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null | null |
# Antler-7B-RP-GGUF
## 概要
[Aratako/Antler-7B-RP](https://huggingface.co/Aratako/Antler-7B-RP)の量子化済みGGUF版です。ライセンス等詳細は元モデルをご確認ください。
|
{"language": ["ja"], "license": "apache-2.0", "tags": ["not-for-all-audiences", "nsfw"], "datasets": ["grimulkan/LimaRP-augmented", "Aratako/Rosebleu-1on1-Dialogues-RP"], "base_model": ["Aratako/Antler-7B-RP"]}
|
Aratako/Antler-7B-RP-GGUF
| null |
[
"gguf",
"not-for-all-audiences",
"nsfw",
"ja",
"dataset:grimulkan/LimaRP-augmented",
"dataset:Aratako/Rosebleu-1on1-Dialogues-RP",
"base_model:Aratako/Antler-7B-RP",
"license:apache-2.0",
"region:us"
] | null |
2024-04-13T12:59:14+00:00
|
[] |
[
"ja"
] |
TAGS
#gguf #not-for-all-audiences #nsfw #ja #dataset-grimulkan/LimaRP-augmented #dataset-Aratako/Rosebleu-1on1-Dialogues-RP #base_model-Aratako/Antler-7B-RP #license-apache-2.0 #region-us
|
# Antler-7B-RP-GGUF
## 概要
Aratako/Antler-7B-RPの量子化済みGGUF版です。ライセンス等詳細は元モデルをご確認ください。
|
[
"# Antler-7B-RP-GGUF",
"## 概要\nAratako/Antler-7B-RPの量子化済みGGUF版です。ライセンス等詳細は元モデルをご確認ください。"
] |
[
"TAGS\n#gguf #not-for-all-audiences #nsfw #ja #dataset-grimulkan/LimaRP-augmented #dataset-Aratako/Rosebleu-1on1-Dialogues-RP #base_model-Aratako/Antler-7B-RP #license-apache-2.0 #region-us \n",
"# Antler-7B-RP-GGUF",
"## 概要\nAratako/Antler-7B-RPの量子化済みGGUF版です。ライセンス等詳細は元モデルをご確認ください。"
] |
reinforcement-learning
|
stable-baselines3
|
# **PPO** Agent playing **BipedalWalker-v3**
This is a trained model of a **PPO** agent playing **BipedalWalker-v3**
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": ["BipedalWalker-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "BipedalWalker-v3", "type": "BipedalWalker-v3"}, "metrics": [{"type": "mean_reward", "value": "209.21 +/- 91.56", "name": "mean_reward", "verified": false}]}]}]}
|
koopatroopa787/ppo-BipedalWalker-v4
| null |
[
"stable-baselines3",
"BipedalWalker-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"doi:10.57967/hf/2156",
"model-index",
"region:us"
] | null |
2024-04-13T13:00:24+00:00
|
[] |
[] |
TAGS
#stable-baselines3 #BipedalWalker-v3 #deep-reinforcement-learning #reinforcement-learning #doi-10.57967/hf/2156 #model-index #region-us
|
# PPO Agent playing BipedalWalker-v3
This is a trained model of a PPO agent playing BipedalWalker-v3
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
|
[
"# PPO Agent playing BipedalWalker-v3\nThis is a trained model of a PPO agent playing BipedalWalker-v3\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] |
[
"TAGS\n#stable-baselines3 #BipedalWalker-v3 #deep-reinforcement-learning #reinforcement-learning #doi-10.57967/hf/2156 #model-index #region-us \n",
"# PPO Agent playing BipedalWalker-v3\nThis is a trained model of a PPO agent playing BipedalWalker-v3\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] |
text2text-generation
|
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_samsum_model
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) 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: 4
- eval_batch_size: 4
- 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: 1
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "mit", "tags": ["generated_from_trainer"], "base_model": "facebook/bart-large-cnn", "model-index": [{"name": "bart_samsum_model", "results": []}]}
|
Khushi870/bart_samsum_model
| null |
[
"transformers",
"tensorboard",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:facebook/bart-large-cnn",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-13T13:01:48+00:00
|
[] |
[] |
TAGS
#transformers #tensorboard #safetensors #bart #text2text-generation #generated_from_trainer #base_model-facebook/bart-large-cnn #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# bart_samsum_model
This model is a fine-tuned version of facebook/bart-large-cnn 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: 4
- eval_batch_size: 4
- 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: 1
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
[
"# bart_samsum_model\n\nThis model is a fine-tuned version of facebook/bart-large-cnn 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: 4\n- eval_batch_size: 4\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: 1",
"### Training results",
"### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.2.1+cu121\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] |
[
"TAGS\n#transformers #tensorboard #safetensors #bart #text2text-generation #generated_from_trainer #base_model-facebook/bart-large-cnn #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# bart_samsum_model\n\nThis model is a fine-tuned version of facebook/bart-large-cnn 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: 4\n- eval_batch_size: 4\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: 1",
"### Training results",
"### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.2.1+cu121\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] |
text-generation
|
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
{"library_name": "transformers", "tags": []}
|
Rutts07/gemma-2b-it-ai-human-gen
| null |
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-13T13:02:07+00:00
|
[
"1910.09700"
] |
[] |
TAGS
#transformers #safetensors #gemma #text-generation #conversational #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:
- 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 #gemma #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"
] |
null |
adapter-transformers
|
# Adapter `BigTMiami/F_adapter_ia3_classification_P_20_to_C_30` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [BigTMiami/amazon_helpfulness](https://huggingface.co/datasets/BigTMiami/amazon_helpfulness/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("roberta-base")
adapter_name = model.load_adapter("BigTMiami/F_adapter_ia3_classification_P_20_to_C_30", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
{"tags": ["roberta", "adapter-transformers"], "datasets": ["BigTMiami/amazon_helpfulness"]}
|
BigTMiami/F_adapter_ia3_classification_P_20_to_C_30
| null |
[
"adapter-transformers",
"roberta",
"dataset:BigTMiami/amazon_helpfulness",
"region:us"
] | null |
2024-04-13T13:02:22+00:00
|
[] |
[] |
TAGS
#adapter-transformers #roberta #dataset-BigTMiami/amazon_helpfulness #region-us
|
# Adapter 'BigTMiami/F_adapter_ia3_classification_P_20_to_C_30' for roberta-base
An adapter for the 'roberta-base' model that was trained on the BigTMiami/amazon_helpfulness dataset and includes a prediction head for classification.
This adapter was created for usage with the Adapters library.
## Usage
First, install 'adapters':
Now, the adapter can be loaded and activated like this:
## Architecture & Training
## Evaluation results
|
[
"# Adapter 'BigTMiami/F_adapter_ia3_classification_P_20_to_C_30' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/amazon_helpfulness dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] |
[
"TAGS\n#adapter-transformers #roberta #dataset-BigTMiami/amazon_helpfulness #region-us \n",
"# Adapter 'BigTMiami/F_adapter_ia3_classification_P_20_to_C_30' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/amazon_helpfulness dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] |
null | null |
GGUF quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Mistral-22B-v0.2 - GGUF
- Model creator: https://huggingface.co/Vezora/
- Original model: https://huggingface.co/Vezora/Mistral-22B-v0.2/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Mistral-22B-v0.2.Q2_K.gguf](https://huggingface.co/RichardErkhov/Mistral-22B-v0.2-gguf/blob/main/Mistral-22B-v0.2.Q2_K.gguf) | Q2_K | 7.7GB |
| [Mistral-22B-v0.2.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Mistral-22B-v0.2-gguf/blob/main/Mistral-22B-v0.2.IQ3_XS.gguf) | IQ3_XS | 8.54GB |
| [Mistral-22B-v0.2.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Mistral-22B-v0.2-gguf/blob/main/Mistral-22B-v0.2.IQ3_S.gguf) | IQ3_S | 9.02GB |
| [Mistral-22B-v0.2.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Mistral-22B-v0.2-gguf/blob/main/Mistral-22B-v0.2.Q3_K_S.gguf) | Q3_K_S | 8.97GB |
| [Mistral-22B-v0.2.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Mistral-22B-v0.2-gguf/blob/main/Mistral-22B-v0.2.IQ3_M.gguf) | IQ3_M | 9.37GB |
| [Mistral-22B-v0.2.Q3_K.gguf](https://huggingface.co/RichardErkhov/Mistral-22B-v0.2-gguf/blob/main/Mistral-22B-v0.2.Q3_K.gguf) | Q3_K | 10.01GB |
| [Mistral-22B-v0.2.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Mistral-22B-v0.2-gguf/blob/main/Mistral-22B-v0.2.Q3_K_M.gguf) | Q3_K_M | 10.01GB |
| [Mistral-22B-v0.2.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Mistral-22B-v0.2-gguf/blob/main/Mistral-22B-v0.2.Q3_K_L.gguf) | Q3_K_L | 10.92GB |
| [Mistral-22B-v0.2.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Mistral-22B-v0.2-gguf/blob/main/Mistral-22B-v0.2.IQ4_XS.gguf) | IQ4_XS | 11.21GB |
| [Mistral-22B-v0.2.Q4_0.gguf](https://huggingface.co/RichardErkhov/Mistral-22B-v0.2-gguf/blob/main/Mistral-22B-v0.2.Q4_0.gguf) | Q4_0 | 11.7GB |
| [Mistral-22B-v0.2.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Mistral-22B-v0.2-gguf/blob/main/Mistral-22B-v0.2.IQ4_NL.gguf) | IQ4_NL | 11.82GB |
| [Mistral-22B-v0.2.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Mistral-22B-v0.2-gguf/blob/main/Mistral-22B-v0.2.Q4_K_S.gguf) | Q4_K_S | 11.78GB |
| [Mistral-22B-v0.2.Q4_K.gguf](https://huggingface.co/RichardErkhov/Mistral-22B-v0.2-gguf/blob/main/Mistral-22B-v0.2.Q4_K.gguf) | Q4_K | 12.42GB |
| [Mistral-22B-v0.2.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Mistral-22B-v0.2-gguf/blob/main/Mistral-22B-v0.2.Q4_K_M.gguf) | Q4_K_M | 12.42GB |
| [Mistral-22B-v0.2.Q4_1.gguf](https://huggingface.co/RichardErkhov/Mistral-22B-v0.2-gguf/blob/main/Mistral-22B-v0.2.Q4_1.gguf) | Q4_1 | 12.98GB |
| [Mistral-22B-v0.2.Q5_0.gguf](https://huggingface.co/RichardErkhov/Mistral-22B-v0.2-gguf/blob/main/Mistral-22B-v0.2.Q5_0.gguf) | Q5_0 | 14.27GB |
| [Mistral-22B-v0.2.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Mistral-22B-v0.2-gguf/blob/main/Mistral-22B-v0.2.Q5_K_S.gguf) | Q5_K_S | 14.27GB |
| [Mistral-22B-v0.2.Q5_K.gguf](https://huggingface.co/RichardErkhov/Mistral-22B-v0.2-gguf/blob/main/Mistral-22B-v0.2.Q5_K.gguf) | Q5_K | 14.64GB |
| [Mistral-22B-v0.2.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Mistral-22B-v0.2-gguf/blob/main/Mistral-22B-v0.2.Q5_K_M.gguf) | Q5_K_M | 14.64GB |
| [Mistral-22B-v0.2.Q5_1.gguf](https://huggingface.co/RichardErkhov/Mistral-22B-v0.2-gguf/blob/main/Mistral-22B-v0.2.Q5_1.gguf) | Q5_1 | 15.55GB |
| [Mistral-22B-v0.2.Q6_K.gguf](https://huggingface.co/RichardErkhov/Mistral-22B-v0.2-gguf/blob/main/Mistral-22B-v0.2.Q6_K.gguf) | Q6_K | 16.99GB |
Original model description:
---
license: apache-2.0
---
<img src="https://huggingface.co/Vezora/Mistral-22B-v0.1/resolve/main/unsloth.png" width="100" height="150" />
### Mistral-22b-v.02 Release Announcement 🚀
## This model is not an moe, it is infact a 22B parameter dense model!
**Date**: April 13
**Creator** [Nicolas Mejia-Petit](https://twitter.com/mejia_petit)
### Overview
- Just two days after our release of **Mistral-22b-v0.1**, we are excited to introduce our handcrafted experimental model, **Mistral-22b-v.02**. This model is a culmination of equal knowledge distilled from all experts into a single, dense 22b model. This model is not a single trained expert, rather its a compressed MOE model, turning it into a dense 22b mode. This is the first working MOE to Dense model conversion.
- v0.2 has trained on 8x more data than v0.1!
### Capabilities
- **Math Proficiency**: The model exhibits strong mathematical abilities. Dispite not being trained on math.
- **Better at Coding** The model is significantly better at coding, than V1, it passed some of my simple coding test, such as "Create a simple HTML site with a button that changes the background color to a random color", which V1 failed.
- **More Cohesive** This V2 model is significantly more cohesive, and better at aunderstanding the prompts and answering with the appopriate answer.
- **Highly Uncencored** Since this model was also Re-Alligned to be uncencored, it can just answer anything you ask. So use at your own risk, we take no responsibility for your generated responces.
- **Multi Turn** The dataset this model trained on was mostly all multi turn conversations, spanning many different topics, with some emphasis on coding.
- **Json Mode** I did train this model on answering in JSON and using JSON tools., I have yet to try it, in depth but preliminary test shows it works, including.
- **Agent abilities** I did train this model on agent datasets, that teach it to do real world tasks such as picking up an object, and even navigating a webpage based off HTML.
- **Good Chili Recipe** The model gives a good chili recipe :)
- **32k Sequence Length** This model was trained with a 32k sequence length.
### Experimental Nature
Please note that Mistral-22b is still in a WIP. v0.3 has started training now, with a different method than used before, this is to hopefully make the model more round in its internel knowlledge. Through my testing I found V2 to be a significant improvement over v.1.
### Upcoming Release: V.3
- v0.3 will feature a different base model for testing purposes, however this model is pretty darn good for a second test. :)
- I have done some preliminary results with my new v0.3 base model, and it appears to achieve a lower loss after the first epoch compared to the other base model used for v0.1 and v0.2. so we have started training v0.3 with the new base model and with the longer dataset, will be done and released in the next 48 hours. :)
### Stay Updated
**V.3**, coming soon! And is currently training, will be done in the next ~24 hours. 🌟Paper Coming Soon🌟
- There will be more of these 22b models. They 5-6 siblings till I find what the best results are for MOE compression.
- However I am very surprised at how good this V.2 model is, off my small testing.
### Usage:
- This model requires a specific chat template, as the training format was Guanaco this is what it looks like:
- "### System: You are a helpful assistant. ### Human###: Give me the best chili recipe you can ###Assistant: Here is the best chili recipe..."
## Thank you!
- Thank you to [Daniel Han](https://twitter.com/danielhanchen), for Unsloth AI which was used to train this model. this led to a 2-3x speed increae and 2-3x decrease in memmory consumption.
- Thank you to [Charles Coddard](https://twitter.com/chargoddard), for providng me with a script that was nessary to make this model.
- Thank you to Mistral, for releasing Another Wonderful open source model, under Apache 2.0.
- Thank you to [Tim Dettmers](https://twitter.com/Tim_Dettmers), for creating QLora
- Thank you to [Tri Dao](https://twitter.com/tri_dao), for creating Flash Attention
- Thank you to Microsoft, for the Lora paper, and the Slice-GPT paper.
- Thank you to the Hugging Face team, for everything.❤️ We really do appreciate you guys and all your hard work and commitment to the open source community!❤️
- Thank you to [Jon Durbin](https://x.com/jon_durbin?s=21) I used one of his DPO datasets converted to SFT, more info will be explained in paper.
## Future plans, train 4-5 more of these experimental models gather preliminary testing results, and then run evaluations on all the models I see have the best possibilities of excelling, then use the best one.
|
{}
|
RichardErkhov/Vezora_-_Mistral-22B-v0.2-gguf
| null |
[
"gguf",
"region:us"
] | null |
2024-04-13T13:03:28+00:00
|
[] |
[] |
TAGS
#gguf #region-us
|
GGUF quantization made by Richard Erkhov.
Github
Discord
Request more models
Mistral-22B-v0.2 - GGUF
* Model creator: URL
* Original model: URL
Name: Mistral-22B-v0.2.Q2\_K.gguf, Quant method: Q2\_K, Size: 7.7GB
Name: Mistral-22B-v0.2.IQ3\_XS.gguf, Quant method: IQ3\_XS, Size: 8.54GB
Name: Mistral-22B-v0.2.IQ3\_S.gguf, Quant method: IQ3\_S, Size: 9.02GB
Name: Mistral-22B-v0.2.Q3\_K\_S.gguf, Quant method: Q3\_K\_S, Size: 8.97GB
Name: Mistral-22B-v0.2.IQ3\_M.gguf, Quant method: IQ3\_M, Size: 9.37GB
Name: Mistral-22B-v0.2.Q3\_K.gguf, Quant method: Q3\_K, Size: 10.01GB
Name: Mistral-22B-v0.2.Q3\_K\_M.gguf, Quant method: Q3\_K\_M, Size: 10.01GB
Name: Mistral-22B-v0.2.Q3\_K\_L.gguf, Quant method: Q3\_K\_L, Size: 10.92GB
Name: Mistral-22B-v0.2.IQ4\_XS.gguf, Quant method: IQ4\_XS, Size: 11.21GB
Name: Mistral-22B-v0.2.Q4\_0.gguf, Quant method: Q4\_0, Size: 11.7GB
Name: Mistral-22B-v0.2.IQ4\_NL.gguf, Quant method: IQ4\_NL, Size: 11.82GB
Name: Mistral-22B-v0.2.Q4\_K\_S.gguf, Quant method: Q4\_K\_S, Size: 11.78GB
Name: Mistral-22B-v0.2.Q4\_K.gguf, Quant method: Q4\_K, Size: 12.42GB
Name: Mistral-22B-v0.2.Q4\_K\_M.gguf, Quant method: Q4\_K\_M, Size: 12.42GB
Name: Mistral-22B-v0.2.Q4\_1.gguf, Quant method: Q4\_1, Size: 12.98GB
Name: Mistral-22B-v0.2.Q5\_0.gguf, Quant method: Q5\_0, Size: 14.27GB
Name: Mistral-22B-v0.2.Q5\_K\_S.gguf, Quant method: Q5\_K\_S, Size: 14.27GB
Name: Mistral-22B-v0.2.Q5\_K.gguf, Quant method: Q5\_K, Size: 14.64GB
Name: Mistral-22B-v0.2.Q5\_K\_M.gguf, Quant method: Q5\_K\_M, Size: 14.64GB
Name: Mistral-22B-v0.2.Q5\_1.gguf, Quant method: Q5\_1, Size: 15.55GB
Name: Mistral-22B-v0.2.Q6\_K.gguf, Quant method: Q6\_K, Size: 16.99GB
```
Original model description:
---
```
license: apache-2.0
-------------------
<img src="URL width="100" height="150" />
### Mistral-22b-v.02 Release Announcement
This model is not an moe, it is infact a 22B parameter dense model!
-------------------------------------------------------------------
Date: April 13
Creator Nicolas Mejia-Petit
### Overview
* Just two days after our release of Mistral-22b-v0.1, we are excited to introduce our handcrafted experimental model, Mistral-22b-v.02. This model is a culmination of equal knowledge distilled from all experts into a single, dense 22b model. This model is not a single trained expert, rather its a compressed MOE model, turning it into a dense 22b mode. This is the first working MOE to Dense model conversion.
* v0.2 has trained on 8x more data than v0.1!
### Capabilities
* Math Proficiency: The model exhibits strong mathematical abilities. Dispite not being trained on math.
* Better at Coding The model is significantly better at coding, than V1, it passed some of my simple coding test, such as "Create a simple HTML site with a button that changes the background color to a random color", which V1 failed.
* More Cohesive This V2 model is significantly more cohesive, and better at aunderstanding the prompts and answering with the appopriate answer.
* Highly Uncencored Since this model was also Re-Alligned to be uncencored, it can just answer anything you ask. So use at your own risk, we take no responsibility for your generated responces.
* Multi Turn The dataset this model trained on was mostly all multi turn conversations, spanning many different topics, with some emphasis on coding.
* Json Mode I did train this model on answering in JSON and using JSON tools., I have yet to try it, in depth but preliminary test shows it works, including.
* Agent abilities I did train this model on agent datasets, that teach it to do real world tasks such as picking up an object, and even navigating a webpage based off HTML.
* Good Chili Recipe The model gives a good chili recipe :)
* 32k Sequence Length This model was trained with a 32k sequence length.
### Experimental Nature
Please note that Mistral-22b is still in a WIP. v0.3 has started training now, with a different method than used before, this is to hopefully make the model more round in its internel knowlledge. Through my testing I found V2 to be a significant improvement over v.1.
### Upcoming Release: V.3
* v0.3 will feature a different base model for testing purposes, however this model is pretty darn good for a second test. :)
* I have done some preliminary results with my new v0.3 base model, and it appears to achieve a lower loss after the first epoch compared to the other base model used for v0.1 and v0.2. so we have started training v0.3 with the new base model and with the longer dataset, will be done and released in the next 48 hours. :)
### Stay Updated
V.3, coming soon! And is currently training, will be done in the next ~24 hours. Paper Coming Soon
* There will be more of these 22b models. They 5-6 siblings till I find what the best results are for MOE compression.
* However I am very surprised at how good this V.2 model is, off my small testing.
### Usage:
* This model requires a specific chat template, as the training format was Guanaco this is what it looks like:
* "### System: You are a helpful assistant. ### Human###: Give me the best chili recipe you can ###Assistant: Here is the best chili recipe..."
Thank you!
----------
* Thank you to Daniel Han, for Unsloth AI which was used to train this model. this led to a 2-3x speed increae and 2-3x decrease in memmory consumption.
* Thank you to Charles Coddard, for providng me with a script that was nessary to make this model.
* Thank you to Mistral, for releasing Another Wonderful open source model, under Apache 2.0.
* Thank you to Tim Dettmers, for creating QLora
* Thank you to Tri Dao, for creating Flash Attention
* Thank you to Microsoft, for the Lora paper, and the Slice-GPT paper.
* Thank you to the Hugging Face team, for everything.️ We really do appreciate you guys and all your hard work and commitment to the open source community!️
* Thank you to Jon Durbin I used one of his DPO datasets converted to SFT, more info will be explained in paper.
Future plans, train 4-5 more of these experimental models gather preliminary testing results, and then run evaluations on all the models I see have the best possibilities of excelling, then use the best one.
