it in other ways (e.g. can pass in a dataset path directly).\n- I added intermediate checkpointing (i.e. every 'n' steps) and automatically upload the checkpoint to HuggingFace Hub.",
"## Getting started\n\nThe below instructions can be copy-pasted as is on to a running instance. They assume that the 'HF_TOKEN' environment variable is set with a valid token.",
"## Baselines\n\nTwo baseline configs are provided in the 'baseline' directory.\nWe forked the original recipe to support customizing the location/path of the Alpaca dataset.\n\n\n\nThe adversarial config uses a dataset that is equivalent to 4x the original alpaca cleaned dataset with extra examples that include prompt injection attempts. See token coloring description for more info.",
"## Colorful\n\nThe 'colorful' directory implements the changes required to support token coloring. This includes a custom dataset implementation and training script."
] | [
6,
232,
40,
88,
33
] | [
"passage: TAGS\n#region-us \n# torchtune research repo: token coloring (colorful llama)\n\nPlayground to try out token coloring with TorchTune.\n\nThe repo was generated using the alpha version of torchtune.\n\nBrief notes:\n\n- The starting recipe is based on the Alpaca Llama2 7B full finetune recipe (switched to bf16).\n- I assume 'output/' is used to store model outputs and 'model/' is used to store the base model checkpoints.\n\nFor the 'colorful' recipe:\n\n- I copied a lot of functionality (like the actual model definition, dataset, etc) from torchtune repository directly since I needed to make changes.\n- I reduced the flexiblity of the recipe (e.g. cannot specify the model or tokenizer) and increased it in other ways (e.g. can pass in a dataset path directly).\n- I added intermediate checkpointing (i.e. every 'n' steps) and automatically upload the checkpoint to HuggingFace Hub.## Getting started\n\nThe below instructions can be copy-pasted as is on to a running instance. They assume that the 'HF_TOKEN' environment variable is set with a valid token.## Baselines\n\nTwo baseline configs are provided in the 'baseline' directory.\nWe forked the original recipe to support customizing the location/path of the Alpaca dataset.\n\n\n\nThe adversarial config uses a dataset that is equivalent to 4x the original alpaca cleaned dataset with extra examples that include prompt injection attempts. See token coloring description for more info.## Colorful\n\nThe 'colorful' directory implements the changes required to support token coloring. This includes a custom dataset implementation and training script."
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null | null | transformers |
# MoEv4Config-TestWeightedTIES-7b
MoEv4Config-TestWeightedTIES-7b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [Kukedlc/NeuTrixOmniBe-7B-model-remix](https://huggingface.co/Kukedlc/NeuTrixOmniBe-7B-model-remix)
* [PetroGPT/WestSeverus-7B-DPO](https://huggingface.co/PetroGPT/WestSeverus-7B-DPO)
* [vanillaOVO/supermario_v4](https://huggingface.co/vanillaOVO/supermario_v4)
## 🧩 Configuration
```yaml
models:
- model: Kukedlc/NeuTrixOmniBe-7B-model-remix
# No parameters necessary for base model
- model: Kukedlc/NeuTrixOmniBe-7B-model-remix
parameters:
density: [1, 0.7, 0.1]
weight: [0, 0.3, 0.7, 1]
- model: PetroGPT/WestSeverus-7B-DPO
parameters:
density: [1, 0.7, 0.3]
weight: [0, 0.25, 0.5, 1]
- model: vanillaOVO/supermario_v4
parameters:
density: 0.33
weight:
- filter: mlp
value: 0.5
- value: 0
merge_method: ties
base_model: Kukedlc/NeuTrixOmniBe-7B-model-remix
parameters:
int8_mask: true
normalize: true
sparsify:
- filter: mlp
value: 0.5
- filter: self_attn
value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "jsfs11/MoEv4Config-TestWeightedTIES-7b"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "Kukedlc/NeuTrixOmniBe-7B-model-remix", "PetroGPT/WestSeverus-7B-DPO", "vanillaOVO/supermario_v4"], "base_model": ["Kukedlc/NeuTrixOmniBe-7B-model-remix", "PetroGPT/WestSeverus-7B-DPO", "vanillaOVO/supermario_v4"]} | text-generation | jsfs11/MoEv4Config-TestWeightedTIES-7b | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"Kukedlc/NeuTrixOmniBe-7B-model-remix",
"PetroGPT/WestSeverus-7B-DPO",
"vanillaOVO/supermario_v4",
"base_model:Kukedlc/NeuTrixOmniBe-7B-model-remix",
"base_model:PetroGPT/WestSeverus-7B-DPO",
"base_model:vanillaOVO/supermario_v4",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2024-02-12T01:21:41+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #Kukedlc/NeuTrixOmniBe-7B-model-remix #PetroGPT/WestSeverus-7B-DPO #vanillaOVO/supermario_v4 #base_model-Kukedlc/NeuTrixOmniBe-7B-model-remix #base_model-PetroGPT/WestSeverus-7B-DPO #base_model-vanillaOVO/supermario_v4 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# MoEv4Config-TestWeightedTIES-7b
MoEv4Config-TestWeightedTIES-7b is a merge of the following models using LazyMergekit:
* Kukedlc/NeuTrixOmniBe-7B-model-remix
* PetroGPT/WestSeverus-7B-DPO
* vanillaOVO/supermario_v4
## Configuration
## Usage
| [
"# MoEv4Config-TestWeightedTIES-7b\n\nMoEv4Config-TestWeightedTIES-7b is a merge of the following models using LazyMergekit:\n* Kukedlc/NeuTrixOmniBe-7B-model-remix\n* PetroGPT/WestSeverus-7B-DPO\n* vanillaOVO/supermario_v4",
"## Configuration",
"## Usage"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #Kukedlc/NeuTrixOmniBe-7B-model-remix #PetroGPT/WestSeverus-7B-DPO #vanillaOVO/supermario_v4 #base_model-Kukedlc/NeuTrixOmniBe-7B-model-remix #base_model-PetroGPT/WestSeverus-7B-DPO #base_model-vanillaOVO/supermario_v4 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# MoEv4Config-TestWeightedTIES-7b\n\nMoEv4Config-TestWeightedTIES-7b is a merge of the following models using LazyMergekit:\n* Kukedlc/NeuTrixOmniBe-7B-model-remix\n* PetroGPT/WestSeverus-7B-DPO\n* vanillaOVO/supermario_v4",
"## Configuration",
"## Usage"
] | [
170,
85,
4,
3
] | [
"passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #Kukedlc/NeuTrixOmniBe-7B-model-remix #PetroGPT/WestSeverus-7B-DPO #vanillaOVO/supermario_v4 #base_model-Kukedlc/NeuTrixOmniBe-7B-model-remix #base_model-PetroGPT/WestSeverus-7B-DPO #base_model-vanillaOVO/supermario_v4 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# MoEv4Config-TestWeightedTIES-7b\n\nMoEv4Config-TestWeightedTIES-7b is a merge of the following models using LazyMergekit:\n* Kukedlc/NeuTrixOmniBe-7B-model-remix\n* PetroGPT/WestSeverus-7B-DPO\n* vanillaOVO/supermario_v4## Configuration## Usage"
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null | null | transformers |
# Model Card for Model ID
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# Model Card for Model ID
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