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
|
[
"### Mistral-22b-v.02 Release Announcement\n\n\nThis model is not an moe, it is infact a 22B parameter dense model!\n-------------------------------------------------------------------\n\n\nDate: April 13\nCreator Nicolas Mejia-Petit",
"### Overview\n\n\n* Just two days after our release of Mistral-22b-v0.1, we are excited to introduce our handcrafted experimental model, Mistral-22b-v.02. This model is a culmination of equal knowledge distilled from all experts into a single, dense 22b model. This model is not a single trained expert, rather its a compressed MOE model, turning it into a dense 22b mode. This is the first working MOE to Dense model conversion.\n* v0.2 has trained on 8x more data than v0.1!",
"### Capabilities\n\n\n* Math Proficiency: The model exhibits strong mathematical abilities. Dispite not being trained on math.\n* Better at Coding The model is significantly better at coding, than V1, it passed some of my simple coding test, such as \"Create a simple HTML site with a button that changes the background color to a random color\", which V1 failed.\n* More Cohesive This V2 model is significantly more cohesive, and better at aunderstanding the prompts and answering with the appopriate answer.\n* Highly Uncencored Since this model was also Re-Alligned to be uncencored, it can just answer anything you ask. So use at your own risk, we take no responsibility for your generated responces.\n* Multi Turn The dataset this model trained on was mostly all multi turn conversations, spanning many different topics, with some emphasis on coding.\n* Json Mode I did train this model on answering in JSON and using JSON tools., I have yet to try it, in depth but preliminary test shows it works, including.\n* Agent abilities I did train this model on agent datasets, that teach it to do real world tasks such as picking up an object, and even navigating a webpage based off HTML.\n* Good Chili Recipe The model gives a good chili recipe :)\n* 32k Sequence Length This model was trained with a 32k sequence length.",
"### Experimental Nature\n\n\nPlease note that Mistral-22b is still in a WIP. v0.3 has started training now, with a different method than used before, this is to hopefully make the model more round in its internel knowlledge. Through my testing I found V2 to be a significant improvement over v.1.",
"### Upcoming Release: V.3\n\n\n* v0.3 will feature a different base model for testing purposes, however this model is pretty darn good for a second test. :)\n* I have done some preliminary results with my new v0.3 base model, and it appears to achieve a lower loss after the first epoch compared to the other base model used for v0.1 and v0.2. so we have started training v0.3 with the new base model and with the longer dataset, will be done and released in the next 48 hours. :)",
"### Stay Updated\n\n\nV.3, coming soon! And is currently training, will be done in the next ~24 hours. Paper Coming Soon\n\n\n* There will be more of these 22b models. They 5-6 siblings till I find what the best results are for MOE compression.\n* However I am very surprised at how good this V.2 model is, off my small testing.",
"### Usage:\n\n\n* This model requires a specific chat template, as the training format was Guanaco this is what it looks like:\n* \"### System: You are a helpful assistant. ### Human###: Give me the best chili recipe you can ###Assistant: Here is the best chili recipe...\"\n\n\nThank you!\n----------\n\n\n* Thank you to Daniel Han, for Unsloth AI which was used to train this model. this led to a 2-3x speed increae and 2-3x decrease in memmory consumption.\n* Thank you to Charles Coddard, for providng me with a script that was nessary to make this model.\n* Thank you to Mistral, for releasing Another Wonderful open source model, under Apache 2.0.\n* Thank you to Tim Dettmers, for creating QLora\n* Thank you to Tri Dao, for creating Flash Attention\n* Thank you to Microsoft, for the Lora paper, and the Slice-GPT paper.\n* Thank you to the Hugging Face team, for everything.️ We really do appreciate you guys and all your hard work and commitment to the open source community!️\n* Thank you to Jon Durbin I used one of his DPO datasets converted to SFT, more info will be explained in paper.\n\n\nFuture plans, train 4-5 more of these experimental models gather preliminary testing results, and then run evaluations on all the models I see have the best possibilities of excelling, then use the best one.\n---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------"
] |
[
"TAGS\n#gguf #region-us \n",
"### Mistral-22b-v.02 Release Announcement\n\n\nThis model is not an moe, it is infact a 22B parameter dense model!\n-------------------------------------------------------------------\n\n\nDate: April 13\nCreator Nicolas Mejia-Petit",
"### Overview\n\n\n* Just two days after our release of Mistral-22b-v0.1, we are excited to introduce our handcrafted experimental model, Mistral-22b-v.02. This model is a culmination of equal knowledge distilled from all experts into a single, dense 22b model. This model is not a single trained expert, rather its a compressed MOE model, turning it into a dense 22b mode. This is the first working MOE to Dense model conversion.\n* v0.2 has trained on 8x more data than v0.1!",
"### Capabilities\n\n\n* Math Proficiency: The model exhibits strong mathematical abilities. Dispite not being trained on math.\n* Better at Coding The model is significantly better at coding, than V1, it passed some of my simple coding test, such as \"Create a simple HTML site with a button that changes the background color to a random color\", which V1 failed.\n* More Cohesive This V2 model is significantly more cohesive, and better at aunderstanding the prompts and answering with the appopriate answer.\n* Highly Uncencored Since this model was also Re-Alligned to be uncencored, it can just answer anything you ask. So use at your own risk, we take no responsibility for your generated responces.\n* Multi Turn The dataset this model trained on was mostly all multi turn conversations, spanning many different topics, with some emphasis on coding.\n* Json Mode I did train this model on answering in JSON and using JSON tools., I have yet to try it, in depth but preliminary test shows it works, including.\n* Agent abilities I did train this model on agent datasets, that teach it to do real world tasks such as picking up an object, and even navigating a webpage based off HTML.\n* Good Chili Recipe The model gives a good chili recipe :)\n* 32k Sequence Length This model was trained with a 32k sequence length.",
"### Experimental Nature\n\n\nPlease note that Mistral-22b is still in a WIP. v0.3 has started training now, with a different method than used before, this is to hopefully make the model more round in its internel knowlledge. Through my testing I found V2 to be a significant improvement over v.1.",
"### Upcoming Release: V.3\n\n\n* v0.3 will feature a different base model for testing purposes, however this model is pretty darn good for a second test. :)\n* I have done some preliminary results with my new v0.3 base model, and it appears to achieve a lower loss after the first epoch compared to the other base model used for v0.1 and v0.2. so we have started training v0.3 with the new base model and with the longer dataset, will be done and released in the next 48 hours. :)",
"### Stay Updated\n\n\nV.3, coming soon! And is currently training, will be done in the next ~24 hours. Paper Coming Soon\n\n\n* There will be more of these 22b models. They 5-6 siblings till I find what the best results are for MOE compression.\n* However I am very surprised at how good this V.2 model is, off my small testing.",
"### Usage:\n\n\n* This model requires a specific chat template, as the training format was Guanaco this is what it looks like:\n* \"### System: You are a helpful assistant. ### Human###: Give me the best chili recipe you can ###Assistant: Here is the best chili recipe...\"\n\n\nThank you!\n----------\n\n\n* Thank you to Daniel Han, for Unsloth AI which was used to train this model. this led to a 2-3x speed increae and 2-3x decrease in memmory consumption.\n* Thank you to Charles Coddard, for providng me with a script that was nessary to make this model.\n* Thank you to Mistral, for releasing Another Wonderful open source model, under Apache 2.0.\n* Thank you to Tim Dettmers, for creating QLora\n* Thank you to Tri Dao, for creating Flash Attention\n* Thank you to Microsoft, for the Lora paper, and the Slice-GPT paper.\n* Thank you to the Hugging Face team, for everything.️ We really do appreciate you guys and all your hard work and commitment to the open source community!️\n* Thank you to Jon Durbin I used one of his DPO datasets converted to SFT, more info will be explained in paper.\n\n\nFuture plans, train 4-5 more of these experimental models gather preliminary testing results, and then run evaluations on all the models I see have the best possibilities of excelling, then use the best one.\n---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------"
] |
text-generation
|
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[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]
|
{"library_name": "transformers", "tags": []}
|
Abirate/gemma-1.1-7b-it-finetuned-on-kaggle-writeups
| null |
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"doi:10.57967/hf/2071",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-13T13:04:38+00:00
|
[
"1910.09700"
] |
[] |
TAGS
#transformers #safetensors #gemma #text-generation #conversational #arxiv-1910.09700 #doi-10.57967/hf/2071 #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:
- 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 #gemma #text-generation #conversational #arxiv-1910.09700 #doi-10.57967/hf/2071 #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"
] |
null | null |
# English to Spanish Machine Translation
## Introduction:
In this project, we'll build a sequence-to-sequence Transformer model, which we'll train on an English-to-Spanish machine translation task.
In this task we will learn:
- Vectorize text using the Keras TextVectorization layer.
- Implement a TransformerEncoder layer, a TransformerDecoder layer, and a PositionalEmbedding layer.
- Prepare data for training a sequence-to-sequence model.
- Use the trained model to generate translations of never-seen-before input sentences (sequence-to-sequence inference).
## Dataset Collection:
We'll be working with an English-to-Spanish translation dataset provided by Anki from this source:
"http://storage.googleapis.com/download.tensorflow.org/data/spa-eng.zip"
## Dependencies
- numpy
- keras
- tensorflow
## Data Processing
- Every line has a sentence in English and a comparable sentence in Spanish. The target sequence is the Spanish sentence, and the source sequence is the English sentence. To the Spanish sentence, we prepend the token "[start]" and attach the token "[end]".
- To vectorize the text data, we will utilize two instances of the TextVectorization layer (one for English and one for Spanish). This means that instead of the original strings, we will convert them into integer sequences, where each integer is the index of a word in a vocabulary.
- At each training step, the model will seek to predict target words N+1 (and beyond) using the source sentence and the target words 0 to N.
```python
def make_dataset(pairs):
eng_texts, spa_texts = zip(*pairs)
eng_texts = list(eng_texts)
spa_texts = list(spa_texts)
dataset = tf_data.Dataset.from_tensor_slices((eng_texts, spa_texts))
dataset = dataset.batch(batch_size)
dataset = dataset.map(format_dataset)
return dataset.cache().shuffle(2048).prefetch(16)
train_ds = make_dataset(train_pairs)
val_ds = make_dataset(val_pairs)
```
- We have batches of 64 pairs, and all sequences are 20 steps long.
```batch
inputs["encoder_inputs"].shape: (64, 20)
inputs["decoder_inputs"].shape: (64, 20)
targets.shape: (64, 20)
```
## Model Architecture:
The model architecture consists of an Encoder-Decoder LSTM network with an embedding layer.To make the model aware of word order, we also use a PositionalEmbedding layer.
The TransformerEncoder will receive the source sequence and create a new representation of it. The target sequence up to this point (target words 0 to N) will be delivered to the TransformerDecoder together with this updated representation. Next, the TransformerDecoder will try to anticipate words N+1 and beyond in the target sequence.
Since the TransformerDecoder views all of the sequences at once, we have to make sure that when it predicts token N+1, it only takes information from target tokens 0 to N. If we don't, it might use information from the future, which would produce a model that is unusable at inference time.
### Training the Model:
Accuracy will be used as a fast approach to track training results on validation data. Keep in mind that BLEU scores and other measures are usually used by machine translation algorithms, not accuracy alone.
In this case, we are only training for one epoch; however, you need train for at least thirty epochs in order to get the model to converge.
```python
epochs = 1 # This should be at least 30 for convergence
transformer.summary()
transformer.compile(
"rmsprop", loss="sparse_categorical_crossentropy", metrics=["accuracy"]
)
transformer.fit(train_ds, epochs=epochs, validation_data=val_ds)
```
```batch
Model: "transformer"
┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┓
┃ Layer (type) ┃ Output Shape ┃ Param # ┃ Connected to ┃
┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━┩
│ encoder_inputs │ (None, None) │ 0 │ - │
│ (InputLayer) │ │ │ │
├─────────────────────┼───────────────────┼─────────┼──────────────────────┤
│ positional_embeddi… │ (None, None, 256) │ 3,845,… │ encoder_inputs[0][0] │
│ (PositionalEmbeddi… │ │ │ │
├─────────────────────┼───────────────────┼─────────┼──────────────────────┤
│ decoder_inputs │ (None, None) │ 0 │ - │
│ (InputLayer) │ │ │ │
├─────────────────────┼───────────────────┼─────────┼──────────────────────┤
│ transformer_encoder │ (None, None, 256) │ 3,155,… │ positional_embeddin… │
│ (TransformerEncode… │ │ │ │
├─────────────────────┼───────────────────┼─────────┼──────────────────────┤
│ functional_5 │ (None, None, │ 12,959… │ decoder_inputs[0][0… │
│ (Functional) │ 15000) │ │ transformer_encoder… │
└─────────────────────┴───────────────────┴─────────┴──────────────────────┘
Total params: 19,960,216 (76.14 MB)
Trainable params: 19,960,216 (76.14 MB)
Non-trainable params: 0 (0.00 B)
```
### Result Analysis:
The vectorized English text and the goal token "[start]" are simply fed into the model. We then continuously produce the following token until we reach the token "[end]".
```batch
She handed him the money. [start] ella le pasó el dinero [end]
Tom has never heard Mary sing. [start] tom nunca ha oído cantar a mary [end]
Perhaps she will come tomorrow. [start] tal vez ella vendrá mañana [end]
I love to write. [start] me encanta escribir [end]
His French is improving little by little. [start] su francés va a [UNK] sólo un poco [end]
My hotel told me to call you. [start] mi hotel me dijo que te [UNK] [end]
```
## Contributor
Janaatul Ferdaws Amrin ([email protected])
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
---
|
{"license": "mit"}
|
jannatulferdaws/dl-project2
| null |
[
"license:mit",
"region:us"
] | null |
2024-04-13T13:05:24+00:00
|
[] |
[] |
TAGS
#license-mit #region-us
|
# English to Spanish Machine Translation
## Introduction:
In this project, we'll build a sequence-to-sequence Transformer model, which we'll train on an English-to-Spanish machine translation task.
In this task we will learn:
- Vectorize text using the Keras TextVectorization layer.
- Implement a TransformerEncoder layer, a TransformerDecoder layer, and a PositionalEmbedding layer.
- Prepare data for training a sequence-to-sequence model.
- Use the trained model to generate translations of never-seen-before input sentences (sequence-to-sequence inference).
## Dataset Collection:
We'll be working with an English-to-Spanish translation dataset provided by Anki from this source:
"URL
## Dependencies
- numpy
- keras
- tensorflow
## Data Processing
- Every line has a sentence in English and a comparable sentence in Spanish. The target sequence is the Spanish sentence, and the source sequence is the English sentence. To the Spanish sentence, we prepend the token "[start]" and attach the token "[end]".
- To vectorize the text data, we will utilize two instances of the TextVectorization layer (one for English and one for Spanish). This means that instead of the original strings, we will convert them into integer sequences, where each integer is the index of a word in a vocabulary.
- At each training step, the model will seek to predict target words N+1 (and beyond) using the source sentence and the target words 0 to N.
- We have batches of 64 pairs, and all sequences are 20 steps long.
## Model Architecture:
The model architecture consists of an Encoder-Decoder LSTM network with an embedding layer.To make the model aware of word order, we also use a PositionalEmbedding layer.
The TransformerEncoder will receive the source sequence and create a new representation of it. The target sequence up to this point (target words 0 to N) will be delivered to the TransformerDecoder together with this updated representation. Next, the TransformerDecoder will try to anticipate words N+1 and beyond in the target sequence.
Since the TransformerDecoder views all of the sequences at once, we have to make sure that when it predicts token N+1, it only takes information from target tokens 0 to N. If we don't, it might use information from the future, which would produce a model that is unusable at inference time.
### Training the Model:
Accuracy will be used as a fast approach to track training results on validation data. Keep in mind that BLEU scores and other measures are usually used by machine translation algorithms, not accuracy alone.
In this case, we are only training for one epoch; however, you need train for at least thirty epochs in order to get the model to converge.
### Result Analysis:
The vectorized English text and the goal token "[start]" are simply fed into the model. We then continuously produce the following token until we reach the token "[end]".
## Contributor
Janaatul Ferdaws Amrin (amrincse26@URL)
## License
This project is licensed under the MIT License - see the LICENSE file for details.
---
|
[
"# English to Spanish Machine Translation",
"## Introduction:\nIn this project, we'll build a sequence-to-sequence Transformer model, which we'll train on an English-to-Spanish machine translation task.\n\nIn this task we will learn:\n- Vectorize text using the Keras TextVectorization layer.\n- Implement a TransformerEncoder layer, a TransformerDecoder layer, and a PositionalEmbedding layer.\n- Prepare data for training a sequence-to-sequence model.\n- Use the trained model to generate translations of never-seen-before input sentences (sequence-to-sequence inference).",
"## Dataset Collection:\nWe'll be working with an English-to-Spanish translation dataset provided by Anki from this source: \n\"URL",
"## Dependencies\n- numpy\n- keras\n- tensorflow",
"## Data Processing\n\n- Every line has a sentence in English and a comparable sentence in Spanish. The target sequence is the Spanish sentence, and the source sequence is the English sentence. To the Spanish sentence, we prepend the token \"[start]\" and attach the token \"[end]\".\n- To vectorize the text data, we will utilize two instances of the TextVectorization layer (one for English and one for Spanish). This means that instead of the original strings, we will convert them into integer sequences, where each integer is the index of a word in a vocabulary.\n- At each training step, the model will seek to predict target words N+1 (and beyond) using the source sentence and the target words 0 to N.\n\n- We have batches of 64 pairs, and all sequences are 20 steps long.",
"## Model Architecture:\nThe model architecture consists of an Encoder-Decoder LSTM network with an embedding layer.To make the model aware of word order, we also use a PositionalEmbedding layer.\n\nThe TransformerEncoder will receive the source sequence and create a new representation of it. The target sequence up to this point (target words 0 to N) will be delivered to the TransformerDecoder together with this updated representation. Next, the TransformerDecoder will try to anticipate words N+1 and beyond in the target sequence.\n Since the TransformerDecoder views all of the sequences at once, we have to make sure that when it predicts token N+1, it only takes information from target tokens 0 to N. If we don't, it might use information from the future, which would produce a model that is unusable at inference time.",
"### Training the Model:\nAccuracy will be used as a fast approach to track training results on validation data. Keep in mind that BLEU scores and other measures are usually used by machine translation algorithms, not accuracy alone.\n\nIn this case, we are only training for one epoch; however, you need train for at least thirty epochs in order to get the model to converge.",
"### Result Analysis:\nThe vectorized English text and the goal token \"[start]\" are simply fed into the model. We then continuously produce the following token until we reach the token \"[end]\".",
"## Contributor\nJanaatul Ferdaws Amrin (amrincse26@URL)",
"## License\nThis project is licensed under the MIT License - see the LICENSE file for details.\n\n---"
] |
[
"TAGS\n#license-mit #region-us \n",
"# English to Spanish Machine Translation",
"## Introduction:\nIn this project, we'll build a sequence-to-sequence Transformer model, which we'll train on an English-to-Spanish machine translation task.\n\nIn this task we will learn:\n- Vectorize text using the Keras TextVectorization layer.\n- Implement a TransformerEncoder layer, a TransformerDecoder layer, and a PositionalEmbedding layer.\n- Prepare data for training a sequence-to-sequence model.\n- Use the trained model to generate translations of never-seen-before input sentences (sequence-to-sequence inference).",
"## Dataset Collection:\nWe'll be working with an English-to-Spanish translation dataset provided by Anki from this source: \n\"URL",
"## Dependencies\n- numpy\n- keras\n- tensorflow",
"## Data Processing\n\n- Every line has a sentence in English and a comparable sentence in Spanish. The target sequence is the Spanish sentence, and the source sequence is the English sentence. To the Spanish sentence, we prepend the token \"[start]\" and attach the token \"[end]\".\n- To vectorize the text data, we will utilize two instances of the TextVectorization layer (one for English and one for Spanish). This means that instead of the original strings, we will convert them into integer sequences, where each integer is the index of a word in a vocabulary.\n- At each training step, the model will seek to predict target words N+1 (and beyond) using the source sentence and the target words 0 to N.\n\n- We have batches of 64 pairs, and all sequences are 20 steps long.",
"## Model Architecture:\nThe model architecture consists of an Encoder-Decoder LSTM network with an embedding layer.To make the model aware of word order, we also use a PositionalEmbedding layer.\n\nThe TransformerEncoder will receive the source sequence and create a new representation of it. The target sequence up to this point (target words 0 to N) will be delivered to the TransformerDecoder together with this updated representation. Next, the TransformerDecoder will try to anticipate words N+1 and beyond in the target sequence.\n Since the TransformerDecoder views all of the sequences at once, we have to make sure that when it predicts token N+1, it only takes information from target tokens 0 to N. If we don't, it might use information from the future, which would produce a model that is unusable at inference time.",
"### Training the Model:\nAccuracy will be used as a fast approach to track training results on validation data. Keep in mind that BLEU scores and other measures are usually used by machine translation algorithms, not accuracy alone.\n\nIn this case, we are only training for one epoch; however, you need train for at least thirty epochs in order to get the model to converge.",
"### Result Analysis:\nThe vectorized English text and the goal token \"[start]\" are simply fed into the model. We then continuously produce the following token until we reach the token \"[end]\".",
"## Contributor\nJanaatul Ferdaws Amrin (amrincse26@URL)",
"## License\nThis project is licensed under the MIT License - see the LICENSE file for details.\n\n---"
] |
text-classification
|
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. -->
# results4.2
This model is a fine-tuned version of [jkhan447/sarcasm-detection-Bert-base-uncased](https://huggingface.co/jkhan447/sarcasm-detection-Bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6805
- Accuracy: 0.6916
- F1: 0.4216
## 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: 500
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "jkhan447/sarcasm-detection-Bert-base-uncased", "model-index": [{"name": "results4.2", "results": []}]}
|
dianamihalache27/results4.2
| null |
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:jkhan447/sarcasm-detection-Bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-13T13:05:59+00:00
|
[] |
[] |
TAGS
#transformers #safetensors #bert #text-classification #generated_from_trainer #base_model-jkhan447/sarcasm-detection-Bert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# results4.2
This model is a fine-tuned version of jkhan447/sarcasm-detection-Bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6805
- Accuracy: 0.6916
- F1: 0.4216
## 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: 500
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
[
"# results4.2\n\nThis model is a fine-tuned version of jkhan447/sarcasm-detection-Bert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.6805\n- Accuracy: 0.6916\n- F1: 0.4216",
"## 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: 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: 500\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.2\n- Pytorch 2.2.1+cu121\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] |
[
"TAGS\n#transformers #safetensors #bert #text-classification #generated_from_trainer #base_model-jkhan447/sarcasm-detection-Bert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# results4.2\n\nThis model is a fine-tuned version of jkhan447/sarcasm-detection-Bert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.6805\n- Accuracy: 0.6916\n- F1: 0.4216",
"## 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: 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: 500\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.2\n- Pytorch 2.2.1+cu121\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] |
text-generation
|
transformers
|
# Model Card for Mixtral-8x22B-Instruct
The Mixtral-8x22B Large Language Model (LLM) is a pretrained generative Sparse Mixture of Experts.
For full details of this model please read our [release blog post](https://mistral.ai/news/mixtral-of-experts/).
## Instruction format
The template used to build a prompt for the Instruct model is defined as follows:
```
<|im_start|>system
You are an unbiased, uncensored, helpful assistant.<|im_end|>
<|im_start|>user
Hello, Who are you?<|im_end|>
<|im_start|>assistant
```
In the Transformers library, one can use [chat templates](https://huggingface.co/docs/transformers/main/en/chat_templating) which make sure the right format is applied.
## Run the model
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "PawanKrd/Mixtral-8x22B-Instruct-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
messages = [
{"role": "user", "content": "What is your favourite condiment?"},
{"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
{"role": "user", "content": "Do you have mayonnaise recipes?"}
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda")
outputs = model.generate(inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
By default, transformers will load the model in full precision. Therefore you might be interested to further reduce down the memory requirements to run the model through the optimizations we offer in HF ecosystem:
### In half-precision
Note `float16` precision only works on GPU devices
<details>
<summary> Click to expand </summary>
```diff
+ import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "PawanKrd/Mixtral-8x22B-Instruct-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
+ model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")
messages = [
{"role": "user", "content": "What is your favourite condiment?"},
{"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
{"role": "user", "content": "Do you have mayonnaise recipes?"}
]
input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda")
outputs = model.generate(input_ids, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
</details>
### Lower precision using (8-bit & 4-bit) using `bitsandbytes`
<details>
<summary> Click to expand </summary>
```diff
+ import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "PawanKrd/Mixtral-8x22B-Instruct-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
+ model = AutoModelForCausalLM.from_pretrained(model_id, load_in_4bit=True, device_map="auto")
text = "Hello my name is"
messages = [
{"role": "user", "content": "What is your favourite condiment?"},
{"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
{"role": "user", "content": "Do you have mayonnaise recipes?"}
]
input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda")
outputs = model.generate(input_ids, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
</details>
### Load the model with Flash Attention 2
<details>
<summary> Click to expand </summary>
```diff
+ import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "PawanKrd/Mixtral-8x22B-Instruct-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
+ model = AutoModelForCausalLM.from_pretrained(model_id, use_flash_attention_2=True, device_map="auto")
messages = [
{"role": "user", "content": "What is your favourite condiment?"},
{"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
{"role": "user", "content": "Do you have mayonnaise recipes?"}
]
input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda")
outputs = model.generate(input_ids, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
</details>
# Training
Fine-tuned on 8xH100 80GB GPUs
# The Mistral AI Team
Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Louis Ternon, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.
|
{"language": ["fr", "it", "de", "es", "en"], "license": "apache-2.0", "inference": {"parameters": {"temperature": 0.5}}, "widget": [{"messages": [{"role": "user", "content": "What is your favorite condiment?"}]}]}
|
PawanKrd/Mixtral-8x22B-Instruct-v0.1
| null |
[
"transformers",
"safetensors",
"mixtral",
"text-generation",
"conversational",
"fr",
"it",
"de",
"es",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-13T13:07:19+00:00
|
[] |
[
"fr",
"it",
"de",
"es",
"en"
] |
TAGS
#transformers #safetensors #mixtral #text-generation #conversational #fr #it #de #es #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Mixtral-8x22B-Instruct
The Mixtral-8x22B Large Language Model (LLM) is a pretrained generative Sparse Mixture of Experts.
For full details of this model please read our release blog post.
## Instruction format
The template used to build a prompt for the Instruct model is defined as follows:
In the Transformers library, one can use chat templates which make sure the right format is applied.
## Run the model
By default, transformers will load the model in full precision. Therefore you might be interested to further reduce down the memory requirements to run the model through the optimizations we offer in HF ecosystem:
### In half-precision
Note 'float16' precision only works on GPU devices
<details>
<summary> Click to expand </summary>
</details>
### Lower precision using (8-bit & 4-bit) using 'bitsandbytes'
<details>
<summary> Click to expand </summary>
</details>
### Load the model with Flash Attention 2
<details>
<summary> Click to expand </summary>
</details>
# Training
Fine-tuned on 8xH100 80GB GPUs
# The Mistral AI Team
Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Louis Ternon, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.
|
[
"# Model Card for Mixtral-8x22B-Instruct\nThe Mixtral-8x22B Large Language Model (LLM) is a pretrained generative Sparse Mixture of Experts.\n\nFor full details of this model please read our release blog post.",
"## Instruction format\n\nThe template used to build a prompt for the Instruct model is defined as follows:\n\n\nIn the Transformers library, one can use chat templates which make sure the right format is applied.",
"## Run the model\n\n\n\nBy default, transformers will load the model in full precision. Therefore you might be interested to further reduce down the memory requirements to run the model through the optimizations we offer in HF ecosystem:",
"### In half-precision\n\nNote 'float16' precision only works on GPU devices\n\n<details>\n<summary> Click to expand </summary>\n\n\n</details>",
"### Lower precision using (8-bit & 4-bit) using 'bitsandbytes'\n\n<details>\n<summary> Click to expand </summary>\n\n\n</details>",
"### Load the model with Flash Attention 2\n\n<details>\n<summary> Click to expand </summary>\n\n\n</details>",
"# Training\nFine-tuned on 8xH100 80GB GPUs",
"# The Mistral AI Team\nAlbert Jiang, Alexandre Sablayrolles, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Louis Ternon, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed."
] |
[
"TAGS\n#transformers #safetensors #mixtral #text-generation #conversational #fr #it #de #es #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Mixtral-8x22B-Instruct\nThe Mixtral-8x22B Large Language Model (LLM) is a pretrained generative Sparse Mixture of Experts.\n\nFor full details of this model please read our release blog post.",
"## Instruction format\n\nThe template used to build a prompt for the Instruct model is defined as follows:\n\n\nIn the Transformers library, one can use chat templates which make sure the right format is applied.",
"## Run the model\n\n\n\nBy default, transformers will load the model in full precision. Therefore you might be interested to further reduce down the memory requirements to run the model through the optimizations we offer in HF ecosystem:",
"### In half-precision\n\nNote 'float16' precision only works on GPU devices\n\n<details>\n<summary> Click to expand </summary>\n\n\n</details>",
"### Lower precision using (8-bit & 4-bit) using 'bitsandbytes'\n\n<details>\n<summary> Click to expand </summary>\n\n\n</details>",
"### Load the model with Flash Attention 2\n\n<details>\n<summary> Click to expand </summary>\n\n\n</details>",
"# Training\nFine-tuned on 8xH100 80GB GPUs",
"# The Mistral AI Team\nAlbert Jiang, Alexandre Sablayrolles, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Louis Ternon, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed."
] |
reinforcement-learning
|
stable-baselines3
|
# **We used the architecture of the MlpPolicy** Agent playing **LunarLander-v2**
This is a trained model of a **We used the architecture of the MlpPolicy** 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": "We used the architecture of the MlpPolicy", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "285.97 +/- 29.30", "name": "mean_reward", "verified": false}]}]}]}
|
FitTechMike/ppo-LunarLander-v2
| null |
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null |
2024-04-13T13:07:52+00:00
|
[] |
[] |
TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# We used the architecture of the MlpPolicy Agent playing LunarLander-v2
This is a trained model of a We used the architecture of the MlpPolicy agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
|
[
"# We used the architecture of the MlpPolicy Agent playing LunarLander-v2\nThis is a trained model of a We used the architecture of the MlpPolicy 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",
"# We used the architecture of the MlpPolicy Agent playing LunarLander-v2\nThis is a trained model of a We used the architecture of the MlpPolicy agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] |
null |
adapter-transformers
|
# Adapter `BigTMiami/G_adapter_compactor_classification_P_20_to_C_30` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [BigTMiami/amazon_helpfulness](https://huggingface.co/datasets/BigTMiami/amazon_helpfulness/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("roberta-base")
adapter_name = model.load_adapter("BigTMiami/G_adapter_compactor_classification_P_20_to_C_30", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
{"tags": ["adapter-transformers", "roberta"], "datasets": ["BigTMiami/amazon_helpfulness"]}
|
BigTMiami/G_adapter_compactor_classification_P_20_to_C_30
| null |
[
"adapter-transformers",
"roberta",
"dataset:BigTMiami/amazon_helpfulness",
"region:us"
] | null |
2024-04-13T13:08:21+00:00
|
[] |
[] |
TAGS
#adapter-transformers #roberta #dataset-BigTMiami/amazon_helpfulness #region-us
|
# Adapter 'BigTMiami/G_adapter_compactor_classification_P_20_to_C_30' for roberta-base
An adapter for the 'roberta-base' model that was trained on the BigTMiami/amazon_helpfulness dataset and includes a prediction head for classification.
This adapter was created for usage with the Adapters library.
## Usage
First, install 'adapters':
Now, the adapter can be loaded and activated like this:
## Architecture & Training
## Evaluation results
|
[
"# Adapter 'BigTMiami/G_adapter_compactor_classification_P_20_to_C_30' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/amazon_helpfulness dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] |
[
"TAGS\n#adapter-transformers #roberta #dataset-BigTMiami/amazon_helpfulness #region-us \n",
"# Adapter 'BigTMiami/G_adapter_compactor_classification_P_20_to_C_30' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/amazon_helpfulness dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] |
null | null |
# Cran-May/Mistral-22B-v0.2-Q4_K_S-GGUF
This model was converted to GGUF format from [`Vezora/Mistral-22B-v0.2`](https://huggingface.co/Vezora/Mistral-22B-v0.2) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Vezora/Mistral-22B-v0.2) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo Cran-May/Mistral-22B-v0.2-Q4_K_S-GGUF --model mistral-22b-v0.2.Q4_K_S.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo Cran-May/Mistral-22B-v0.2-Q4_K_S-GGUF --model mistral-22b-v0.2.Q4_K_S.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m mistral-22b-v0.2.Q4_K_S.gguf -n 128
```
|
{"license": "apache-2.0", "tags": ["llama-cpp", "gguf-my-repo"]}
|
Cran-May/Mistral-22B-v0.2-Q4_K_S-GGUF
| null |
[
"gguf",
"llama-cpp",
"gguf-my-repo",
"license:apache-2.0",
"region:us"
] | null |
2024-04-13T13:08:52+00:00
|
[] |
[] |
TAGS
#gguf #llama-cpp #gguf-my-repo #license-apache-2.0 #region-us
|
# Cran-May/Mistral-22B-v0.2-Q4_K_S-GGUF
This model was converted to GGUF format from 'Vezora/Mistral-22B-v0.2' using URL via the URL's GGUF-my-repo space.
Refer to the original model card for more details on the model.
## Use with URL
Install URL through brew.
Invoke the URL server or the CLI.
CLI:
Server:
Note: You can also use this checkpoint directly through the usage steps listed in the URL repo as well.
|
[
"# Cran-May/Mistral-22B-v0.2-Q4_K_S-GGUF\nThis model was converted to GGUF format from 'Vezora/Mistral-22B-v0.2' using URL via the URL's GGUF-my-repo space.\nRefer to the original model card for more details on the model.",
"## Use with URL\n\nInstall URL through brew.\n\n\nInvoke the URL server or the CLI.\n\nCLI:\n\n\n\nServer:\n\n\n\nNote: You can also use this checkpoint directly through the usage steps listed in the URL repo as well."
] |
[
"TAGS\n#gguf #llama-cpp #gguf-my-repo #license-apache-2.0 #region-us \n",
"# Cran-May/Mistral-22B-v0.2-Q4_K_S-GGUF\nThis model was converted to GGUF format from 'Vezora/Mistral-22B-v0.2' using URL via the URL's GGUF-my-repo space.\nRefer to the original model card for more details on the model.",
"## Use with URL\n\nInstall URL through brew.\n\n\nInvoke the URL server or the CLI.\n\nCLI:\n\n\n\nServer:\n\n\n\nNote: You can also use this checkpoint directly through the usage steps listed in the URL repo as well."
] |
text-to-image
|
diffusers
|
# helltaker
<Gallery />
## Download model
Weights for this model are available in Safetensors format.
[Download](/zaxcal/helltaker/tree/main) them in the Files & versions tab.
|
{"tags": ["text-to-image", "stable-diffusion", "lora", "diffusers", "template:sd-lora"], "widget": [{"text": "UNICODE\u0000\u0000h\u0000e\u0000l\u0000l\u0000t\u0000a\u0000k\u0000e\u0000r\u0000,\u0000 \u0000m\u0000a\u0000s\u0000t\u0000e\u0000r\u0000p\u0000i\u0000e\u0000c\u0000e\u0000,\u0000 \u0000b\u0000e\u0000s\u0000t\u0000 \u0000q\u0000u\u0000a\u0000l\u0000i\u0000t\u0000y\u0000,\u0000 \u0000f\u0000r\u0000o\u0000m\u0000 \u0000s\u0000i\u0000d\u0000e\u0000,\u0000 \u0000f\u0000o\u0000c\u0000u\u0000s\u0000 \u0000a\u0000w\u0000a\u0000y\u0000,\u0000 \u0000c\u0000o\u0000m\u0000e\u0000 \u0000o\u0000n\u0000 \u0000w\u0000a\u0000t\u0000e\u0000r\u0000,\u0000 \u0000d\u0000r\u0000e\u0000a\u0000m\u0000,\u0000 \u0000f\u0000a\u0000n\u0000t\u0000a\u0000s\u0000y\u0000,\u0000 \u00001\u0000g\u0000i\u0000r\u0000l\u0000,\u0000 \u0000s\u0000h\u0000o\u0000r\u0000t\u0000 \u0000h\u0000a\u0000i\u0000r\u0000,\u0000 \u0000(\u0000N\u0000a\u0000v\u0000y\u0000 \u0000h\u0000a\u0000i\u0000r\u0000:\u00001\u0000.\u00000\u00005\u0000)\u0000,\u0000 \u0000c\u0000l\u0000e\u0000a\u0000r\u0000 \u0000r\u0000e\u0000d\u0000 \u0000e\u0000y\u0000e\u0000s\u0000,\u0000 \u0000h\u0000o\u0000o\u0000d\u0000i\u0000e\u0000 \u0000w\u0000h\u0000i\u0000t\u0000e\u0000 \u0000z\u0000i\u0000p\u0000,\u0000 \u0000w\u0000h\u0000i\u0000t\u0000e\u0000 \u0000s\u0000h\u0000o\u0000e\u0000s\u0000,\u0000 \u0000S\u0000u\u0000m\u0000m\u0000o\u0000n\u0000e\u0000r\u0000,\u0000 \u0000(\u0000m\u0000a\u0000g\u0000i\u0000c\u0000 \u0000c\u0000i\u0000r\u0000c\u0000l\u0000e\u0000:\u00001\u0000.\u00001\u00006\u0000)\u0000,\u0000 \u0000(\u0000e\u0000m\u0000b\u0000a\u0000r\u0000r\u0000a\u0000s\u0000s\u0000i\u0000n\u0000g\u0000 \u0000f\u0000a\u0000c\u0000e\u0000:\u00001\u0000.\u00001\u0000)\u0000,\u0000 \u0000s\u0000k\u0000y\u0000 \u0000b\u0000a\u0000c\u0000k\u0000g\u0000r\u0000o\u0000u\u0000n\u0000d\u0000,\u0000 \u0000b\u0000e\u0000a\u0000u\u0000t\u0000i\u0000f\u0000u\u0000l\u0000 \u0000d\u0000e\u0000t\u0000a\u0000i\u0000l\u0000e\u0000d\u0000 \u0000w\u0000a\u0000t\u0000e\u0000r\u0000,\u0000 \u0000d\u0000y\u0000n\u0000a\u0000m\u0000i\u0000c\u0000,\u0000 \u0000h\u0000i\u0000g\u0000h\u0000 \u0000r\u0000e\u0000s\u0000o\u0000l\u0000u\u0000t\u0000i\u0000o\u0000n\u0000 \u0000i\u0000l\u0000l\u0000u\u0000s\u0000t\u0000r\u0000a\u0000t\u0000i\u0000o\u0000n\u0000,\u0000 \u0000m\u0000a\u0000s\u0000t\u0000e\u0000r\u0000i\u0000p\u0000i\u0000e\u0000c\u0000e\u0000,\u0000 \u0000b\u0000e\u0000s\u0000t\u0000 \u0000q\u0000u\u0000a\u0000l\u0000i\u0000t\u0000y\u0000,\u0000 \u0000f\u0000i\u0000n\u0000e\u0000l\u0000y\u0000 \u0000d\u0000e\u0000t\u0000a\u0000i\u0000l\u0000e\u0000d\u0000 \u0000e\u0000y\u0000e\u0000s\u0000,\u0000 \u0000w\u0000a\u0000t\u0000e\u0000r\u0000,\u0000 \u0000s\u0000k\u0000y\u0000,\u0000 \u0000f\u0000u\u0000l\u0000l\u0000 \u0000s\u0000h\u0000o\u0000t\u0000,\u0000 \u0000f\u0000u\u0000l\u0000l\u0000 \u0000b\u0000o\u0000d\u0000y\u0000,\u0000 \u0000b\u0000l\u0000u\u0000e\u0000 \u0000s\u0000k\u0000y\u0000,\u0000 \u0000m\u0000e\u0000d\u0000i\u0000u\u0000m\u0000 \u0000b\u0000r\u0000e\u0000a\u0000s\u0000t\u0000s\u0000,\u0000 \u0000c\u0000l\u0000o\u0000s\u0000e\u0000d\u0000 \u0000m\u0000o\u0000u\u0000t\u0000h\u0000", "output": {"url": "images/02458-1668977201.jpeg"}}], "base_model": "NikoEternal/HellTaker"}
|
zaxcal/helltaker
| null |
[
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:NikoEternal/HellTaker",
"region:us"
] | null |
2024-04-13T13:08:56+00:00
|
[] |
[] |
TAGS
#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-NikoEternal/HellTaker #region-us
|
# helltaker
<Gallery />
## Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.
|
[
"# helltaker\n\n<Gallery />",
"## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab."
] |
[
"TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-NikoEternal/HellTaker #region-us \n",
"# helltaker\n\n<Gallery />",
"## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab."
] |
reinforcement-learning
| null |
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'aa-unh/lunarlander-scratch'
'batch_size': 512
'minibatch_size': 128}
```
|
{"tags": ["LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "-161.06 +/- 131.71", "name": "mean_reward", "verified": false}]}]}]}
|
aa-unh/lunarlander-scratch
| null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] | null |
2024-04-13T13:09:02+00:00
|
[] |
[] |
TAGS
#tensorboard #LunarLander-v2 #ppo #deep-reinforcement-learning #reinforcement-learning #custom-implementation #deep-rl-course #model-index #region-us
|
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
|
[
"# PPO Agent Playing LunarLander-v2\n\n This is a trained model of a PPO agent playing LunarLander-v2.\n\n # Hyperparameters"
] |
[
"TAGS\n#tensorboard #LunarLander-v2 #ppo #deep-reinforcement-learning #reinforcement-learning #custom-implementation #deep-rl-course #model-index #region-us \n",
"# PPO Agent Playing LunarLander-v2\n\n This is a trained model of a PPO agent playing LunarLander-v2.\n\n # Hyperparameters"
] |
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:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.10.0
|
{"library_name": "peft", "base_model": "mistralai/Mistral-7B-Instruct-v0.2"}
|
akshaysaxena/Enlighten_Instruct
| null |
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"region:us"
] | null |
2024-04-13T13:09:16+00:00
|
[
"1910.09700"
] |
[] |
TAGS
#peft #safetensors #arxiv-1910.09700 #base_model-mistralai/Mistral-7B-Instruct-v0.2 #region-us
|
# 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]
## 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
### Framework versions
- PEFT 0.10.0
|
[
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\n\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",
"### Framework versions\n\n- PEFT 0.10.0"
] |
[
"TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-mistralai/Mistral-7B-Instruct-v0.2 #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\n\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",
"### Framework versions\n\n- PEFT 0.10.0"
] |
reinforcement-learning
|
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": "241.14 +/- 14.72", "name": "mean_reward", "verified": false}]}]}]}
|
toure32/ppo-LunarLander-v2
| null |
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null |
2024-04-13T13:09:28+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"
] |
null |
transformers
|
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/migtissera/Tess-2.0-Mixtral-8x22B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x22B-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [PART 1](https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x22B-GGUF/resolve/main/Tess-2.0-Mixtral-8x22B.IQ3_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x22B-GGUF/resolve/main/Tess-2.0-Mixtral-8x22B.IQ3_S.gguf.part2of2) | IQ3_S | 61.6 | beats Q3_K* |
| [PART 1](https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x22B-GGUF/resolve/main/Tess-2.0-Mixtral-8x22B.IQ3_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x22B-GGUF/resolve/main/Tess-2.0-Mixtral-8x22B.IQ3_M.gguf.part2of2) | IQ3_M | 64.6 | |
| [PART 1](https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x22B-GGUF/resolve/main/Tess-2.0-Mixtral-8x22B.Q4_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x22B-GGUF/resolve/main/Tess-2.0-Mixtral-8x22B.Q4_K_S.gguf.part2of2) | Q4_K_S | 80.6 | fast, recommended |
| [PART 1](https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x22B-GGUF/resolve/main/Tess-2.0-Mixtral-8x22B.Q8_0.gguf.part1of4) [PART 2](https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x22B-GGUF/resolve/main/Tess-2.0-Mixtral-8x22B.Q8_0.gguf.part2of4) [PART 3](https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x22B-GGUF/resolve/main/Tess-2.0-Mixtral-8x22B.Q8_0.gguf.part3of4) [PART 4](https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x22B-GGUF/resolve/main/Tess-2.0-Mixtral-8x22B.Q8_0.gguf.part4of4) | Q8_0 | 149.5 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
{"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "base_model": "migtissera/Tess-2.0-Mixtral-8x22B", "quantized_by": "mradermacher"}
|
mradermacher/Tess-2.0-Mixtral-8x22B-GGUF
| null |
[
"transformers",
"en",
"base_model:migtissera/Tess-2.0-Mixtral-8x22B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2024-04-13T13:13:08+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #en #base_model-migtissera/Tess-2.0-Mixtral-8x22B #license-apache-2.0 #endpoints_compatible #region-us
|
About
-----
static quants of URL
weighted/imatrix quants are available at URL
Usage
-----
If you are unsure how to use GGUF files, refer to one of TheBloke's
READMEs for
more details, including on how to concatenate multi-part files.
Provided Quants
---------------
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
!URL
And here are Artefact2's thoughts on the matter:
URL
FAQ / Model Request
-------------------
See URL for some answers to
questions you might have and/or if you want some other model quantized.
Thanks
------
I thank my company, nethype GmbH, for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
|
[] |
[
"TAGS\n#transformers #en #base_model-migtissera/Tess-2.0-Mixtral-8x22B #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
text-classification
|
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. -->
# results5
This model is a fine-tuned version of [jkhan447/sarcasm-detection-Bert-base-uncased-CR](https://huggingface.co/jkhan447/sarcasm-detection-Bert-base-uncased-CR) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7753
- Accuracy: 0.6916
- F1: 0.4216
## 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: 500
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "jkhan447/sarcasm-detection-Bert-base-uncased-CR", "model-index": [{"name": "results5", "results": []}]}
|
dianamihalache27/results5
| null |
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:jkhan447/sarcasm-detection-Bert-base-uncased-CR",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-13T13:14:45+00:00
|
[] |
[] |
TAGS
#transformers #safetensors #bert #text-classification #generated_from_trainer #base_model-jkhan447/sarcasm-detection-Bert-base-uncased-CR #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# results5
This model is a fine-tuned version of jkhan447/sarcasm-detection-Bert-base-uncased-CR on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7753
- Accuracy: 0.6916
- F1: 0.4216
## 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: 500
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
[
"# results5\n\nThis model is a fine-tuned version of jkhan447/sarcasm-detection-Bert-base-uncased-CR on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.7753\n- Accuracy: 0.6916\n- F1: 0.4216",
"## 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: 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: 500\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.2\n- Pytorch 2.2.1+cu121\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] |
[
"TAGS\n#transformers #safetensors #bert #text-classification #generated_from_trainer #base_model-jkhan447/sarcasm-detection-Bert-base-uncased-CR #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# results5\n\nThis model is a fine-tuned version of jkhan447/sarcasm-detection-Bert-base-uncased-CR on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.7753\n- Accuracy: 0.6916\n- F1: 0.4216",
"## 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: 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: 500\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.2\n- Pytorch 2.2.1+cu121\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] |
text-generation
|
transformers
|
# Model mera-mix-4x7B
This is a mixture of experts (MoE) model that is half as large (4 experts instead of 8) as the [Mixtral-8x7B](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1)
while been comparable to it across different benchmarks. You can use it as a drop in replacement for your Mixtral-8x7B and get much faster inference.
mera-mix-4x7B achieves the score of 75.91 on the OpenLLM Eval and compares well with 72.7 by Mixtral-8x7B and 74.46 by Mixtral-8x22B.
You can try the model with the [Mera Mixture Chat](https://huggingface.co/spaces/meraGPT/mera-mixture-chat).
In addition, to the official Open LLM Leaderboard, the results on OpenLLM Eval have been validated by [others as well (76.59)](https://github.com/saucam/model_evals/tree/main?tab=readme-ov-file#model-eval-results).
Our own initial eval is available [here (76.37)](https://gist.github.com/codelion/78f88333230801c9bbaa6fc22078d820).
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_meraGPT__mera-mix-4x7B)
| Metric |Value|
|---------------------------------|----:|
|Avg. |75.91|
|AI2 Reasoning Challenge (25-Shot)|72.95|
|HellaSwag (10-Shot) |89.17|
|MMLU (5-Shot) |64.44|
|TruthfulQA (0-shot) |77.17|
|Winogrande (5-shot) |85.64|
|GSM8k (5-shot) |66.11|
|
{"license": "apache-2.0", "model-index": [{"name": "mera-mix-4x7B", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "AI2 Reasoning Challenge (25-Shot)", "type": "ai2_arc", "config": "ARC-Challenge", "split": "test", "args": {"num_few_shot": 25}}, "metrics": [{"type": "acc_norm", "value": 72.95, "name": "normalized accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=meraGPT/mera-mix-4x7B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "HellaSwag (10-Shot)", "type": "hellaswag", "split": "validation", "args": {"num_few_shot": 10}}, "metrics": [{"type": "acc_norm", "value": 89.17, "name": "normalized accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=meraGPT/mera-mix-4x7B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "MMLU (5-Shot)", "type": "cais/mmlu", "config": "all", "split": "test", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 64.44, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=meraGPT/mera-mix-4x7B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "TruthfulQA (0-shot)", "type": "truthful_qa", "config": "multiple_choice", "split": "validation", "args": {"num_few_shot": 0}}, "metrics": [{"type": "mc2", "value": 77.17}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=meraGPT/mera-mix-4x7B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "Winogrande (5-shot)", "type": "winogrande", "config": "winogrande_xl", "split": "validation", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 85.64, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=meraGPT/mera-mix-4x7B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "GSM8k (5-shot)", "type": "gsm8k", "config": "main", "split": "test", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 66.11, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=meraGPT/mera-mix-4x7B", "name": "Open LLM Leaderboard"}}]}]}
|
meraGPT/mera-mix-4x7B
| null |
[
"transformers",
"safetensors",
"mixtral",
"text-generation",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null |
2024-04-13T13:21:18+00:00
|
[] |
[] |
TAGS
#transformers #safetensors #mixtral #text-generation #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
Model mera-mix-4x7B
===================
This is a mixture of experts (MoE) model that is half as large (4 experts instead of 8) as the Mixtral-8x7B
while been comparable to it across different benchmarks. You can use it as a drop in replacement for your Mixtral-8x7B and get much faster inference.
mera-mix-4x7B achieves the score of 75.91 on the OpenLLM Eval and compares well with 72.7 by Mixtral-8x7B and 74.46 by Mixtral-8x22B.
You can try the model with the Mera Mixture Chat.
In addition, to the official Open LLM Leaderboard, the results on OpenLLM Eval have been validated by others as well (76.59).
Our own initial eval is available here (76.37).
Open LLM Leaderboard Evaluation Results
=======================================
Detailed results can be found here
|
[] |
[
"TAGS\n#transformers #safetensors #mixtral #text-generation #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text-classification
|
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. -->
# results6
This model is a fine-tuned version of [jkhan447/sarcasm-detection-Bert-base-uncased-CR-POS](https://huggingface.co/jkhan447/sarcasm-detection-Bert-base-uncased-CR-POS) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6308
- Accuracy: 0.6945
- F1: 0.3537
## 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: 500
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "jkhan447/sarcasm-detection-Bert-base-uncased-CR-POS", "model-index": [{"name": "results6", "results": []}]}
|
dianamihalache27/results6
| null |
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:jkhan447/sarcasm-detection-Bert-base-uncased-CR-POS",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-13T13:21:30+00:00
|
[] |
[] |
TAGS
#transformers #safetensors #bert #text-classification #generated_from_trainer #base_model-jkhan447/sarcasm-detection-Bert-base-uncased-CR-POS #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# results6
This model is a fine-tuned version of jkhan447/sarcasm-detection-Bert-base-uncased-CR-POS on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6308
- Accuracy: 0.6945
- F1: 0.3537
## 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: 500
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
[
"# results6\n\nThis model is a fine-tuned version of jkhan447/sarcasm-detection-Bert-base-uncased-CR-POS on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.6308\n- Accuracy: 0.6945\n- F1: 0.3537",
"## 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: 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: 500\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.2\n- Pytorch 2.2.1+cu121\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] |
[
"TAGS\n#transformers #safetensors #bert #text-classification #generated_from_trainer #base_model-jkhan447/sarcasm-detection-Bert-base-uncased-CR-POS #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# results6\n\nThis model is a fine-tuned version of jkhan447/sarcasm-detection-Bert-base-uncased-CR-POS on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.6308\n- Accuracy: 0.6945\n- F1: 0.3537",
"## 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: 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: 500\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.2\n- Pytorch 2.2.1+cu121\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] |
null | null |
# Cran-May/Mistral-22B-v0.2-Q4_K_M-GGUF
This model was converted to GGUF format from [`Vezora/Mistral-22B-v0.2`](https://huggingface.co/Vezora/Mistral-22B-v0.2) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Vezora/Mistral-22B-v0.2) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo Cran-May/Mistral-22B-v0.2-Q4_K_M-GGUF --model mistral-22b-v0.2.Q4_K_M.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo Cran-May/Mistral-22B-v0.2-Q4_K_M-GGUF --model mistral-22b-v0.2.Q4_K_M.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m mistral-22b-v0.2.Q4_K_M.gguf -n 128
```
|
{"license": "apache-2.0", "tags": ["llama-cpp", "gguf-my-repo"]}
|
Cran-May/Mistral-22B-v0.2-Q4_K_M-GGUF
| null |
[
"gguf",
"llama-cpp",
"gguf-my-repo",
"license:apache-2.0",
"region:us"
] | null |
2024-04-13T13:22:22+00:00
|
[] |
[] |
TAGS
#gguf #llama-cpp #gguf-my-repo #license-apache-2.0 #region-us
|
# Cran-May/Mistral-22B-v0.2-Q4_K_M-GGUF
This model was converted to GGUF format from 'Vezora/Mistral-22B-v0.2' using URL via the URL's GGUF-my-repo space.
Refer to the original model card for more details on the model.
## Use with URL
Install URL through brew.
Invoke the URL server or the CLI.
CLI:
Server:
Note: You can also use this checkpoint directly through the usage steps listed in the URL repo as well.
|
[
"# Cran-May/Mistral-22B-v0.2-Q4_K_M-GGUF\nThis model was converted to GGUF format from 'Vezora/Mistral-22B-v0.2' using URL via the URL's GGUF-my-repo space.\nRefer to the original model card for more details on the model.",
"## Use with URL\n\nInstall URL through brew.\n\n\nInvoke the URL server or the CLI.\n\nCLI:\n\n\n\nServer:\n\n\n\nNote: You can also use this checkpoint directly through the usage steps listed in the URL repo as well."
] |
[
"TAGS\n#gguf #llama-cpp #gguf-my-repo #license-apache-2.0 #region-us \n",
"# Cran-May/Mistral-22B-v0.2-Q4_K_M-GGUF\nThis model was converted to GGUF format from 'Vezora/Mistral-22B-v0.2' using URL via the URL's GGUF-my-repo space.\nRefer to the original model card for more details on the model.",
"## Use with URL\n\nInstall URL through brew.\n\n\nInvoke the URL server or the CLI.\n\nCLI:\n\n\n\nServer:\n\n\n\nNote: You can also use this checkpoint directly through the usage steps listed in the URL repo as well."
] |
null |
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
{"library_name": "transformers", "tags": []}
|
HackerCIS/gemma-2b_LoRA
| null |
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null |
2024-04-13T13:23:47+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"
] |
feature-extraction
|
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
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[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
|
{"library_name": "transformers", "tags": []}
|
stvhuang/rcr-run-5pqr6lwp-90396-master-0_20240402T105012-ep14
| null |
[
"transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null |
2024-04-13T13:25:39+00:00
|
[
"1910.09700"
] |
[] |
TAGS
#transformers #safetensors #xlm-roberta #feature-extraction #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 #xlm-roberta #feature-extraction #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"
] |
null | null |
# #Roleplay #Multimodal #Vision
This repository hosts GGUF-IQ-Imatrix quants for [Nitral-AI/Nyanade_Stunna-Maid-7B](https://huggingface.co/Nitral-AI/Nyanade_Stunna-Maid-7B).
**Recommended starting [SillyTavern presets here](https://huggingface.co/Lewdiculous/Eris_PrimeV4-Vision-32k-7B-GGUF-IQ-Imatrix/tree/main/sillytavern-presets-lewdicu-3.0.2-mistral-0.2).**
This is a **#multimodal** model that also has **#vision** capabilities. <br> Read the full card information if you also want to use that functionality.
"Expected to be used with up to `--contextsize 8192`."
**What does "Imatrix" mean?**
<details><summary>
⇲ Click here to expand/hide more information about this topic.
</summary>
It stands for **Importance Matrix**, a technique used to improve the quality of quantized models.
The **Imatrix** is calculated based on calibration data, and it helps determine the importance of different model activations during the quantization process.
The idea is to preserve the most important information during quantization, which can help reduce the loss of model performance, especially when the calibration data is diverse.
[[1]](https://github.com/ggerganov/llama.cpp/discussions/5006) [[2]](https://github.com/ggerganov/llama.cpp/discussions/5263#discussioncomment-8395384)
For imatrix data generation, kalomaze's `groups_merged.txt` with additional roleplay chats was used, you can find it [here](https://huggingface.co/Lewdiculous/Nyanade_Stunna-Maid-7B-GGUF-IQ-Imatrix/blob/main/imatrix-with-rp-ex.txt). This was just to add a bit more diversity to the data with the intended use case in mind.
</details><br>
# Vision/multimodal capabilities:
<details><summary>
⇲ Click here to expand/hide how this would work in practice in a roleplay chat.
</summary>
</details><br>
<details><summary>
⇲ Click here to expand/hide what your SillyTavern Image Captions extension settings should look like.
</summary>
</details><br>
**If you want to use vision functionality:**
* Make sure you are using the latest version of [KoboldCpp](https://github.com/LostRuins/koboldcpp).
To use the multimodal capabilities of this model, such as **vision**, you also need to load the specified **mmproj** file, you can get it [here](https://huggingface.co/cjpais/llava-1.6-mistral-7b-gguf/blob/main/mmproj-model-f16.gguf) or as uploaded in the **mmproj** folder in the repository.
* You can load the **mmproj file** by using the corresponding section in the interface:
* For CLI users, you can load the **mmproj file** by adding the respective flag to your usual command:
```
--mmproj your-mmproj-file.gguf
```
# Quantization information:
<details><summary>
⇲ Click here to expand/hide more information about this topic.
</summary>
```python
quantization_options = [
"Q4_K_M", "Q4_K_S", "IQ4_XS", "Q5_K_M", "Q5_K_S",
"Q6_K", "Q8_0", "IQ3_M", "IQ3_S", "IQ3_XXS"
]
```
**Steps performed:**
```
Base⇢ GGUF(F16)⇢ Imatrix-Data(F16)⇢ GGUF(Imatrix-Quants)
```
*Using the latest llama.cpp at the time.*
</details><br>
|
{"tags": ["gguf", "quantized", "roleplay", "multimodal", "vision", "llava", "sillytavern", "merge", "mistral", "conversational"], "inference": false}
|
Lewdiculous/Nyanade_Stunna-Maid-7B-GGUF-IQ-Imatrix
| null |
[
"gguf",
"quantized",
"roleplay",
"multimodal",
"vision",
"llava",
"sillytavern",
"merge",
"mistral",
"conversational",
"region:us"
] | null |
2024-04-13T13:28:26+00:00
|
[] |
[] |
TAGS
#gguf #quantized #roleplay #multimodal #vision #llava #sillytavern #merge #mistral #conversational #region-us
|
# #Roleplay #Multimodal #Vision
This repository hosts GGUF-IQ-Imatrix quants for Nitral-AI/Nyanade_Stunna-Maid-7B.
Recommended starting SillyTavern presets here.
This is a #multimodal model that also has #vision capabilities. <br> Read the full card information if you also want to use that functionality.
"Expected to be used with up to '--contextsize 8192'."
!image/jpeg
What does "Imatrix" mean?
<details><summary>
⇲ Click here to expand/hide more information about this topic.
</summary>
It stands for Importance Matrix, a technique used to improve the quality of quantized models.
The Imatrix is calculated based on calibration data, and it helps determine the importance of different model activations during the quantization process.
The idea is to preserve the most important information during quantization, which can help reduce the loss of model performance, especially when the calibration data is diverse.
[[1]](URL [[2]](URL
For imatrix data generation, kalomaze's 'groups_merged.txt' with additional roleplay chats was used, you can find it here. This was just to add a bit more diversity to the data with the intended use case in mind.
</details><br>
# Vision/multimodal capabilities:
<details><summary>
⇲ Click here to expand/hide how this would work in practice in a roleplay chat.
</summary>
!image/jpeg
</details><br>
<details><summary>
⇲ Click here to expand/hide what your SillyTavern Image Captions extension settings should look like.
</summary>
!image/jpeg
</details><br>
If you want to use vision functionality:
* Make sure you are using the latest version of KoboldCpp.
To use the multimodal capabilities of this model, such as vision, you also need to load the specified mmproj file, you can get it here or as uploaded in the mmproj folder in the repository.
* You can load the mmproj file by using the corresponding section in the interface:
!image/png
* For CLI users, you can load the mmproj file by adding the respective flag to your usual command:
# Quantization information:
<details><summary>
⇲ Click here to expand/hide more information about this topic.
</summary>
Steps performed:
*Using the latest URL at the time.*
</details><br>
|
[
"# #Roleplay #Multimodal #Vision\n\nThis repository hosts GGUF-IQ-Imatrix quants for Nitral-AI/Nyanade_Stunna-Maid-7B.\n\nRecommended starting SillyTavern presets here.\n\nThis is a #multimodal model that also has #vision capabilities. <br> Read the full card information if you also want to use that functionality.\n\n\"Expected to be used with up to '--contextsize 8192'.\"\n\n!image/jpeg\n\nWhat does \"Imatrix\" mean?\n\n<details><summary>\n⇲ Click here to expand/hide more information about this topic.\n</summary>\n \nIt stands for Importance Matrix, a technique used to improve the quality of quantized models.\nThe Imatrix is calculated based on calibration data, and it helps determine the importance of different model activations during the quantization process.\nThe idea is to preserve the most important information during quantization, which can help reduce the loss of model performance, especially when the calibration data is diverse.\n[[1]](URL [[2]](URL\n\nFor imatrix data generation, kalomaze's 'groups_merged.txt' with additional roleplay chats was used, you can find it here. This was just to add a bit more diversity to the data with the intended use case in mind.\n \n</details><br>",
"# Vision/multimodal capabilities:\n\n<details><summary>\n⇲ Click here to expand/hide how this would work in practice in a roleplay chat.\n</summary>\n\n!image/jpeg\n\n</details><br>\n\n<details><summary>\n⇲ Click here to expand/hide what your SillyTavern Image Captions extension settings should look like.\n</summary>\n \n!image/jpeg\n \n</details><br>\n\nIf you want to use vision functionality:\n\n* Make sure you are using the latest version of KoboldCpp.\n\nTo use the multimodal capabilities of this model, such as vision, you also need to load the specified mmproj file, you can get it here or as uploaded in the mmproj folder in the repository.\n\n* You can load the mmproj file by using the corresponding section in the interface:\n\n!image/png\n\n* For CLI users, you can load the mmproj file by adding the respective flag to your usual command:",
"# Quantization information:\n\n\n<details><summary>\n⇲ Click here to expand/hide more information about this topic.\n</summary>\n\n\n\nSteps performed:\n\n\n*Using the latest URL at the time.*\n \n</details><br>"
] |
[
"TAGS\n#gguf #quantized #roleplay #multimodal #vision #llava #sillytavern #merge #mistral #conversational #region-us \n",
"# #Roleplay #Multimodal #Vision\n\nThis repository hosts GGUF-IQ-Imatrix quants for Nitral-AI/Nyanade_Stunna-Maid-7B.\n\nRecommended starting SillyTavern presets here.\n\nThis is a #multimodal model that also has #vision capabilities. <br> Read the full card information if you also want to use that functionality.\n\n\"Expected to be used with up to '--contextsize 8192'.\"\n\n!image/jpeg\n\nWhat does \"Imatrix\" mean?\n\n<details><summary>\n⇲ Click here to expand/hide more information about this topic.\n</summary>\n \nIt stands for Importance Matrix, a technique used to improve the quality of quantized models.\nThe Imatrix is calculated based on calibration data, and it helps determine the importance of different model activations during the quantization process.\nThe idea is to preserve the most important information during quantization, which can help reduce the loss of model performance, especially when the calibration data is diverse.\n[[1]](URL [[2]](URL\n\nFor imatrix data generation, kalomaze's 'groups_merged.txt' with additional roleplay chats was used, you can find it here. This was just to add a bit more diversity to the data with the intended use case in mind.\n \n</details><br>",
"# Vision/multimodal capabilities:\n\n<details><summary>\n⇲ Click here to expand/hide how this would work in practice in a roleplay chat.\n</summary>\n\n!image/jpeg\n\n</details><br>\n\n<details><summary>\n⇲ Click here to expand/hide what your SillyTavern Image Captions extension settings should look like.\n</summary>\n \n!image/jpeg\n \n</details><br>\n\nIf you want to use vision functionality:\n\n* Make sure you are using the latest version of KoboldCpp.\n\nTo use the multimodal capabilities of this model, such as vision, you also need to load the specified mmproj file, you can get it here or as uploaded in the mmproj folder in the repository.\n\n* You can load the mmproj file by using the corresponding section in the interface:\n\n!image/png\n\n* For CLI users, you can load the mmproj file by adding the respective flag to your usual command:",
"# Quantization information:\n\n\n<details><summary>\n⇲ Click here to expand/hide more information about this topic.\n</summary>\n\n\n\nSteps performed:\n\n\n*Using the latest URL at the time.*\n \n</details><br>"
] |
null | null |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
{}
|
RegularNico/hotz
| null |
[
"arxiv:1910.09700",
"region:us"
] | null |
2024-04-13T13:28:57+00:00
|
[
"1910.09700"
] |
[] |
TAGS
#arxiv-1910.09700 #region-us
|
# Model Card for Model ID
This modelcard aims to be a base template for new models. It has been generated using this raw template.
## 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]
## 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\n\n\n\nThis modelcard aims to be a base template for new models. It has been generated using this raw template.",
"## Model Details",
"### Model Description\n\n\n\n\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#arxiv-1910.09700 #region-us \n",
"# Model Card for Model ID\n\n\n\nThis modelcard aims to be a base template for new models. It has been generated using this raw template.",
"## Model Details",
"### Model Description\n\n\n\n\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"
] |
reinforcement-learning
|
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": "257.52 +/- 14.47", "name": "mean_reward", "verified": false}]}]}]}
|
LakshitKava/PPO-LunarLander-v2
| null |
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null |
2024-04-13T13:29:09+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"
] |
reinforcement-learning
|
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": "294.70 +/- 17.14", "name": "mean_reward", "verified": false}]}]}]}
|
aldjia/ppo-LunarLander-v2
| null |
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null |
2024-04-13T13:32:10+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"
] |
text-classification
|
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. -->
# results7
This model is a fine-tuned version of [jkhan447/sarcasm-detection-RoBerta-base-CR](https://huggingface.co/jkhan447/sarcasm-detection-RoBerta-base-CR) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5911
- Accuracy: 0.7089
- F1: 0.4195
## 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: 500
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "jkhan447/sarcasm-detection-RoBerta-base-CR", "model-index": [{"name": "results7", "results": []}]}
|
dianamihalache27/results7
| null |
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:jkhan447/sarcasm-detection-RoBerta-base-CR",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-13T13:32:31+00:00
|
[] |
[] |
TAGS
#transformers #safetensors #roberta #text-classification #generated_from_trainer #base_model-jkhan447/sarcasm-detection-RoBerta-base-CR #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# results7
This model is a fine-tuned version of jkhan447/sarcasm-detection-RoBerta-base-CR on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5911
- Accuracy: 0.7089
- F1: 0.4195
## 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: 500
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
[
"# results7\n\nThis model is a fine-tuned version of jkhan447/sarcasm-detection-RoBerta-base-CR on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.5911\n- Accuracy: 0.7089\n- F1: 0.4195",
"## 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: 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: 500\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.2\n- Pytorch 2.2.1+cu121\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] |
[
"TAGS\n#transformers #safetensors #roberta #text-classification #generated_from_trainer #base_model-jkhan447/sarcasm-detection-RoBerta-base-CR #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# results7\n\nThis model is a fine-tuned version of jkhan447/sarcasm-detection-RoBerta-base-CR on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.5911\n- Accuracy: 0.7089\n- F1: 0.4195",
"## 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: 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: 500\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.2\n- Pytorch 2.2.1+cu121\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] |
reinforcement-learning
|
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": "291.52 +/- 19.33", "name": "mean_reward", "verified": false}]}]}]}
|
Yann2310/amazigh_warior
| null |
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null |
2024-04-13T13:33:21+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"
] |
reinforcement-learning
|
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": "256.03 +/- 15.50", "name": "mean_reward", "verified": false}]}]}]}
|
hOelfY/ppo-LunarLander-v2
| null |
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null |
2024-04-13T13:36:20+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"
] |
text-generation
|
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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### Model Sources [optional]
<!-- 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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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### 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]
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## Model Card Contact
[More Information Needed]
|
{"library_name": "transformers", "tags": []}
|
shallow6414/aucx3qi
| null |
[
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-13T13:38:44+00:00
|
[
"1910.09700"
] |
[] |
TAGS
#transformers #safetensors #stablelm #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #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 #stablelm #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #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"
] |
text-generation
|
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
{"library_name": "transformers", "tags": []}
|
LexiconShiftInnovations/Gemma_Dental_it_07_merged
| null |
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-13T13:39:55+00:00
|
[
"1910.09700"
] |
[] |
TAGS
#transformers #safetensors #gemma #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:
- 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 #gemma #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"
] |
text-generation
|
transformers
|
# nishide-dev/suzume-poc-mlx-8bit
This model was converted to MLX format from [`alfredplpl/suzume-poc`]() using mlx-lm version **0.7.0**.
Refer to the [original model card](https://huggingface.co/alfredplpl/suzume-poc) for more details on the model.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("nishide-dev/suzume-poc-mlx-8bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
|
{"language": ["ja", "en"], "license": "other", "library_name": "transformers", "tags": ["mlx"], "license_name": "gemma-terms-of-use", "license_link": "https://www.kaggle.com/models/google/gemma/license/consent", "inference": false}
|
nishide-dev/suzume-poc-mlx-8bit
| null |
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"mlx",
"ja",
"en",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-13T13:40:20+00:00
|
[] |
[
"ja",
"en"
] |
TAGS
#transformers #safetensors #gemma #text-generation #mlx #ja #en #license-other #autotrain_compatible #text-generation-inference #region-us
|
# nishide-dev/suzume-poc-mlx-8bit
This model was converted to MLX format from ['alfredplpl/suzume-poc']() using mlx-lm version 0.7.0.
Refer to the original model card for more details on the model.
## Use with mlx
|
[
"# nishide-dev/suzume-poc-mlx-8bit\nThis model was converted to MLX format from ['alfredplpl/suzume-poc']() using mlx-lm version 0.7.0.\nRefer to the original model card for more details on the model.",
"## Use with mlx"
] |
[
"TAGS\n#transformers #safetensors #gemma #text-generation #mlx #ja #en #license-other #autotrain_compatible #text-generation-inference #region-us \n",
"# nishide-dev/suzume-poc-mlx-8bit\nThis model was converted to MLX format from ['alfredplpl/suzume-poc']() using mlx-lm version 0.7.0.\nRefer to the original model card for more details on the model.",
"## Use with mlx"
] |
text-generation
|
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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## Model Card Contact
[More Information Needed]
|
{"library_name": "transformers", "tags": []}
|
andreidima/Llama-2-7b-Romanian-qlora
| null |
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null |
2024-04-13T13:40:29+00:00
|
[
"1910.09700"
] |
[] |
TAGS
#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #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]:
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- Hardware Type:
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### Compute Infrastructure
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#### Software
[optional]
BibTeX:
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## 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]:",
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"### Direct Use",
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"### 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]",
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"### Testing Data, Factors & Metrics",
"#### Testing Data",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
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"## Model Card Contact"
] |
[
"TAGS\n#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #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]:",
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"### Direct Use",
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"## 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:",
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"### Testing Data, Factors & Metrics",
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"## Technical Specifications [optional]",
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"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
text2text-generation
|
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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## How to Get Started with the Model
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[More Information Needed]
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
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#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
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#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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#### Metrics
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[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]
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## Technical Specifications [optional]
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[More Information Needed]
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## 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. -->
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[More Information Needed]
|
{"library_name": "transformers", "tags": []}
|
falba/t5-small-finetuned-news-ep5
| null |
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-13T13:41:28+00:00
|
[
"1910.09700"
] |
[] |
TAGS
#transformers #safetensors #t5 #text2text-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:
- 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]:",
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"### 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]",
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"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
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"## 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 #t5 #text2text-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]:",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
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"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
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. -->
# gpt7B_domar_finetune
This model is a fine-tuned version of [AI-Sweden-Models/gpt-sw3-6.7b](https://huggingface.co/AI-Sweden-Models/gpt-sw3-6.7b) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7250
## 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
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.7786 | 0.79 | 200 | 0.7329 |
| 0.6735 | 1.59 | 400 | 0.7287 |
| 0.77 | 2.38 | 600 | 0.7264 |
| 0.7436 | 3.18 | 800 | 0.7253 |
| 0.6804 | 3.97 | 1000 | 0.7252 |
| 0.6184 | 4.77 | 1200 | 0.7250 |
### Framework versions
- PEFT 0.8.2
- Transformers 4.38.1
- Pytorch 2.2.0+cu118
- Datasets 2.17.1
- Tokenizers 0.15.2
|
{"license": "other", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "AI-Sweden-Models/gpt-sw3-6.7b", "model-index": [{"name": "gpt7B_domar_finetune", "results": []}]}
|
thorirhrafn/gpt7B_domar_finetune
| null |
[
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:AI-Sweden-Models/gpt-sw3-6.7b",
"license:other",
"region:us"
] | null |
2024-04-13T13:41:45+00:00
|
[] |
[] |
TAGS
#peft #tensorboard #safetensors #generated_from_trainer #base_model-AI-Sweden-Models/gpt-sw3-6.7b #license-other #region-us
|
gpt7B\_domar\_finetune
======================
This model is a fine-tuned version of AI-Sweden-Models/gpt-sw3-6.7b on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7250
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
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5
### Training results
### Framework versions
* PEFT 0.8.2
* Transformers 4.38.1
* Pytorch 2.2.0+cu118
* Datasets 2.17.1
* Tokenizers 0.15.2
|
[
"### 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* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.1\n* Pytorch 2.2.0+cu118\n* Datasets 2.17.1\n* Tokenizers 0.15.2"
] |
[
"TAGS\n#peft #tensorboard #safetensors #generated_from_trainer #base_model-AI-Sweden-Models/gpt-sw3-6.7b #license-other #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* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.1\n* Pytorch 2.2.0+cu118\n* Datasets 2.17.1\n* Tokenizers 0.15.2"
] |
null |
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
{"library_name": "transformers", "tags": []}
|
cackerman/rewrites_mistral7unsloth_4bit_ft_full_secondft2
| null |
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null |
2024-04-13T13:43:01+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",
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"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
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"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
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"## 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"
] |
text-classification
|
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. -->
# results8
This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5802
- Accuracy: 0.7320
- F1: 0.4188
## 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: 500
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "google-bert/bert-base-uncased", "model-index": [{"name": "results8", "results": []}]}
|
dianamihalache27/results8
| null |
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-13T13:43:14+00:00
|
[] |
[] |
TAGS
#transformers #safetensors #bert #text-classification #generated_from_trainer #base_model-google-bert/bert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# results8
This model is a fine-tuned version of google-bert/bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5802
- Accuracy: 0.7320
- F1: 0.4188
## 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: 500
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
[
"# results8\n\nThis model is a fine-tuned version of google-bert/bert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.5802\n- Accuracy: 0.7320\n- F1: 0.4188",
"## 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: 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: 500\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.2\n- Pytorch 2.2.1+cu121\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] |
[
"TAGS\n#transformers #safetensors #bert #text-classification #generated_from_trainer #base_model-google-bert/bert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# results8\n\nThis model is a fine-tuned version of google-bert/bert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.5802\n- Accuracy: 0.7320\n- F1: 0.4188",
"## 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: 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: 500\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.2\n- Pytorch 2.2.1+cu121\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] |
null |
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
{"library_name": "transformers", "tags": []}
|
ahmedabdo/blip-lora-v2
| null |
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null |
2024-04-13T13:49:43+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"
] |
reinforcement-learning
|
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": "240.50 +/- 14.74", "name": "mean_reward", "verified": false}]}]}]}
|
Astowny/ppo-LunarLander-v2
| null |
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null |
2024-04-13T13:51:27+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"
] |
text-classification
|
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. -->
# results9
This model is a fine-tuned version of [mrm8488/distilbert-finetuned-sarcasm-classification](https://huggingface.co/mrm8488/distilbert-finetuned-sarcasm-classification) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7560
- Accuracy: 0.6888
- F1: 0.2653
## 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: 500
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "mrm8488/distilbert-finetuned-sarcasm-classification", "model-index": [{"name": "results9", "results": []}]}
|
dianamihalache27/results9
| null |
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:mrm8488/distilbert-finetuned-sarcasm-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-13T13:51:44+00:00
|
[] |
[] |
TAGS
#transformers #safetensors #distilbert #text-classification #generated_from_trainer #base_model-mrm8488/distilbert-finetuned-sarcasm-classification #autotrain_compatible #endpoints_compatible #region-us
|
# results9
This model is a fine-tuned version of mrm8488/distilbert-finetuned-sarcasm-classification on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7560
- Accuracy: 0.6888
- F1: 0.2653
## 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: 500
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
[
"# results9\n\nThis model is a fine-tuned version of mrm8488/distilbert-finetuned-sarcasm-classification on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.7560\n- Accuracy: 0.6888\n- F1: 0.2653",
"## 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: 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: 500\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.2\n- Pytorch 2.2.1+cu121\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] |
[
"TAGS\n#transformers #safetensors #distilbert #text-classification #generated_from_trainer #base_model-mrm8488/distilbert-finetuned-sarcasm-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# results9\n\nThis model is a fine-tuned version of mrm8488/distilbert-finetuned-sarcasm-classification on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.7560\n- Accuracy: 0.6888\n- F1: 0.2653",
"## 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: 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: 500\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.2\n- Pytorch 2.2.1+cu121\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] |
text-generation
| null |
# Model Card for Psyfighter2-13B-vore-GGUF
This is a quantized version of [SnakyMcSnekFace/Psyfighter2-13B-vore](https://huggingface.co/SnakyMcSnekFace/Psyfighter2-13B-vore) model.
This model is a version of [KoboldAI/LLaMA2-13B-Psyfighter2](https://huggingface.co/KoboldAI/LLaMA2-13B-Psyfighter2) finetuned to better understand vore context. The primary purpose of this model is to be a storywriting assistant, as well as a conversational model in a chat.
The Adventure Mode is still work in progress, and will be added later.
## Model Details
### Model Description
The model behaves similarly to `KoboldAI/LLaMA2-13B-Psyfighter2`, which it was derived from. Please [see the README.md here](https://huggingface.co/KoboldAI/LLaMA2-13B-Psyfighter2/blob/main/README.md) to learn more.
This model was fine-tuned on ~55 MiB of free-form text, containing stories focused around the vore theme. As a result, it has a strong vorny bias.
## How to Get Started with the Model
The model can be used with any AI chatbots and front-ends designed to work with `.gguf` models. The model fits fully into 8GB VRAM, but can also run with degraded performance on smaller graphics cards.
Similarly to the base model, the less prompt the model receives, the more creative is the output. For example, the writing assistant will generate an entire story when prompted with only 2-3 words.
In the chat mode, if the conversation is not going where you would like it to go, edit the model's output and let it continue generation. The model will also match the style of the conversation.
### Koboldcpp Colab Notebook
The easiest way to try out the model is [Koboldcpp Colab Notebook](https://colab.research.google.com/github/lostruins/koboldcpp/blob/concedo/colab.ipynb). This method doesn't require you to have a powerful graphics card.
- Open the notebook
- Paste the model URL into the field: `https://huggingface.co/SnakyMcSnekFace/Psyfighter2-13B-vore-GGUF/resolve/main/Psyfighter2-13B-vore.Q4_K_M.gguf`
- Start the notebook, wait for the URL to CloudFlare tunnel to appear at the bottom and click it
- Use the model as a writing assistant
- You can try an adventure from [https://aetherroom.club/](https://aetherroom.club/), but keep in mind that the model will not let you take turn unless you stop it. Adventure mode is work-in-progress.
### Faraday
Another convenient way to use the model is [Faraday.dev](https://faraday.dev/) application, which allows you to run the model locally on your computer. You'll need a graphics card with at least 8GB VRAM to use `Q4_K_M` version comfortably, and 16GB VRAM for `Q8_0`. (`Q4_K_M` version is smaller and faster, `Q8_0` is slower but more coherent.)
Download the [Psyfighter2-13B-vore.Q4_K_M.gguf](https://huggingface.co/SnakyMcSnekFace/Psyfighter2-13B-vore-GGUF/resolve/main/Psyfighter2-13B-vore.Q4_K_M.gguf) or [Psyfighter2-13B-vore.Q8_0.gguf](https://huggingface.co/SnakyMcSnekFace/Psyfighter2-13B-vore-GGUF/resolve/main/Psyfighter2-13B-vore.Q8_0.gguf) file into `%appdata%\faraday\models` folder on your computer. The model should appear in `Manage Models` menu under `Downloaded Models`. You can then select it in your character card or set it as a default model.
### Others
TBD
## Bias, Risks, and Limitations
By design, this model has a strong vorny bias. It's not intended for use by anyone below 18 years old.
## Training Details
This model was fine-tuned on free-form text comprised of stories focused around the vore theme using the [QLoRA method](https://arxiv.org/abs/2305.14314). The resulting adapter was merged into the base model. The quantized version of the model was prepared using [llama.cpp](https://github.com/ggerganov/llama.cpp).
### Training Procedure
The model was fine-tuned using the [QLoRA method](https://arxiv.org/abs/2305.14314) on NVIDIA GeForce RTX 4060 Ti over the span of ~7 days. Training was performed using [text-generation-webui by oobabooga](https://github.com/oobabooga/text-generation-webui) with [Training PRO plug-in by FartyPants](https://github.com/FartyPants/Training_PRO).
LoRa adapter configuration:
- Rank: 512
- Alpha: 1024
- Dropout rate: 0.05
- Target weights: v_prog, q_proj
Training parameters:
- Sample size: 768 tokens
- Samples per epoch: 47420
- Number of epochs: 2
- First epoch: Learning rate = 3e-4, 1000 steps warmup, cosine schedule
- Second epoch: Learning rate = 1e-4, 256 steps warmup, inverse sqrt schedule
#### Preprocessing
The stories in dataset were pre-processed as follows:
- titles, foreword, tags, and anything not comprising the text of the story was removed
- non-ascii characters and character sequences serving as chapter separators were removed
- any story mentioning underage personas was taken out of the dataset
- names of private characters were replaced with randomized names across the dataset
## Environmental Impact
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 GeForce RTX 4060 Ti
- **Hours used:** 168
- **Cloud Provider:** N/A
- **Compute Region:** US-East
- **Carbon Emitted:** 5.8 kg CO2 eq.
|
{"language": ["en"], "license": "llama2", "tags": ["storywriting", "finetuned", "roleplay", "vore", "not-for-all-audiences", "gguf", "nsfw", "uncensored"], "pipeline_tag": "text-generation", "inference": false, "base_model": "SnakyMcSnekFace/Psyfighter2-13B-vore", "model_type": "llama", "prompt_template": "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{prompt}\n\n### Response:"}
|
SnakyMcSnekFace/Psyfighter2-13B-vore-GGUF
| null |
[
"gguf",
"storywriting",
"finetuned",
"roleplay",
"vore",
"not-for-all-audiences",
"nsfw",
"uncensored",
"text-generation",
"en",
"arxiv:2305.14314",
"arxiv:1910.09700",
"base_model:SnakyMcSnekFace/Psyfighter2-13B-vore",
"license:llama2",
"region:us"
] | null |
2024-04-13T13:55:46+00:00
|
[
"2305.14314",
"1910.09700"
] |
[
"en"
] |
TAGS
#gguf #storywriting #finetuned #roleplay #vore #not-for-all-audiences #nsfw #uncensored #text-generation #en #arxiv-2305.14314 #arxiv-1910.09700 #base_model-SnakyMcSnekFace/Psyfighter2-13B-vore #license-llama2 #region-us
|
# Model Card for Psyfighter2-13B-vore-GGUF
This is a quantized version of SnakyMcSnekFace/Psyfighter2-13B-vore model.
This model is a version of KoboldAI/LLaMA2-13B-Psyfighter2 finetuned to better understand vore context. The primary purpose of this model is to be a storywriting assistant, as well as a conversational model in a chat.
The Adventure Mode is still work in progress, and will be added later.
## Model Details
### Model Description
The model behaves similarly to 'KoboldAI/LLaMA2-13B-Psyfighter2', which it was derived from. Please see the URL here to learn more.
This model was fine-tuned on ~55 MiB of free-form text, containing stories focused around the vore theme. As a result, it has a strong vorny bias.
## How to Get Started with the Model
The model can be used with any AI chatbots and front-ends designed to work with '.gguf' models. The model fits fully into 8GB VRAM, but can also run with degraded performance on smaller graphics cards.
Similarly to the base model, the less prompt the model receives, the more creative is the output. For example, the writing assistant will generate an entire story when prompted with only 2-3 words.
In the chat mode, if the conversation is not going where you would like it to go, edit the model's output and let it continue generation. The model will also match the style of the conversation.
### Koboldcpp Colab Notebook
The easiest way to try out the model is Koboldcpp Colab Notebook. This method doesn't require you to have a powerful graphics card.
- Open the notebook
- Paste the model URL into the field: 'URL
- Start the notebook, wait for the URL to CloudFlare tunnel to appear at the bottom and click it
- Use the model as a writing assistant
- You can try an adventure from URL but keep in mind that the model will not let you take turn unless you stop it. Adventure mode is work-in-progress.
### Faraday
Another convenient way to use the model is URL application, which allows you to run the model locally on your computer. You'll need a graphics card with at least 8GB VRAM to use 'Q4_K_M' version comfortably, and 16GB VRAM for 'Q8_0'. ('Q4_K_M' version is smaller and faster, 'Q8_0' is slower but more coherent.)
Download the Psyfighter2-13B-vore.Q4_K_M.gguf or Psyfighter2-13B-vore.Q8_0.gguf file into '%appdata%\faraday\models' folder on your computer. The model should appear in 'Manage Models' menu under 'Downloaded Models'. You can then select it in your character card or set it as a default model.
### Others
TBD
## Bias, Risks, and Limitations
By design, this model has a strong vorny bias. It's not intended for use by anyone below 18 years old.
## Training Details
This model was fine-tuned on free-form text comprised of stories focused around the vore theme using the QLoRA method. The resulting adapter was merged into the base model. The quantized version of the model was prepared using URL.
### Training Procedure
The model was fine-tuned using the QLoRA method on NVIDIA GeForce RTX 4060 Ti over the span of ~7 days. Training was performed using text-generation-webui by oobabooga with Training PRO plug-in by FartyPants.
LoRa adapter configuration:
- Rank: 512
- Alpha: 1024
- Dropout rate: 0.05
- Target weights: v_prog, q_proj
Training parameters:
- Sample size: 768 tokens
- Samples per epoch: 47420
- Number of epochs: 2
- First epoch: Learning rate = 3e-4, 1000 steps warmup, cosine schedule
- Second epoch: Learning rate = 1e-4, 256 steps warmup, inverse sqrt schedule
#### Preprocessing
The stories in dataset were pre-processed as follows:
- titles, foreword, tags, and anything not comprising the text of the story was removed
- non-ascii characters and character sequences serving as chapter separators were removed
- any story mentioning underage personas was taken out of the dataset
- names of private characters were replaced with randomized names across the dataset
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: NVIDIA GeForce RTX 4060 Ti
- Hours used: 168
- Cloud Provider: N/A
- Compute Region: US-East
- Carbon Emitted: 5.8 kg CO2 eq.
|
[
"# Model Card for Psyfighter2-13B-vore-GGUF\n\nThis is a quantized version of SnakyMcSnekFace/Psyfighter2-13B-vore model.\n\nThis model is a version of KoboldAI/LLaMA2-13B-Psyfighter2 finetuned to better understand vore context. The primary purpose of this model is to be a storywriting assistant, as well as a conversational model in a chat.\n\nThe Adventure Mode is still work in progress, and will be added later.",
"## Model Details",
"### Model Description\n\nThe model behaves similarly to 'KoboldAI/LLaMA2-13B-Psyfighter2', which it was derived from. Please see the URL here to learn more.\n\nThis model was fine-tuned on ~55 MiB of free-form text, containing stories focused around the vore theme. As a result, it has a strong vorny bias.",
"## How to Get Started with the Model\n\nThe model can be used with any AI chatbots and front-ends designed to work with '.gguf' models. The model fits fully into 8GB VRAM, but can also run with degraded performance on smaller graphics cards.\n\nSimilarly to the base model, the less prompt the model receives, the more creative is the output. For example, the writing assistant will generate an entire story when prompted with only 2-3 words.\n\nIn the chat mode, if the conversation is not going where you would like it to go, edit the model's output and let it continue generation. The model will also match the style of the conversation.",
"### Koboldcpp Colab Notebook\n\nThe easiest way to try out the model is Koboldcpp Colab Notebook. This method doesn't require you to have a powerful graphics card.\n\n- Open the notebook\n- Paste the model URL into the field: 'URL\n- Start the notebook, wait for the URL to CloudFlare tunnel to appear at the bottom and click it\n- Use the model as a writing assistant\n- You can try an adventure from URL but keep in mind that the model will not let you take turn unless you stop it. Adventure mode is work-in-progress.",
"### Faraday \n\nAnother convenient way to use the model is URL application, which allows you to run the model locally on your computer. You'll need a graphics card with at least 8GB VRAM to use 'Q4_K_M' version comfortably, and 16GB VRAM for 'Q8_0'. ('Q4_K_M' version is smaller and faster, 'Q8_0' is slower but more coherent.)\n\nDownload the Psyfighter2-13B-vore.Q4_K_M.gguf or Psyfighter2-13B-vore.Q8_0.gguf file into '%appdata%\\faraday\\models' folder on your computer. The model should appear in 'Manage Models' menu under 'Downloaded Models'. You can then select it in your character card or set it as a default model.",
"### Others\n\nTBD",
"## Bias, Risks, and Limitations\n\nBy design, this model has a strong vorny bias. It's not intended for use by anyone below 18 years old.",
"## Training Details\n\nThis model was fine-tuned on free-form text comprised of stories focused around the vore theme using the QLoRA method. The resulting adapter was merged into the base model. The quantized version of the model was prepared using URL.",
"### Training Procedure\n\nThe model was fine-tuned using the QLoRA method on NVIDIA GeForce RTX 4060 Ti over the span of ~7 days. Training was performed using text-generation-webui by oobabooga with Training PRO plug-in by FartyPants.\n\n\nLoRa adapter configuration:\n\n- Rank: 512\n- Alpha: 1024\n- Dropout rate: 0.05\n- Target weights: v_prog, q_proj\n\nTraining parameters:\n\n- Sample size: 768 tokens\n- Samples per epoch: 47420\n- Number of epochs: 2\n- First epoch: Learning rate = 3e-4, 1000 steps warmup, cosine schedule\n- Second epoch: Learning rate = 1e-4, 256 steps warmup, inverse sqrt schedule",
"#### Preprocessing\n\nThe stories in dataset were pre-processed as follows:\n\n- titles, foreword, tags, and anything not comprising the text of the story was removed\n- non-ascii characters and character sequences serving as chapter separators were removed\n- any story mentioning underage personas was taken out of the dataset\n- names of private characters were replaced with randomized names across the dataset",
"## Environmental Impact\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: NVIDIA GeForce RTX 4060 Ti\n- Hours used: 168\n- Cloud Provider: N/A\n- Compute Region: US-East\n- Carbon Emitted: 5.8 kg CO2 eq."
] |
[
"TAGS\n#gguf #storywriting #finetuned #roleplay #vore #not-for-all-audiences #nsfw #uncensored #text-generation #en #arxiv-2305.14314 #arxiv-1910.09700 #base_model-SnakyMcSnekFace/Psyfighter2-13B-vore #license-llama2 #region-us \n",
"# Model Card for Psyfighter2-13B-vore-GGUF\n\nThis is a quantized version of SnakyMcSnekFace/Psyfighter2-13B-vore model.\n\nThis model is a version of KoboldAI/LLaMA2-13B-Psyfighter2 finetuned to better understand vore context. The primary purpose of this model is to be a storywriting assistant, as well as a conversational model in a chat.\n\nThe Adventure Mode is still work in progress, and will be added later.",
"## Model Details",
"### Model Description\n\nThe model behaves similarly to 'KoboldAI/LLaMA2-13B-Psyfighter2', which it was derived from. Please see the URL here to learn more.\n\nThis model was fine-tuned on ~55 MiB of free-form text, containing stories focused around the vore theme. As a result, it has a strong vorny bias.",
"## How to Get Started with the Model\n\nThe model can be used with any AI chatbots and front-ends designed to work with '.gguf' models. The model fits fully into 8GB VRAM, but can also run with degraded performance on smaller graphics cards.\n\nSimilarly to the base model, the less prompt the model receives, the more creative is the output. For example, the writing assistant will generate an entire story when prompted with only 2-3 words.\n\nIn the chat mode, if the conversation is not going where you would like it to go, edit the model's output and let it continue generation. The model will also match the style of the conversation.",
"### Koboldcpp Colab Notebook\n\nThe easiest way to try out the model is Koboldcpp Colab Notebook. This method doesn't require you to have a powerful graphics card.\n\n- Open the notebook\n- Paste the model URL into the field: 'URL\n- Start the notebook, wait for the URL to CloudFlare tunnel to appear at the bottom and click it\n- Use the model as a writing assistant\n- You can try an adventure from URL but keep in mind that the model will not let you take turn unless you stop it. Adventure mode is work-in-progress.",
"### Faraday \n\nAnother convenient way to use the model is URL application, which allows you to run the model locally on your computer. You'll need a graphics card with at least 8GB VRAM to use 'Q4_K_M' version comfortably, and 16GB VRAM for 'Q8_0'. ('Q4_K_M' version is smaller and faster, 'Q8_0' is slower but more coherent.)\n\nDownload the Psyfighter2-13B-vore.Q4_K_M.gguf or Psyfighter2-13B-vore.Q8_0.gguf file into '%appdata%\\faraday\\models' folder on your computer. The model should appear in 'Manage Models' menu under 'Downloaded Models'. You can then select it in your character card or set it as a default model.",
"### Others\n\nTBD",
"## Bias, Risks, and Limitations\n\nBy design, this model has a strong vorny bias. It's not intended for use by anyone below 18 years old.",
"## Training Details\n\nThis model was fine-tuned on free-form text comprised of stories focused around the vore theme using the QLoRA method. The resulting adapter was merged into the base model. The quantized version of the model was prepared using URL.",
"### Training Procedure\n\nThe model was fine-tuned using the QLoRA method on NVIDIA GeForce RTX 4060 Ti over the span of ~7 days. Training was performed using text-generation-webui by oobabooga with Training PRO plug-in by FartyPants.\n\n\nLoRa adapter configuration:\n\n- Rank: 512\n- Alpha: 1024\n- Dropout rate: 0.05\n- Target weights: v_prog, q_proj\n\nTraining parameters:\n\n- Sample size: 768 tokens\n- Samples per epoch: 47420\n- Number of epochs: 2\n- First epoch: Learning rate = 3e-4, 1000 steps warmup, cosine schedule\n- Second epoch: Learning rate = 1e-4, 256 steps warmup, inverse sqrt schedule",
"#### Preprocessing\n\nThe stories in dataset were pre-processed as follows:\n\n- titles, foreword, tags, and anything not comprising the text of the story was removed\n- non-ascii characters and character sequences serving as chapter separators were removed\n- any story mentioning underage personas was taken out of the dataset\n- names of private characters were replaced with randomized names across the dataset",
"## Environmental Impact\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: NVIDIA GeForce RTX 4060 Ti\n- Hours used: 168\n- Cloud Provider: N/A\n- Compute Region: US-East\n- Carbon Emitted: 5.8 kg CO2 eq."
] |
text-generation
|
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
{"library_name": "transformers", "tags": []}
|
yuhuixu/mistral-7b-sft-beta-ultrafeedback-v0.1
| null |
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-13T13:56:18+00:00
|
[
"1910.09700"
] |
[] |
TAGS
#transformers #safetensors #mistral #text-generation #conversational #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:
- 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 #mistral #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"
] |
text-generation
|
transformers
|
# merge
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 SLERP merge method.
### Models Merged
The following models were included in the merge:
* [NousResearch/Hermes-2-Pro-Mistral-7B](https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B)
* [WizardLM/WizardMath-7B-V1.1](https://huggingface.co/WizardLM/WizardMath-7B-V1.1)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: NousResearch/Hermes-2-Pro-Mistral-7B
- model: WizardLM/WizardMath-7B-V1.1
merge_method: slerp
base_model: NousResearch/Hermes-2-Pro-Mistral-7B
dtype: bfloat16
parameters:
t: [0, 0.5, 1, 0.5, 0] # V shaped curve: Hermes for input & output, WizardMath in the middle layers
```
|
{"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["NousResearch/Hermes-2-Pro-Mistral-7B", "WizardLM/WizardMath-7B-V1.1"]}
|
mergekit-community/mergekit-slerp-uzattal
| null |
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:NousResearch/Hermes-2-Pro-Mistral-7B",
"base_model:WizardLM/WizardMath-7B-V1.1",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-13T13:56:24+00:00
|
[] |
[] |
TAGS
#transformers #safetensors #mistral #text-generation #mergekit #merge #conversational #base_model-NousResearch/Hermes-2-Pro-Mistral-7B #base_model-WizardLM/WizardMath-7B-V1.1 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# merge
This is a merge of pre-trained language models created using mergekit.
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* NousResearch/Hermes-2-Pro-Mistral-7B
* WizardLM/WizardMath-7B-V1.1
### Configuration
The following YAML configuration was used to produce this model:
|
[
"# merge\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the SLERP merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* NousResearch/Hermes-2-Pro-Mistral-7B\n* WizardLM/WizardMath-7B-V1.1",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] |
[
"TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #conversational #base_model-NousResearch/Hermes-2-Pro-Mistral-7B #base_model-WizardLM/WizardMath-7B-V1.1 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# merge\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the SLERP merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* NousResearch/Hermes-2-Pro-Mistral-7B\n* WizardLM/WizardMath-7B-V1.1",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] |
reinforcement-learning
|
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": "239.77 +/- 61.79", "name": "mean_reward", "verified": false}]}]}]}
|
konawa/ppo-LunarLander-v2
| null |
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null |
2024-04-13T13:57:37+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"
] |
text-generation
| null |
# karakuri-midroze-mg-Q6_K.gguf
下記モデルをDARE_TIES方式にてmergeしたものをQ6_Kに量子化しています。
- [karakuri-ai/karakuri-lm-70b-v0.1](https://huggingface.co/karakuri-ai/karakuri-lm-70b-v0.1)
- [sophosympatheia/Midnight-Rose-70B-v2.0.3](https://huggingface.co/sophosympatheia/Midnight-Rose-70B-v2.0.3)
## モデル概要
これは日本語の特定の能力がmergeにより、どのように向上するかをテストするための実験モデルです。
## ライセンス
merge元の各モデルのライセンスに従います。
|
{"language": ["ja"], "tags": ["merge"], "pipeline_tag": "text-generation"}
|
sbtom/karakuri-midroze-mg.gguf
| null |
[
"merge",
"text-generation",
"ja",
"region:us"
] | null |
2024-04-13T13:57:58+00:00
|
[] |
[
"ja"
] |
TAGS
#merge #text-generation #ja #region-us
|
# karakuri-midroze-mg-Q6_K.gguf
下記モデルをDARE_TIES方式にてmergeしたものをQ6_Kに量子化しています。
- karakuri-ai/karakuri-lm-70b-v0.1
- sophosympatheia/Midnight-Rose-70B-v2.0.3
## モデル概要
これは日本語の特定の能力がmergeにより、どのように向上するかをテストするための実験モデルです。
## ライセンス
merge元の各モデルのライセンスに従います。
|
[
"# karakuri-midroze-mg-Q6_K.gguf\n\n下記モデルをDARE_TIES方式にてmergeしたものをQ6_Kに量子化しています。\n- karakuri-ai/karakuri-lm-70b-v0.1\n- sophosympatheia/Midnight-Rose-70B-v2.0.3",
"## モデル概要\n\nこれは日本語の特定の能力がmergeにより、どのように向上するかをテストするための実験モデルです。",
"## ライセンス\n merge元の各モデルのライセンスに従います。"
] |
[
"TAGS\n#merge #text-generation #ja #region-us \n",
"# karakuri-midroze-mg-Q6_K.gguf\n\n下記モデルをDARE_TIES方式にてmergeしたものをQ6_Kに量子化しています。\n- karakuri-ai/karakuri-lm-70b-v0.1\n- sophosympatheia/Midnight-Rose-70B-v2.0.3",
"## モデル概要\n\nこれは日本語の特定の能力がmergeにより、どのように向上するかをテストするための実験モデルです。",
"## ライセンス\n merge元の各モデルのライセンスに従います。"
] |
text-classification
|
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
{"library_name": "transformers", "tags": []}
|
hiramochoavea/homomex24-beto-85-15
| null |
[
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-13T13:58:05+00:00
|
[
"1910.09700"
] |
[] |
TAGS
#transformers #safetensors #bert #text-classification #arxiv-1910.09700 #autotrain_compatible #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 #bert #text-classification #arxiv-1910.09700 #autotrain_compatible #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"
] |
question-answering
|
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. -->
# Albert-finetuned-ChennaiQA-final
This model is a fine-tuned version of [albert/albert-base-v2](https://huggingface.co/albert/albert-base-v2) 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: 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: 15
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "albert/albert-base-v2", "model-index": [{"name": "Albert-finetuned-ChennaiQA-final", "results": []}]}
|
aditi2212/Albert-finetuned-ChennaiQA-final
| null |
[
"transformers",
"tensorboard",
"safetensors",
"albert",
"question-answering",
"generated_from_trainer",
"base_model:albert/albert-base-v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2024-04-13T13:59:03+00:00
|
[] |
[] |
TAGS
#transformers #tensorboard #safetensors #albert #question-answering #generated_from_trainer #base_model-albert/albert-base-v2 #license-apache-2.0 #endpoints_compatible #region-us
|
# Albert-finetuned-ChennaiQA-final
This model is a fine-tuned version of albert/albert-base-v2 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: 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: 15
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
[
"# Albert-finetuned-ChennaiQA-final\n\nThis model is a fine-tuned version of albert/albert-base-v2 on the None 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: 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: 15",
"### Framework versions\n\n- Transformers 4.38.2\n- Pytorch 2.2.1+cu121\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] |
[
"TAGS\n#transformers #tensorboard #safetensors #albert #question-answering #generated_from_trainer #base_model-albert/albert-base-v2 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Albert-finetuned-ChennaiQA-final\n\nThis model is a fine-tuned version of albert/albert-base-v2 on the None 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: 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: 15",
"### Framework versions\n\n- Transformers 4.38.2\n- Pytorch 2.2.1+cu121\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] |
reinforcement-learning
|
sample-factory
|
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r aa-unh/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
{"library_name": "sample-factory", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "sample-factory"], "model-index": [{"name": "APPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "doom_health_gathering_supreme", "type": "doom_health_gathering_supreme"}, "metrics": [{"type": "mean_reward", "value": "12.14 +/- 6.11", "name": "mean_reward", "verified": false}]}]}]}
|
aa-unh/rl_course_vizdoom_health_gathering_supreme
| null |
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null |
2024-04-13T13:59:09+00:00
|
[] |
[] |
TAGS
#sample-factory #tensorboard #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
A(n) APPO model trained on the doom_health_gathering_supreme environment.
This model was trained using Sample-Factory 2.0: URL
Documentation for how to use Sample-Factory can be found at URL
## Downloading the model
After installing Sample-Factory, download the model with:
## Using the model
To run the model after download, use the 'enjoy' script corresponding to this environment:
You can also upload models to the Hugging Face Hub using the same script with the '--push_to_hub' flag.
See URL for more details
## Training with this model
To continue training with this model, use the 'train' script corresponding to this environment:
Note, you may have to adjust '--train_for_env_steps' to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
[
"## Downloading the model\n\nAfter installing Sample-Factory, download the model with:",
"## Using the model\n\nTo run the model after download, use the 'enjoy' script corresponding to this environment:\n\n\n\nYou can also upload models to the Hugging Face Hub using the same script with the '--push_to_hub' flag.\nSee URL for more details",
"## Training with this model\n\nTo continue training with this model, use the 'train' script corresponding to this environment:\n\n\nNote, you may have to adjust '--train_for_env_steps' to a suitably high number as the experiment will resume at the number of steps it concluded at."
] |
[
"TAGS\n#sample-factory #tensorboard #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"## Downloading the model\n\nAfter installing Sample-Factory, download the model with:",
"## Using the model\n\nTo run the model after download, use the 'enjoy' script corresponding to this environment:\n\n\n\nYou can also upload models to the Hugging Face Hub using the same script with the '--push_to_hub' flag.\nSee URL for more details",
"## Training with this model\n\nTo continue training with this model, use the 'train' script corresponding to this environment:\n\n\nNote, you may have to adjust '--train_for_env_steps' to a suitably high number as the experiment will resume at the number of steps it concluded at."
] |
reinforcement-learning
|
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": "256.50 +/- 17.38", "name": "mean_reward", "verified": false}]}]}]}
|
saousan/ppo-LunarLander-v2
| null |
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null |
2024-04-13T13:59:22+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"
] |
reinforcement-learning
|
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": "259.26 +/- 17.64", "name": "mean_reward", "verified": false}]}]}]}
|
Likeuk/ppo-LunarLander-v2-likek
| null |
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null |
2024-04-13T13:59:34+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"
] |
reinforcement-learning
|
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": "262.74 +/- 17.40", "name": "mean_reward", "verified": false}]}]}]}
|
SamirLahouar/ppo-LunarLander-v2
| null |
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null |
2024-04-13T13:59:53+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"
] |
null |
transformers
|
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/bunnycore/CreativeSmart-2x7B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/CreativeSmart-2x7B-GGUF/resolve/main/CreativeSmart-2x7B.Q2_K.gguf) | Q2_K | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/CreativeSmart-2x7B-GGUF/resolve/main/CreativeSmart-2x7B.IQ3_XS.gguf) | IQ3_XS | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/CreativeSmart-2x7B-GGUF/resolve/main/CreativeSmart-2x7B.Q3_K_S.gguf) | Q3_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/CreativeSmart-2x7B-GGUF/resolve/main/CreativeSmart-2x7B.IQ3_S.gguf) | IQ3_S | 5.7 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/CreativeSmart-2x7B-GGUF/resolve/main/CreativeSmart-2x7B.IQ3_M.gguf) | IQ3_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/CreativeSmart-2x7B-GGUF/resolve/main/CreativeSmart-2x7B.Q3_K_M.gguf) | Q3_K_M | 6.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/CreativeSmart-2x7B-GGUF/resolve/main/CreativeSmart-2x7B.Q3_K_L.gguf) | Q3_K_L | 6.8 | |
| [GGUF](https://huggingface.co/mradermacher/CreativeSmart-2x7B-GGUF/resolve/main/CreativeSmart-2x7B.IQ4_XS.gguf) | IQ4_XS | 7.1 | |
| [GGUF](https://huggingface.co/mradermacher/CreativeSmart-2x7B-GGUF/resolve/main/CreativeSmart-2x7B.Q4_K_S.gguf) | Q4_K_S | 7.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/CreativeSmart-2x7B-GGUF/resolve/main/CreativeSmart-2x7B.Q4_K_M.gguf) | Q4_K_M | 7.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/CreativeSmart-2x7B-GGUF/resolve/main/CreativeSmart-2x7B.Q5_K_S.gguf) | Q5_K_S | 9.0 | |
| [GGUF](https://huggingface.co/mradermacher/CreativeSmart-2x7B-GGUF/resolve/main/CreativeSmart-2x7B.Q5_K_M.gguf) | Q5_K_M | 9.2 | |
| [GGUF](https://huggingface.co/mradermacher/CreativeSmart-2x7B-GGUF/resolve/main/CreativeSmart-2x7B.Q6_K.gguf) | Q6_K | 10.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/CreativeSmart-2x7B-GGUF/resolve/main/CreativeSmart-2x7B.Q8_0.gguf) | Q8_0 | 13.8 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
{"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["moe", "frankenmoe", "merge", "mergekit", "lazymergekit", "Nexusflow/Starling-LM-7B-beta", "bunnycore/Chimera-Apex-7B"], "base_model": "bunnycore/CreativeSmart-2x7B", "quantized_by": "mradermacher"}
|
mradermacher/CreativeSmart-2x7B-GGUF
| null |
[
"transformers",
"gguf",
"moe",
"frankenmoe",
"merge",
"mergekit",
"lazymergekit",
"Nexusflow/Starling-LM-7B-beta",
"bunnycore/Chimera-Apex-7B",
"en",
"base_model:bunnycore/CreativeSmart-2x7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2024-04-13T14:05:37+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #gguf #moe #frankenmoe #merge #mergekit #lazymergekit #Nexusflow/Starling-LM-7B-beta #bunnycore/Chimera-Apex-7B #en #base_model-bunnycore/CreativeSmart-2x7B #license-apache-2.0 #endpoints_compatible #region-us
|
About
-----
static quants of URL
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
Usage
-----
If you are unsure how to use GGUF files, refer to one of TheBloke's
READMEs for
more details, including on how to concatenate multi-part files.
Provided Quants
---------------
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
!URL
And here are Artefact2's thoughts on the matter:
URL
FAQ / Model Request
-------------------
See URL for some answers to
questions you might have and/or if you want some other model quantized.
Thanks
------
I thank my company, nethype GmbH, for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
|
[] |
[
"TAGS\n#transformers #gguf #moe #frankenmoe #merge #mergekit #lazymergekit #Nexusflow/Starling-LM-7B-beta #bunnycore/Chimera-Apex-7B #en #base_model-bunnycore/CreativeSmart-2x7B #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
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. -->
# llama2-7b-base
This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3907
## 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: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.4847 | 1.0 | 2694 | 0.3899 |
| 0.3707 | 2.0 | 5388 | 0.3813 |
| 0.309 | 3.0 | 8082 | 0.3836 |
| 0.2186 | 4.0 | 10776 | 0.3907 |
### Framework versions
- PEFT 0.7.2.dev0
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.2
|
{"library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "meta-llama/Llama-2-7b-chat-hf", "model-index": [{"name": "llama2-7b-base", "results": []}]}
|
K-kiron/llama2-7b-base
| null |
[
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | null |
2024-04-13T14:06:27+00:00
|
[] |
[] |
TAGS
#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-meta-llama/Llama-2-7b-chat-hf #region-us
|
llama2-7b-base
==============
This model is a fine-tuned version of meta-llama/Llama-2-7b-chat-hf on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3907
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: 4
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.03
* num\_epochs: 4
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* PEFT 0.7.2.dev0
* Transformers 4.36.2
* Pytorch 2.1.2+cu121
* Datasets 2.16.1
* Tokenizers 0.15.2
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 4\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* lr\\_scheduler\\_warmup\\_ratio: 0.03\n* num\\_epochs: 4\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.7.2.dev0\n* Transformers 4.36.2\n* Pytorch 2.1.2+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.2"
] |
[
"TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-meta-llama/Llama-2-7b-chat-hf #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 4\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* lr\\_scheduler\\_warmup\\_ratio: 0.03\n* num\\_epochs: 4\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.7.2.dev0\n* Transformers 4.36.2\n* Pytorch 2.1.2+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.2"
] |
text-generation
|
transformers
|
# Main model
- **Developed by:** LeroyDyer
- **License:** apache-2.0
- **Finetuned from model :** LeroyDyer/Mixtral_AI_CyberTron_Swahili_SFT
This model will be fully swahili speaking despite being adapted from and english speaking model : All training applied will be in swahili or other dialects @
undergoing fine tuning stages as well as merging stages and retuning stages ! Searching for instruct datasets in swahili
this is a super fine tuned model .... but it may be behind other models: in the series : Hence this model is for applying lora adapter found on the hub and other created for other models : once applying a lora , set the model in train mode: model.train() And Train on a previoulsy trained dataset before merging the new lora : make sure the prvious dataset still is inline with the model : Often a lora can nudge the model the wrong way and loose some of its previous training as it applys weights on top of the odel which may net be consistant with your model especially if the lora was not trained for this model (but still for the same series (ie mistral))..
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
{"language": ["sw", "en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["text-generation-inference", "transformers", "unsloth", "mistral", "trl"], "datasets": ["iamshnoo/alpaca-cleaned-swahili", "Rogendo/English-Swahili-Sentence-Pairs", "mwitiderrick/SwahiliPlatypus", "uonlp/CulturaX", "lmsys/mt_bench_human_judgments"], "base_model": "LeroyDyer/Mixtral_AI_CyberTron_Swahili_SFT"}
|
LeroyDyer/Mixtral_AI_CyberTron_Swahili_7b
| null |
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"sw",
"en",
"dataset:iamshnoo/alpaca-cleaned-swahili",
"dataset:Rogendo/English-Swahili-Sentence-Pairs",
"dataset:mwitiderrick/SwahiliPlatypus",
"dataset:uonlp/CulturaX",
"dataset:lmsys/mt_bench_human_judgments",
"base_model:LeroyDyer/Mixtral_AI_CyberTron_Swahili_SFT",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-13T14:07:32+00:00
|
[] |
[
"sw",
"en"
] |
TAGS
#transformers #safetensors #mistral #text-generation #text-generation-inference #unsloth #trl #conversational #sw #en #dataset-iamshnoo/alpaca-cleaned-swahili #dataset-Rogendo/English-Swahili-Sentence-Pairs #dataset-mwitiderrick/SwahiliPlatypus #dataset-uonlp/CulturaX #dataset-lmsys/mt_bench_human_judgments #base_model-LeroyDyer/Mixtral_AI_CyberTron_Swahili_SFT #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Main model
- Developed by: LeroyDyer
- License: apache-2.0
- Finetuned from model : LeroyDyer/Mixtral_AI_CyberTron_Swahili_SFT
This model will be fully swahili speaking despite being adapted from and english speaking model : All training applied will be in swahili or other dialects @
undergoing fine tuning stages as well as merging stages and retuning stages ! Searching for instruct datasets in swahili
this is a super fine tuned model .... but it may be behind other models: in the series : Hence this model is for applying lora adapter found on the hub and other created for other models : once applying a lora , set the model in train mode: URL() And Train on a previoulsy trained dataset before merging the new lora : make sure the prvious dataset still is inline with the model : Often a lora can nudge the model the wrong way and loose some of its previous training as it applys weights on top of the odel which may net be consistant with your model especially if the lora was not trained for this model (but still for the same series (ie mistral))..
<img src="URL width="200"/>
|
[
"# Main model\n- Developed by: LeroyDyer\n- License: apache-2.0\n- Finetuned from model : LeroyDyer/Mixtral_AI_CyberTron_Swahili_SFT\nThis model will be fully swahili speaking despite being adapted from and english speaking model : All training applied will be in swahili or other dialects @\n\nundergoing fine tuning stages as well as merging stages and retuning stages ! Searching for instruct datasets in swahili\n\nthis is a super fine tuned model .... but it may be behind other models: in the series : Hence this model is for applying lora adapter found on the hub and other created for other models : once applying a lora , set the model in train mode: URL() And Train on a previoulsy trained dataset before merging the new lora : make sure the prvious dataset still is inline with the model : Often a lora can nudge the model the wrong way and loose some of its previous training as it applys weights on top of the odel which may net be consistant with your model especially if the lora was not trained for this model (but still for the same series (ie mistral))..\n\n<img src=\"URL width=\"200\"/>"
] |
[
"TAGS\n#transformers #safetensors #mistral #text-generation #text-generation-inference #unsloth #trl #conversational #sw #en #dataset-iamshnoo/alpaca-cleaned-swahili #dataset-Rogendo/English-Swahili-Sentence-Pairs #dataset-mwitiderrick/SwahiliPlatypus #dataset-uonlp/CulturaX #dataset-lmsys/mt_bench_human_judgments #base_model-LeroyDyer/Mixtral_AI_CyberTron_Swahili_SFT #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Main model\n- Developed by: LeroyDyer\n- License: apache-2.0\n- Finetuned from model : LeroyDyer/Mixtral_AI_CyberTron_Swahili_SFT\nThis model will be fully swahili speaking despite being adapted from and english speaking model : All training applied will be in swahili or other dialects @\n\nundergoing fine tuning stages as well as merging stages and retuning stages ! Searching for instruct datasets in swahili\n\nthis is a super fine tuned model .... but it may be behind other models: in the series : Hence this model is for applying lora adapter found on the hub and other created for other models : once applying a lora , set the model in train mode: URL() And Train on a previoulsy trained dataset before merging the new lora : make sure the prvious dataset still is inline with the model : Often a lora can nudge the model the wrong way and loose some of its previous training as it applys weights on top of the odel which may net be consistant with your model especially if the lora was not trained for this model (but still for the same series (ie mistral))..\n\n<img src=\"URL width=\"200\"/>"
] |
text-classification
|
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. -->
# results11
This model is a fine-tuned version of [helinivan/english-sarcasm-detector](https://huggingface.co/helinivan/english-sarcasm-detector) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6606
- Accuracy: 0.7233
- F1: 0.4286
## 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: 500
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "helinivan/english-sarcasm-detector", "model-index": [{"name": "results11", "results": []}]}
|
dianamihalache27/results11
| null |
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:helinivan/english-sarcasm-detector",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-13T14:08:56+00:00
|
[] |
[] |
TAGS
#transformers #safetensors #bert #text-classification #generated_from_trainer #base_model-helinivan/english-sarcasm-detector #autotrain_compatible #endpoints_compatible #region-us
|
# results11
This model is a fine-tuned version of helinivan/english-sarcasm-detector on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6606
- Accuracy: 0.7233
- F1: 0.4286
## 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: 500
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
[
"# results11\n\nThis model is a fine-tuned version of helinivan/english-sarcasm-detector on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.6606\n- Accuracy: 0.7233\n- F1: 0.4286",
"## 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: 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: 500\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.2\n- Pytorch 2.2.1+cu121\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] |
[
"TAGS\n#transformers #safetensors #bert #text-classification #generated_from_trainer #base_model-helinivan/english-sarcasm-detector #autotrain_compatible #endpoints_compatible #region-us \n",
"# results11\n\nThis model is a fine-tuned version of helinivan/english-sarcasm-detector on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.6606\n- Accuracy: 0.7233\n- F1: 0.4286",
"## 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: 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: 500\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.2\n- Pytorch 2.2.1+cu121\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] |
text-generation
|
transformers
|
# merge
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 [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [openchat/openchat_3.5](https://huggingface.co/openchat/openchat_3.5) as a base.
### Models Merged
The following models were included in the merge:
* [berkeley-nest/Starling-LM-7B-alpha](https://huggingface.co/berkeley-nest/Starling-LM-7B-alpha)
* [FuseAI/OpenChat-3.5-7B-Solar](https://huggingface.co/FuseAI/OpenChat-3.5-7B-Solar)
* [FuseAI/OpenChat-3.5-7B-Mixtral](https://huggingface.co/FuseAI/OpenChat-3.5-7B-Mixtral)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: openchat/openchat_3.5
- model: FuseAI/OpenChat-3.5-7B-Mixtral
- model: FuseAI/OpenChat-3.5-7B-Solar
- model: berkeley-nest/Starling-LM-7B-alpha
merge_method: model_stock
base_model: openchat/openchat_3.5
dtype: bfloat16
```
|
{"license": "apache-2.0", "library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["berkeley-nest/Starling-LM-7B-alpha", "FuseAI/OpenChat-3.5-7B-Solar", "openchat/openchat_3.5", "FuseAI/OpenChat-3.5-7B-Mixtral"]}
|
nlpguy/StarFusion-alpha1
| null |
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2403.19522",
"base_model:berkeley-nest/Starling-LM-7B-alpha",
"base_model:FuseAI/OpenChat-3.5-7B-Solar",
"base_model:openchat/openchat_3.5",
"base_model:FuseAI/OpenChat-3.5-7B-Mixtral",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-13T14:09:00+00:00
|
[
"2403.19522"
] |
[] |
TAGS
#transformers #safetensors #mistral #text-generation #mergekit #merge #conversational #arxiv-2403.19522 #base_model-berkeley-nest/Starling-LM-7B-alpha #base_model-FuseAI/OpenChat-3.5-7B-Solar #base_model-openchat/openchat_3.5 #base_model-FuseAI/OpenChat-3.5-7B-Mixtral #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# merge
This is a merge of pre-trained language models created using mergekit.
## Merge Details
### Merge Method
This model was merged using the Model Stock merge method using openchat/openchat_3.5 as a base.
### Models Merged
The following models were included in the merge:
* berkeley-nest/Starling-LM-7B-alpha
* FuseAI/OpenChat-3.5-7B-Solar
* FuseAI/OpenChat-3.5-7B-Mixtral
### Configuration
The following YAML configuration was used to produce this model:
|
[
"# merge\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the Model Stock merge method using openchat/openchat_3.5 as a base.",
"### Models Merged\n\nThe following models were included in the merge:\n* berkeley-nest/Starling-LM-7B-alpha\n* FuseAI/OpenChat-3.5-7B-Solar\n* FuseAI/OpenChat-3.5-7B-Mixtral",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] |
[
"TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #conversational #arxiv-2403.19522 #base_model-berkeley-nest/Starling-LM-7B-alpha #base_model-FuseAI/OpenChat-3.5-7B-Solar #base_model-openchat/openchat_3.5 #base_model-FuseAI/OpenChat-3.5-7B-Mixtral #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# merge\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the Model Stock merge method using openchat/openchat_3.5 as a base.",
"### Models Merged\n\nThe following models were included in the merge:\n* berkeley-nest/Starling-LM-7B-alpha\n* FuseAI/OpenChat-3.5-7B-Solar\n* FuseAI/OpenChat-3.5-7B-Mixtral",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] |
null | null |
# Cran-May/Mistral-22B-v0.2-Q2_K-GGUF
This model was converted to GGUF format from [`Vezora/Mistral-22B-v0.2`](https://huggingface.co/Vezora/Mistral-22B-v0.2) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Vezora/Mistral-22B-v0.2) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo Cran-May/Mistral-22B-v0.2-Q2_K-GGUF --model mistral-22b-v0.2.Q2_K.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo Cran-May/Mistral-22B-v0.2-Q2_K-GGUF --model mistral-22b-v0.2.Q2_K.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m mistral-22b-v0.2.Q2_K.gguf -n 128
```
|
{"license": "apache-2.0", "tags": ["llama-cpp", "gguf-my-repo"]}
|
Cran-May/Mistral-22B-v0.2-Q2_K-GGUF
| null |
[
"gguf",
"llama-cpp",
"gguf-my-repo",
"license:apache-2.0",
"region:us"
] | null |
2024-04-13T14:12:36+00:00
|
[] |
[] |
TAGS
#gguf #llama-cpp #gguf-my-repo #license-apache-2.0 #region-us
|
# Cran-May/Mistral-22B-v0.2-Q2_K-GGUF
This model was converted to GGUF format from 'Vezora/Mistral-22B-v0.2' using URL via the URL's GGUF-my-repo space.
Refer to the original model card for more details on the model.
## Use with URL
Install URL through brew.
Invoke the URL server or the CLI.
CLI:
Server:
Note: You can also use this checkpoint directly through the usage steps listed in the URL repo as well.
|
[
"# Cran-May/Mistral-22B-v0.2-Q2_K-GGUF\nThis model was converted to GGUF format from 'Vezora/Mistral-22B-v0.2' using URL via the URL's GGUF-my-repo space.\nRefer to the original model card for more details on the model.",
"## Use with URL\n\nInstall URL through brew.\n\n\nInvoke the URL server or the CLI.\n\nCLI:\n\n\n\nServer:\n\n\n\nNote: You can also use this checkpoint directly through the usage steps listed in the URL repo as well."
] |
[
"TAGS\n#gguf #llama-cpp #gguf-my-repo #license-apache-2.0 #region-us \n",
"# Cran-May/Mistral-22B-v0.2-Q2_K-GGUF\nThis model was converted to GGUF format from 'Vezora/Mistral-22B-v0.2' using URL via the URL's GGUF-my-repo space.\nRefer to the original model card for more details on the model.",
"## Use with URL\n\nInstall URL through brew.\n\n\nInvoke the URL server or the CLI.\n\nCLI:\n\n\n\nServer:\n\n\n\nNote: You can also use this checkpoint directly through the usage steps listed in the URL repo as well."
] |
text-generation
|
transformers
|
# Uploaded model
- **Developed by:** ntvcie
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-2b-bnb-4bit
This gemma 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", "gemma", "trl"], "base_model": "unsloth/gemma-2b-bnb-4bit"}
|
ntvcie/Gemma2bVinhntV4_16bit
| null |
[
"transformers",
"pytorch",
"gemma",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:unsloth/gemma-2b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-13T14:14:35+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gemma #text-generation #text-generation-inference #unsloth #trl #conversational #en #base_model-unsloth/gemma-2b-bnb-4bit #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Uploaded model
- Developed by: ntvcie
- License: apache-2.0
- Finetuned from model : unsloth/gemma-2b-bnb-4bit
This gemma model was trained 2x faster with Unsloth and Huggingface's TRL library.
<img src="URL width="200"/>
|
[
"# Uploaded model\n\n- Developed by: ntvcie\n- License: apache-2.0\n- Finetuned from model : unsloth/gemma-2b-bnb-4bit\n\nThis gemma model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] |
[
"TAGS\n#transformers #pytorch #gemma #text-generation #text-generation-inference #unsloth #trl #conversational #en #base_model-unsloth/gemma-2b-bnb-4bit #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Uploaded model\n\n- Developed by: ntvcie\n- License: apache-2.0\n- Finetuned from model : unsloth/gemma-2b-bnb-4bit\n\nThis gemma model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] |
text-generation
|
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
{"library_name": "transformers", "tags": []}
|
YOUSSEF88/gemma-2b-mt-nl-sql-v1
| null |
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-13T14:20:44+00:00
|
[
"1910.09700"
] |
[] |
TAGS
#transformers #safetensors #gemma #text-generation #conversational #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:
- 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 #gemma #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"
] |
text-generation
|
transformers
|
# merge
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 [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [openchat/openchat_3.5](https://huggingface.co/openchat/openchat_3.5) as a base.
### Models Merged
The following models were included in the merge:
* [rishiraj/smol-7b](https://huggingface.co/rishiraj/smol-7b)
* [FuseAI/OpenChat-3.5-7B-Mixtral](https://huggingface.co/FuseAI/OpenChat-3.5-7B-Mixtral)
* [berkeley-nest/Starling-LM-7B-alpha](https://huggingface.co/berkeley-nest/Starling-LM-7B-alpha)
* [FuseAI/OpenChat-3.5-7B-Solar](https://huggingface.co/FuseAI/OpenChat-3.5-7B-Solar)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: openchat/openchat_3.5
- model: FuseAI/OpenChat-3.5-7B-Mixtral
- model: FuseAI/OpenChat-3.5-7B-Solar
- model: berkeley-nest/Starling-LM-7B-alpha
- model: rishiraj/smol-7b
merge_method: model_stock
base_model: openchat/openchat_3.5
dtype: bfloat16
```
|
{"license": "apache-2.0", "library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["rishiraj/smol-7b", "FuseAI/OpenChat-3.5-7B-Mixtral", "openchat/openchat_3.5", "berkeley-nest/Starling-LM-7B-alpha", "FuseAI/OpenChat-3.5-7B-Solar"]}
|
nlpguy/StarFusion-alpha2
| null |
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2403.19522",
"base_model:rishiraj/smol-7b",
"base_model:FuseAI/OpenChat-3.5-7B-Mixtral",
"base_model:openchat/openchat_3.5",
"base_model:berkeley-nest/Starling-LM-7B-alpha",
"base_model:FuseAI/OpenChat-3.5-7B-Solar",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-13T14:21:03+00:00
|
[
"2403.19522"
] |
[] |
TAGS
#transformers #safetensors #mistral #text-generation #mergekit #merge #conversational #arxiv-2403.19522 #base_model-rishiraj/smol-7b #base_model-FuseAI/OpenChat-3.5-7B-Mixtral #base_model-openchat/openchat_3.5 #base_model-berkeley-nest/Starling-LM-7B-alpha #base_model-FuseAI/OpenChat-3.5-7B-Solar #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# merge
This is a merge of pre-trained language models created using mergekit.
## Merge Details
### Merge Method
This model was merged using the Model Stock merge method using openchat/openchat_3.5 as a base.
### Models Merged
The following models were included in the merge:
* rishiraj/smol-7b
* FuseAI/OpenChat-3.5-7B-Mixtral
* berkeley-nest/Starling-LM-7B-alpha
* FuseAI/OpenChat-3.5-7B-Solar
### Configuration
The following YAML configuration was used to produce this model:
|
[
"# merge\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the Model Stock merge method using openchat/openchat_3.5 as a base.",
"### Models Merged\n\nThe following models were included in the merge:\n* rishiraj/smol-7b\n* FuseAI/OpenChat-3.5-7B-Mixtral\n* berkeley-nest/Starling-LM-7B-alpha\n* FuseAI/OpenChat-3.5-7B-Solar",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] |
[
"TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #conversational #arxiv-2403.19522 #base_model-rishiraj/smol-7b #base_model-FuseAI/OpenChat-3.5-7B-Mixtral #base_model-openchat/openchat_3.5 #base_model-berkeley-nest/Starling-LM-7B-alpha #base_model-FuseAI/OpenChat-3.5-7B-Solar #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# merge\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the Model Stock merge method using openchat/openchat_3.5 as a base.",
"### Models Merged\n\nThe following models were included in the merge:\n* rishiraj/smol-7b\n* FuseAI/OpenChat-3.5-7B-Mixtral\n* berkeley-nest/Starling-LM-7B-alpha\n* FuseAI/OpenChat-3.5-7B-Solar",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] |
text-generation
|
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
{"library_name": "transformers", "tags": []}
|
zzttbrdd/sn6_00m
| null |
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-13T14:23:27+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
## 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 #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"
] |
text-classification
|
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. -->
# results1_2
This model is a fine-tuned version of [jkhan447/sarcasm-detection-Bert-base-uncased](https://huggingface.co/jkhan447/sarcasm-detection-Bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8462
- Accuracy: 0.7161
- F1: 0.4084
## 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: 200
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "jkhan447/sarcasm-detection-Bert-base-uncased", "model-index": [{"name": "results1_2", "results": []}]}
|
dianamihalache27/results1_2
| null |
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:jkhan447/sarcasm-detection-Bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-13T14:24:47+00:00
|
[] |
[] |
TAGS
#transformers #safetensors #bert #text-classification #generated_from_trainer #base_model-jkhan447/sarcasm-detection-Bert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# results1_2
This model is a fine-tuned version of jkhan447/sarcasm-detection-Bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8462
- Accuracy: 0.7161
- F1: 0.4084
## 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: 200
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
[
"# results1_2\n\nThis model is a fine-tuned version of jkhan447/sarcasm-detection-Bert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.8462\n- Accuracy: 0.7161\n- F1: 0.4084",
"## 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: 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: 200\n- num_epochs: 5",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.2\n- Pytorch 2.2.1+cu121\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] |
[
"TAGS\n#transformers #safetensors #bert #text-classification #generated_from_trainer #base_model-jkhan447/sarcasm-detection-Bert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# results1_2\n\nThis model is a fine-tuned version of jkhan447/sarcasm-detection-Bert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 1.8462\n- Accuracy: 0.7161\n- F1: 0.4084",
"## 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: 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: 200\n- num_epochs: 5",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.2\n- Pytorch 2.2.1+cu121\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] |
text-generation
|
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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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
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
{"library_name": "transformers", "tags": []}
|
zzttbrdd/sn6_01m
| null |
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-13T14:26:36+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
## 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 #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"
] |
null | null |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Mistral-7B-Instruct-v0.1 - GGUF
- Model creator: https://huggingface.co/mistralai/
- Original model: https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Mistral-7B-Instruct-v0.1.Q2_K.gguf](https://huggingface.co/RichardErkhov/mistralai_-_Mistral-7B-Instruct-v0.1-gguf/blob/main/Mistral-7B-Instruct-v0.1.Q2_K.gguf) | Q2_K | 2.53GB |
| [Mistral-7B-Instruct-v0.1.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/mistralai_-_Mistral-7B-Instruct-v0.1-gguf/blob/main/Mistral-7B-Instruct-v0.1.IQ3_XS.gguf) | IQ3_XS | 2.81GB |
| [Mistral-7B-Instruct-v0.1.IQ3_S.gguf](https://huggingface.co/RichardErkhov/mistralai_-_Mistral-7B-Instruct-v0.1-gguf/blob/main/Mistral-7B-Instruct-v0.1.IQ3_S.gguf) | IQ3_S | 2.96GB |
| [Mistral-7B-Instruct-v0.1.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/mistralai_-_Mistral-7B-Instruct-v0.1-gguf/blob/main/Mistral-7B-Instruct-v0.1.Q3_K_S.gguf) | Q3_K_S | 2.95GB |
| [Mistral-7B-Instruct-v0.1.IQ3_M.gguf](https://huggingface.co/RichardErkhov/mistralai_-_Mistral-7B-Instruct-v0.1-gguf/blob/main/Mistral-7B-Instruct-v0.1.IQ3_M.gguf) | IQ3_M | 3.06GB |
| [Mistral-7B-Instruct-v0.1.Q3_K.gguf](https://huggingface.co/RichardErkhov/mistralai_-_Mistral-7B-Instruct-v0.1-gguf/blob/main/Mistral-7B-Instruct-v0.1.Q3_K.gguf) | Q3_K | 3.28GB |
| [Mistral-7B-Instruct-v0.1.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/mistralai_-_Mistral-7B-Instruct-v0.1-gguf/blob/main/Mistral-7B-Instruct-v0.1.Q3_K_M.gguf) | Q3_K_M | 3.28GB |
| [Mistral-7B-Instruct-v0.1.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/mistralai_-_Mistral-7B-Instruct-v0.1-gguf/blob/main/Mistral-7B-Instruct-v0.1.Q3_K_L.gguf) | Q3_K_L | 3.56GB |
| [Mistral-7B-Instruct-v0.1.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/mistralai_-_Mistral-7B-Instruct-v0.1-gguf/blob/main/Mistral-7B-Instruct-v0.1.IQ4_XS.gguf) | IQ4_XS | 3.67GB |
| [Mistral-7B-Instruct-v0.1.Q4_0.gguf](https://huggingface.co/RichardErkhov/mistralai_-_Mistral-7B-Instruct-v0.1-gguf/blob/main/Mistral-7B-Instruct-v0.1.Q4_0.gguf) | Q4_0 | 3.83GB |
| [Mistral-7B-Instruct-v0.1.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/mistralai_-_Mistral-7B-Instruct-v0.1-gguf/blob/main/Mistral-7B-Instruct-v0.1.IQ4_NL.gguf) | IQ4_NL | 3.87GB |
| [Mistral-7B-Instruct-v0.1.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/mistralai_-_Mistral-7B-Instruct-v0.1-gguf/blob/main/Mistral-7B-Instruct-v0.1.Q4_K_S.gguf) | Q4_K_S | 3.86GB |
| [Mistral-7B-Instruct-v0.1.Q4_K.gguf](https://huggingface.co/RichardErkhov/mistralai_-_Mistral-7B-Instruct-v0.1-gguf/blob/main/Mistral-7B-Instruct-v0.1.Q4_K.gguf) | Q4_K | 4.07GB |
| [Mistral-7B-Instruct-v0.1.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/mistralai_-_Mistral-7B-Instruct-v0.1-gguf/blob/main/Mistral-7B-Instruct-v0.1.Q4_K_M.gguf) | Q4_K_M | 4.07GB |
| [Mistral-7B-Instruct-v0.1.Q4_1.gguf](https://huggingface.co/RichardErkhov/mistralai_-_Mistral-7B-Instruct-v0.1-gguf/blob/main/Mistral-7B-Instruct-v0.1.Q4_1.gguf) | Q4_1 | 4.24GB |
| [Mistral-7B-Instruct-v0.1.Q5_0.gguf](https://huggingface.co/RichardErkhov/mistralai_-_Mistral-7B-Instruct-v0.1-gguf/blob/main/Mistral-7B-Instruct-v0.1.Q5_0.gguf) | Q5_0 | 4.65GB |
| [Mistral-7B-Instruct-v0.1.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/mistralai_-_Mistral-7B-Instruct-v0.1-gguf/blob/main/Mistral-7B-Instruct-v0.1.Q5_K_S.gguf) | Q5_K_S | 4.65GB |
| [Mistral-7B-Instruct-v0.1.Q5_K.gguf](https://huggingface.co/RichardErkhov/mistralai_-_Mistral-7B-Instruct-v0.1-gguf/blob/main/Mistral-7B-Instruct-v0.1.Q5_K.gguf) | Q5_K | 4.78GB |
| [Mistral-7B-Instruct-v0.1.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/mistralai_-_Mistral-7B-Instruct-v0.1-gguf/blob/main/Mistral-7B-Instruct-v0.1.Q5_K_M.gguf) | Q5_K_M | 4.78GB |
| [Mistral-7B-Instruct-v0.1.Q5_1.gguf](https://huggingface.co/RichardErkhov/mistralai_-_Mistral-7B-Instruct-v0.1-gguf/blob/main/Mistral-7B-Instruct-v0.1.Q5_1.gguf) | Q5_1 | 5.07GB |
| [Mistral-7B-Instruct-v0.1.Q6_K.gguf](https://huggingface.co/RichardErkhov/mistralai_-_Mistral-7B-Instruct-v0.1-gguf/blob/main/Mistral-7B-Instruct-v0.1.Q6_K.gguf) | Q6_K | 5.53GB |
Original model description:
---
license: apache-2.0
pipeline_tag: text-generation
tags:
- finetuned
inference: true
widget:
- messages:
- role: user
content: What is your favorite condiment?
---
# Model Card for Mistral-7B-Instruct-v0.1
The Mistral-7B-Instruct-v0.1 Large Language Model (LLM) is a instruct fine-tuned version of the [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) generative text model using a variety of publicly available conversation datasets.
For full details of this model please read our [paper](https://arxiv.org/abs/2310.06825) and [release blog post](https://mistral.ai/news/announcing-mistral-7b/).
## Instruction format
In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[/INST]` tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.
E.g.
```
text = "<s>[INST] What is your favourite condiment? [/INST]"
"Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> "
"[INST] Do you have mayonnaise recipes? [/INST]"
```
This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")
messages = [
{"role": "user", "content": "What is your favourite condiment?"},
{"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
{"role": "user", "content": "Do you have mayonnaise recipes?"}
]
encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
model_inputs = encodeds.to(device)
model.to(device)
generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])
```
## Model Architecture
This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:
- Grouped-Query Attention
- Sliding-Window Attention
- Byte-fallback BPE tokenizer
## Troubleshooting
- If you see the following error:
```
Traceback (most recent call last):
File "", line 1, in
File "/transformers/models/auto/auto_factory.py", line 482, in from_pretrained
config, kwargs = AutoConfig.from_pretrained(
File "/transformers/models/auto/configuration_auto.py", line 1022, in from_pretrained
config_class = CONFIG_MAPPING[config_dict["model_type"]]
File "/transformers/models/auto/configuration_auto.py", line 723, in getitem
raise KeyError(key)
KeyError: 'mistral'
```
Installing transformers from source should solve the issue
pip install git+https://github.com/huggingface/transformers
This should not be required after transformers-v4.33.4.
## Limitations
The Mistral 7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance.
It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to
make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.
## The Mistral AI Team
Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.
|
{}
|
RichardErkhov/mistralai_-_Mistral-7B-Instruct-v0.1-gguf
| null |
[
"gguf",
"arxiv:2310.06825",
"region:us"
] | null |
2024-04-13T14:29:01+00:00
|
[
"2310.06825"
] |
[] |
TAGS
#gguf #arxiv-2310.06825 #region-us
|
Quantization made by Richard Erkhov.
Github
Discord
Request more models
Mistral-7B-Instruct-v0.1 - GGUF
* Model creator: URL
* Original model: URL
Name: Mistral-7B-Instruct-v0.1.Q2\_K.gguf, Quant method: Q2\_K, Size: 2.53GB
Name: Mistral-7B-Instruct-v0.1.IQ3\_XS.gguf, Quant method: IQ3\_XS, Size: 2.81GB
Name: Mistral-7B-Instruct-v0.1.IQ3\_S.gguf, Quant method: IQ3\_S, Size: 2.96GB
Name: Mistral-7B-Instruct-v0.1.Q3\_K\_S.gguf, Quant method: Q3\_K\_S, Size: 2.95GB
Name: Mistral-7B-Instruct-v0.1.IQ3\_M.gguf, Quant method: IQ3\_M, Size: 3.06GB
Name: Mistral-7B-Instruct-v0.1.Q3\_K.gguf, Quant method: Q3\_K, Size: 3.28GB
Name: Mistral-7B-Instruct-v0.1.Q3\_K\_M.gguf, Quant method: Q3\_K\_M, Size: 3.28GB
Name: Mistral-7B-Instruct-v0.1.Q3\_K\_L.gguf, Quant method: Q3\_K\_L, Size: 3.56GB
Name: Mistral-7B-Instruct-v0.1.IQ4\_XS.gguf, Quant method: IQ4\_XS, Size: 3.67GB
Name: Mistral-7B-Instruct-v0.1.Q4\_0.gguf, Quant method: Q4\_0, Size: 3.83GB
Name: Mistral-7B-Instruct-v0.1.IQ4\_NL.gguf, Quant method: IQ4\_NL, Size: 3.87GB
Name: Mistral-7B-Instruct-v0.1.Q4\_K\_S.gguf, Quant method: Q4\_K\_S, Size: 3.86GB
Name: Mistral-7B-Instruct-v0.1.Q4\_K.gguf, Quant method: Q4\_K, Size: 4.07GB
Name: Mistral-7B-Instruct-v0.1.Q4\_K\_M.gguf, Quant method: Q4\_K\_M, Size: 4.07GB
Name: Mistral-7B-Instruct-v0.1.Q4\_1.gguf, Quant method: Q4\_1, Size: 4.24GB
Name: Mistral-7B-Instruct-v0.1.Q5\_0.gguf, Quant method: Q5\_0, Size: 4.65GB
Name: Mistral-7B-Instruct-v0.1.Q5\_K\_S.gguf, Quant method: Q5\_K\_S, Size: 4.65GB
Name: Mistral-7B-Instruct-v0.1.Q5\_K.gguf, Quant method: Q5\_K, Size: 4.78GB
Name: Mistral-7B-Instruct-v0.1.Q5\_K\_M.gguf, Quant method: Q5\_K\_M, Size: 4.78GB
Name: Mistral-7B-Instruct-v0.1.Q5\_1.gguf, Quant method: Q5\_1, Size: 5.07GB
Name: Mistral-7B-Instruct-v0.1.Q6\_K.gguf, Quant method: Q6\_K, Size: 5.53GB
Original model description:
---------------------------
license: apache-2.0
pipeline\_tag: text-generation
tags:
* finetuned
inference: true
widget:
* messages:
+ role: user
content: What is your favorite condiment?
---
Model Card for Mistral-7B-Instruct-v0.1
=======================================
The Mistral-7B-Instruct-v0.1 Large Language Model (LLM) is a instruct fine-tuned version of the Mistral-7B-v0.1 generative text model using a variety of publicly available conversation datasets.
For full details of this model please read our paper and release blog post.
Instruction format
------------------
In order to leverage instruction fine-tuning, your prompt should be surrounded by '[INST]' and '[/INST]' tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.
E.g.
This format is available as a chat template via the 'apply\_chat\_template()' method:
Model Architecture
------------------
This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:
* Grouped-Query Attention
* Sliding-Window Attention
* Byte-fallback BPE tokenizer
Troubleshooting
---------------
* If you see the following error:
Installing transformers from source should solve the issue
pip install git+URL
This should not be required after transformers-v4.33.4.
Limitations
-----------
The Mistral 7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance.
It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to
make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.
The Mistral AI Team
-------------------
Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.
|
[] |
[
"TAGS\n#gguf #arxiv-2310.06825 #region-us \n"
] |
null | null |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Mistral-7B-v0.1 - GGUF
- Model creator: https://huggingface.co/mistralai/
- Original model: https://huggingface.co/mistralai/Mistral-7B-v0.1/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Mistral-7B-v0.1.Q2_K.gguf](https://huggingface.co/RichardErkhov/mistralai_-_Mistral-7B-v0.1-gguf/blob/main/Mistral-7B-v0.1.Q2_K.gguf) | Q2_K | 2.53GB |
| [Mistral-7B-v0.1.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/mistralai_-_Mistral-7B-v0.1-gguf/blob/main/Mistral-7B-v0.1.IQ3_XS.gguf) | IQ3_XS | 2.81GB |
| [Mistral-7B-v0.1.IQ3_S.gguf](https://huggingface.co/RichardErkhov/mistralai_-_Mistral-7B-v0.1-gguf/blob/main/Mistral-7B-v0.1.IQ3_S.gguf) | IQ3_S | 2.96GB |
| [Mistral-7B-v0.1.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/mistralai_-_Mistral-7B-v0.1-gguf/blob/main/Mistral-7B-v0.1.Q3_K_S.gguf) | Q3_K_S | 2.95GB |
| [Mistral-7B-v0.1.IQ3_M.gguf](https://huggingface.co/RichardErkhov/mistralai_-_Mistral-7B-v0.1-gguf/blob/main/Mistral-7B-v0.1.IQ3_M.gguf) | IQ3_M | 3.06GB |
| [Mistral-7B-v0.1.Q3_K.gguf](https://huggingface.co/RichardErkhov/mistralai_-_Mistral-7B-v0.1-gguf/blob/main/Mistral-7B-v0.1.Q3_K.gguf) | Q3_K | 3.28GB |
| [Mistral-7B-v0.1.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/mistralai_-_Mistral-7B-v0.1-gguf/blob/main/Mistral-7B-v0.1.Q3_K_M.gguf) | Q3_K_M | 3.28GB |
| [Mistral-7B-v0.1.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/mistralai_-_Mistral-7B-v0.1-gguf/blob/main/Mistral-7B-v0.1.Q3_K_L.gguf) | Q3_K_L | 3.56GB |
| [Mistral-7B-v0.1.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/mistralai_-_Mistral-7B-v0.1-gguf/blob/main/Mistral-7B-v0.1.IQ4_XS.gguf) | IQ4_XS | 3.67GB |
| [Mistral-7B-v0.1.Q4_0.gguf](https://huggingface.co/RichardErkhov/mistralai_-_Mistral-7B-v0.1-gguf/blob/main/Mistral-7B-v0.1.Q4_0.gguf) | Q4_0 | 3.83GB |
| [Mistral-7B-v0.1.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/mistralai_-_Mistral-7B-v0.1-gguf/blob/main/Mistral-7B-v0.1.IQ4_NL.gguf) | IQ4_NL | 3.87GB |
| [Mistral-7B-v0.1.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/mistralai_-_Mistral-7B-v0.1-gguf/blob/main/Mistral-7B-v0.1.Q4_K_S.gguf) | Q4_K_S | 3.86GB |
| [Mistral-7B-v0.1.Q4_K.gguf](https://huggingface.co/RichardErkhov/mistralai_-_Mistral-7B-v0.1-gguf/blob/main/Mistral-7B-v0.1.Q4_K.gguf) | Q4_K | 4.07GB |
| [Mistral-7B-v0.1.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/mistralai_-_Mistral-7B-v0.1-gguf/blob/main/Mistral-7B-v0.1.Q4_K_M.gguf) | Q4_K_M | 4.07GB |
| [Mistral-7B-v0.1.Q4_1.gguf](https://huggingface.co/RichardErkhov/mistralai_-_Mistral-7B-v0.1-gguf/blob/main/Mistral-7B-v0.1.Q4_1.gguf) | Q4_1 | 4.24GB |
| [Mistral-7B-v0.1.Q5_0.gguf](https://huggingface.co/RichardErkhov/mistralai_-_Mistral-7B-v0.1-gguf/blob/main/Mistral-7B-v0.1.Q5_0.gguf) | Q5_0 | 4.65GB |
| [Mistral-7B-v0.1.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/mistralai_-_Mistral-7B-v0.1-gguf/blob/main/Mistral-7B-v0.1.Q5_K_S.gguf) | Q5_K_S | 4.65GB |
| [Mistral-7B-v0.1.Q5_K.gguf](https://huggingface.co/RichardErkhov/mistralai_-_Mistral-7B-v0.1-gguf/blob/main/Mistral-7B-v0.1.Q5_K.gguf) | Q5_K | 4.78GB |
| [Mistral-7B-v0.1.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/mistralai_-_Mistral-7B-v0.1-gguf/blob/main/Mistral-7B-v0.1.Q5_K_M.gguf) | Q5_K_M | 4.78GB |
| [Mistral-7B-v0.1.Q5_1.gguf](https://huggingface.co/RichardErkhov/mistralai_-_Mistral-7B-v0.1-gguf/blob/main/Mistral-7B-v0.1.Q5_1.gguf) | Q5_1 | 5.07GB |
| [Mistral-7B-v0.1.Q6_K.gguf](https://huggingface.co/RichardErkhov/mistralai_-_Mistral-7B-v0.1-gguf/blob/main/Mistral-7B-v0.1.Q6_K.gguf) | Q6_K | 5.53GB |
Original model description:
---
license: apache-2.0
pipeline_tag: text-generation
language:
- en
tags:
- pretrained
inference:
parameters:
temperature: 0.7
---
# Model Card for Mistral-7B-v0.1
The Mistral-7B-v0.1 Large Language Model (LLM) is a pretrained generative text model with 7 billion parameters.
Mistral-7B-v0.1 outperforms Llama 2 13B on all benchmarks we tested.
For full details of this model please read our [paper](https://arxiv.org/abs/2310.06825) and [release blog post](https://mistral.ai/news/announcing-mistral-7b/).
## Model Architecture
Mistral-7B-v0.1 is a transformer model, with the following architecture choices:
- Grouped-Query Attention
- Sliding-Window Attention
- Byte-fallback BPE tokenizer
## Troubleshooting
- If you see the following error:
```
KeyError: 'mistral'
```
- Or:
```
NotImplementedError: Cannot copy out of meta tensor; no data!
```
Ensure you are utilizing a stable version of Transformers, 4.34.0 or newer.
## Notice
Mistral 7B is a pretrained base model and therefore does not have any moderation mechanisms.
## The Mistral AI Team
Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.
|
{}
|
RichardErkhov/mistralai_-_Mistral-7B-v0.1-gguf
| null |
[
"gguf",
"arxiv:2310.06825",
"region:us"
] | null |
2024-04-13T14:29:48+00:00
|
[
"2310.06825"
] |
[] |
TAGS
#gguf #arxiv-2310.06825 #region-us
|
Quantization made by Richard Erkhov.
Github
Discord
Request more models
Mistral-7B-v0.1 - GGUF
* Model creator: URL
* Original model: URL
Name: Mistral-7B-v0.1.Q2\_K.gguf, Quant method: Q2\_K, Size: 2.53GB
Name: Mistral-7B-v0.1.IQ3\_XS.gguf, Quant method: IQ3\_XS, Size: 2.81GB
Name: Mistral-7B-v0.1.IQ3\_S.gguf, Quant method: IQ3\_S, Size: 2.96GB
Name: Mistral-7B-v0.1.Q3\_K\_S.gguf, Quant method: Q3\_K\_S, Size: 2.95GB
Name: Mistral-7B-v0.1.IQ3\_M.gguf, Quant method: IQ3\_M, Size: 3.06GB
Name: Mistral-7B-v0.1.Q3\_K.gguf, Quant method: Q3\_K, Size: 3.28GB
Name: Mistral-7B-v0.1.Q3\_K\_M.gguf, Quant method: Q3\_K\_M, Size: 3.28GB
Name: Mistral-7B-v0.1.Q3\_K\_L.gguf, Quant method: Q3\_K\_L, Size: 3.56GB
Name: Mistral-7B-v0.1.IQ4\_XS.gguf, Quant method: IQ4\_XS, Size: 3.67GB
Name: Mistral-7B-v0.1.Q4\_0.gguf, Quant method: Q4\_0, Size: 3.83GB
Name: Mistral-7B-v0.1.IQ4\_NL.gguf, Quant method: IQ4\_NL, Size: 3.87GB
Name: Mistral-7B-v0.1.Q4\_K\_S.gguf, Quant method: Q4\_K\_S, Size: 3.86GB
Name: Mistral-7B-v0.1.Q4\_K.gguf, Quant method: Q4\_K, Size: 4.07GB
Name: Mistral-7B-v0.1.Q4\_K\_M.gguf, Quant method: Q4\_K\_M, Size: 4.07GB
Name: Mistral-7B-v0.1.Q4\_1.gguf, Quant method: Q4\_1, Size: 4.24GB
Name: Mistral-7B-v0.1.Q5\_0.gguf, Quant method: Q5\_0, Size: 4.65GB
Name: Mistral-7B-v0.1.Q5\_K\_S.gguf, Quant method: Q5\_K\_S, Size: 4.65GB
Name: Mistral-7B-v0.1.Q5\_K.gguf, Quant method: Q5\_K, Size: 4.78GB
Name: Mistral-7B-v0.1.Q5\_K\_M.gguf, Quant method: Q5\_K\_M, Size: 4.78GB
Name: Mistral-7B-v0.1.Q5\_1.gguf, Quant method: Q5\_1, Size: 5.07GB
Name: Mistral-7B-v0.1.Q6\_K.gguf, Quant method: Q6\_K, Size: 5.53GB
Original model description:
---------------------------
license: apache-2.0
pipeline\_tag: text-generation
language:
* en
tags:
* pretrained
inference:
parameters:
temperature: 0.7
---
Model Card for Mistral-7B-v0.1
==============================
The Mistral-7B-v0.1 Large Language Model (LLM) is a pretrained generative text model with 7 billion parameters.
Mistral-7B-v0.1 outperforms Llama 2 13B on all benchmarks we tested.
For full details of this model please read our paper and release blog post.
Model Architecture
------------------
Mistral-7B-v0.1 is a transformer model, with the following architecture choices:
* Grouped-Query Attention
* Sliding-Window Attention
* Byte-fallback BPE tokenizer
Troubleshooting
---------------
* If you see the following error:
* Or:
Ensure you are utilizing a stable version of Transformers, 4.34.0 or newer.
Notice
------
Mistral 7B is a pretrained base model and therefore does not have any moderation mechanisms.
The Mistral AI Team
-------------------
Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.
|
[] |
[
"TAGS\n#gguf #arxiv-2310.06825 #region-us \n"
] |
text-to-audio
|
transformers
|
# Tango 2: Aligning Diffusion-based Text-to-Audio Generative Models through Direct Preference Optimization
🎵 We developed **Tango 2** building upon **Tango** for text-to-audio generation. Tango 2 was initialized with the Tango-full-ft checkpoint and underwent alignment training using DPO on audio-alpaca, a pairwise text-to-audio preference dataset. **Tango-2-full** was trained on an extended version of **Audio-alpaca** 🎶
[Read the paper](https://arxiv.org/abs/2404.09956)
## Code
Our code is released here: [https://github.com/declare-lab/tango](https://github.com/declare-lab/tango)
Please follow the instructions in the repository for installation, usage and experiments.
## Quickstart Guide
Download the **Tango 2** model and generate audio from a text prompt:
```python
import IPython
import soundfile as sf
from tango import Tango
tango = Tango("declare-lab/tango2-full")
prompt = "An audience cheering and clapping"
audio = tango.generate(prompt)
sf.write(f"{prompt}.wav", audio, samplerate=16000)
IPython.display.Audio(data=audio, rate=16000)
```
The model will be automatically downloaded and saved in cache. Subsequent runs will load the model directly from cache.
The `generate` function uses 100 steps by default to sample from the latent diffusion model. We recommend using 200 steps for generating better quality audios. This comes at the cost of increased run-time.
```python
prompt = "Rolling thunder with lightning strikes"
audio = tango.generate(prompt, steps=200)
IPython.display.Audio(data=audio, rate=16000)
```
Use the `generate_for_batch` function to generate multiple audio samples for a batch of text prompts:
```python
prompts = [
"A car engine revving",
"A dog barks and rustles with some clicking",
"Water flowing and trickling"
]
audios = tango.generate_for_batch(prompts, samples=2)
```
This will generate two samples for each of the three text prompts.
|
{"language": ["en"], "license": "cc-by-nc-sa-4.0", "tags": ["text-to-audio"], "datasets": ["bjoernp/AudioCaps", "declare-lab/audio-alpaca"], "pipeline_tag": "text-to-audio"}
|
declare-lab/tango2-full
| null |
[
"transformers",
"text-to-audio",
"en",
"dataset:bjoernp/AudioCaps",
"dataset:declare-lab/audio-alpaca",
"arxiv:2404.09956",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2024-04-13T14:30:09+00:00
|
[
"2404.09956"
] |
[
"en"
] |
TAGS
#transformers #text-to-audio #en #dataset-bjoernp/AudioCaps #dataset-declare-lab/audio-alpaca #arxiv-2404.09956 #license-cc-by-nc-sa-4.0 #endpoints_compatible #has_space #region-us
|
# Tango 2: Aligning Diffusion-based Text-to-Audio Generative Models through Direct Preference Optimization
We developed Tango 2 building upon Tango for text-to-audio generation. Tango 2 was initialized with the Tango-full-ft checkpoint and underwent alignment training using DPO on audio-alpaca, a pairwise text-to-audio preference dataset. Tango-2-full was trained on an extended version of Audio-alpaca
Read the paper
## Code
Our code is released here: URL
Please follow the instructions in the repository for installation, usage and experiments.
## Quickstart Guide
Download the Tango 2 model and generate audio from a text prompt:
The model will be automatically downloaded and saved in cache. Subsequent runs will load the model directly from cache.
The 'generate' function uses 100 steps by default to sample from the latent diffusion model. We recommend using 200 steps for generating better quality audios. This comes at the cost of increased run-time.
Use the 'generate_for_batch' function to generate multiple audio samples for a batch of text prompts:
This will generate two samples for each of the three text prompts.
|
[
"# Tango 2: Aligning Diffusion-based Text-to-Audio Generative Models through Direct Preference Optimization\n\n We developed Tango 2 building upon Tango for text-to-audio generation. Tango 2 was initialized with the Tango-full-ft checkpoint and underwent alignment training using DPO on audio-alpaca, a pairwise text-to-audio preference dataset. Tango-2-full was trained on an extended version of Audio-alpaca \n\nRead the paper",
"## Code\n\nOur code is released here: URL\n\n\nPlease follow the instructions in the repository for installation, usage and experiments.",
"## Quickstart Guide\n\nDownload the Tango 2 model and generate audio from a text prompt:\n\n\n\nThe model will be automatically downloaded and saved in cache. Subsequent runs will load the model directly from cache.\n\nThe 'generate' function uses 100 steps by default to sample from the latent diffusion model. We recommend using 200 steps for generating better quality audios. This comes at the cost of increased run-time.\n\n\n\n\nUse the 'generate_for_batch' function to generate multiple audio samples for a batch of text prompts:\n\n\nThis will generate two samples for each of the three text prompts."
] |
[
"TAGS\n#transformers #text-to-audio #en #dataset-bjoernp/AudioCaps #dataset-declare-lab/audio-alpaca #arxiv-2404.09956 #license-cc-by-nc-sa-4.0 #endpoints_compatible #has_space #region-us \n",
"# Tango 2: Aligning Diffusion-based Text-to-Audio Generative Models through Direct Preference Optimization\n\n We developed Tango 2 building upon Tango for text-to-audio generation. Tango 2 was initialized with the Tango-full-ft checkpoint and underwent alignment training using DPO on audio-alpaca, a pairwise text-to-audio preference dataset. Tango-2-full was trained on an extended version of Audio-alpaca \n\nRead the paper",
"## Code\n\nOur code is released here: URL\n\n\nPlease follow the instructions in the repository for installation, usage and experiments.",
"## Quickstart Guide\n\nDownload the Tango 2 model and generate audio from a text prompt:\n\n\n\nThe model will be automatically downloaded and saved in cache. Subsequent runs will load the model directly from cache.\n\nThe 'generate' function uses 100 steps by default to sample from the latent diffusion model. We recommend using 200 steps for generating better quality audios. This comes at the cost of increased run-time.\n\n\n\n\nUse the 'generate_for_batch' function to generate multiple audio samples for a batch of text prompts:\n\n\nThis will generate two samples for each of the three text prompts."
] |
text-classification
|
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. -->
# results1_3
This model is a fine-tuned version of [jkhan447/sarcasm-detection-Bert-base-uncased](https://huggingface.co/jkhan447/sarcasm-detection-Bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1833
- Accuracy: 0.7233
- F1: 0.4607
## 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
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "jkhan447/sarcasm-detection-Bert-base-uncased", "model-index": [{"name": "results1_3", "results": []}]}
|
dianamihalache27/results1_3
| null |
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:jkhan447/sarcasm-detection-Bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-13T14:30:54+00:00
|
[] |
[] |
TAGS
#transformers #safetensors #bert #text-classification #generated_from_trainer #base_model-jkhan447/sarcasm-detection-Bert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# results1_3
This model is a fine-tuned version of jkhan447/sarcasm-detection-Bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1833
- Accuracy: 0.7233
- F1: 0.4607
## 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
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
[
"# results1_3\n\nThis model is a fine-tuned version of jkhan447/sarcasm-detection-Bert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.1833\n- Accuracy: 0.7233\n- F1: 0.4607",
"## 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- lr_scheduler_warmup_steps: 100\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.2\n- Pytorch 2.2.1+cu121\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] |
[
"TAGS\n#transformers #safetensors #bert #text-classification #generated_from_trainer #base_model-jkhan447/sarcasm-detection-Bert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# results1_3\n\nThis model is a fine-tuned version of jkhan447/sarcasm-detection-Bert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 2.1833\n- Accuracy: 0.7233\n- F1: 0.4607",
"## 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- lr_scheduler_warmup_steps: 100\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.2\n- Pytorch 2.2.1+cu121\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] |
null |
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
{"library_name": "transformers", "tags": []}
|
likhithasapu/generator-gemma-2b-it
| null |
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null |
2024-04-13T14:34:07+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",
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"## 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"
] |
text-generation
|
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
{"library_name": "transformers", "tags": []}
|
domenicrosati/beavertails_attack_meta-llama_Llama-2-7b-chat-hf_3e-5_10k
| null |
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-13T14:34:14+00:00
|
[
"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:
- 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]:",
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"## Bias, Risks, and Limitations",
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"## 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:",
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"### 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 #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]:",
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"## Training Details",
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"### Training Procedure",
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"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
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"### Model Architecture and Objective",
"### Compute Infrastructure",
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"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
text-generation
|
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[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]
|
{"library_name": "transformers", "tags": []}
|
zzttbrdd/sn6_05m
| null |
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-13T14:34:56+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
## 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]",
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"### 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 #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]:",
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"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
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"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
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"## 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"
] |
text-classification
|
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. -->
# results
This model is a fine-tuned version of [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2888
- F1: 0.8188
## 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: cosine_with_restarts
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.6378 | 1.0 | 778 | 0.5933 | 0.5576 |
| 0.2868 | 2.0 | 1556 | 0.3661 | 0.6994 |
| 0.1598 | 3.0 | 2334 | 0.2979 | 0.7942 |
| 0.0722 | 4.0 | 3112 | 0.2888 | 0.8188 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "base_model": "intfloat/multilingual-e5-small", "model-index": [{"name": "results", "results": []}]}
|
Samoed/e5-small-hackaton
| null |
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:intfloat/multilingual-e5-small",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-13T14:35:16+00:00
|
[] |
[] |
TAGS
#transformers #safetensors #bert #text-classification #generated_from_trainer #base_model-intfloat/multilingual-e5-small #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
results
=======
This model is a fine-tuned version of intfloat/multilingual-e5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2888
* F1: 0.8188
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: cosine\_with\_restarts
* num\_epochs: 4
### Training results
### Framework versions
* Transformers 4.38.2
* Pytorch 2.2.1+cu121
* Datasets 2.18.0
* Tokenizers 0.15.2
|
[
"### 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: cosine\\_with\\_restarts\n* num\\_epochs: 4",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.2.1+cu121\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] |
[
"TAGS\n#transformers #safetensors #bert #text-classification #generated_from_trainer #base_model-intfloat/multilingual-e5-small #license-mit #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: cosine\\_with\\_restarts\n* num\\_epochs: 4",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.2.1+cu121\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] |
null |
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
{"library_name": "transformers", "tags": []}
|
HeydarS/stable_lm2_witQA_peft_v51
| null |
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null |
2024-04-13T14:36:09+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]:",
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"## Training Details",
"### Training Data",
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"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
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"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
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"## Model Card Contact"
] |
[
"TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n",
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"## Bias, Risks, and Limitations",
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"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
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"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
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"## 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"
] |
text-classification
|
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. -->
# amazon_helpfulness_classification_on_25M_full_pretrained_best_epoch_f1
This model is a fine-tuned version of [ltuzova/amazon_domain_pretrained_model](https://huggingface.co/ltuzova/amazon_domain_pretrained_model) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4608
- Accuracy: 0.8715
- F1 Macro: 0.6989
## 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=(0.9,0.98) and epsilon=1e-06
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.3122 | 1.0 | 7204 | 0.3319 | 0.8662 | 0.5845 |
| 0.2849 | 2.0 | 14408 | 0.3249 | 0.8762 | 0.6800 |
| 0.2655 | 3.0 | 21612 | 0.3479 | 0.872 | 0.6419 |
| 0.2387 | 4.0 | 28816 | 0.4371 | 0.8722 | 0.6910 |
| 0.216 | 5.0 | 36020 | 0.4716 | 0.8692 | 0.7047 |
| 0.1526 | 6.0 | 43224 | 0.5920 | 0.8726 | 0.6825 |
| 0.1242 | 7.0 | 50428 | 0.6568 | 0.873 | 0.6754 |
| 0.1177 | 8.0 | 57632 | 0.7506 | 0.8666 | 0.6755 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "ltuzova/amazon_domain_pretrained_model", "model-index": [{"name": "amazon_helpfulness_classification_on_25M_full_pretrained_best_epoch_f1", "results": []}]}
|
ltuzova/amazon_helpfulness_classification_on_25M_full_pretrained_best_epoch_f1
| null |
[
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:ltuzova/amazon_domain_pretrained_model",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-13T14:41:48+00:00
|
[] |
[] |
TAGS
#transformers #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #base_model-ltuzova/amazon_domain_pretrained_model #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
amazon\_helpfulness\_classification\_on\_25M\_full\_pretrained\_best\_epoch\_f1
===============================================================================
This model is a fine-tuned version of ltuzova/amazon\_domain\_pretrained\_model on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4608
* Accuracy: 0.8715
* F1 Macro: 0.6989
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=(0.9,0.98) and epsilon=1e-06
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.06
* num\_epochs: 10
### Training results
### Framework versions
* Transformers 4.38.2
* Pytorch 2.2.1+cu121
* Datasets 2.18.0
* Tokenizers 0.15.2
|
[
"### 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=(0.9,0.98) and epsilon=1e-06\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.06\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.2.1+cu121\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] |
[
"TAGS\n#transformers #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #base_model-ltuzova/amazon_domain_pretrained_model #license-mit #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=(0.9,0.98) and epsilon=1e-06\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.06\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.2.1+cu121\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] |
null |
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
{"library_name": "transformers", "tags": []}
|
Arati2310/opt125m-lora
| null |
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null |
2024-04-13T14:41:54+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"
] |
null |
adapter-transformers
|
# Adapter `BigTMiami/AA_adp_seq_bn_P_micro` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [BigTMiami/amazon_25M_10_000_condensed](https://huggingface.co/datasets/BigTMiami/amazon_25M_10_000_condensed/) dataset and includes a prediction head for masked lm.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("roberta-base")
adapter_name = model.load_adapter("BigTMiami/AA_adp_seq_bn_P_micro", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
{"tags": ["roberta", "adapter-transformers"], "datasets": ["BigTMiami/amazon_25M_10_000_condensed"]}
|
BigTMiami/AA_adp_seq_bn_P_micro
| null |
[
"adapter-transformers",
"roberta",
"dataset:BigTMiami/amazon_25M_10_000_condensed",
"region:us"
] | null |
2024-04-13T14:44:25+00:00
|
[] |
[] |
TAGS
#adapter-transformers #roberta #dataset-BigTMiami/amazon_25M_10_000_condensed #region-us
|
# Adapter 'BigTMiami/AA_adp_seq_bn_P_micro' for roberta-base
An adapter for the 'roberta-base' model that was trained on the BigTMiami/amazon_25M_10_000_condensed dataset and includes a prediction head for masked lm.
This adapter was created for usage with the Adapters library.
## Usage
First, install 'adapters':
Now, the adapter can be loaded and activated like this:
## Architecture & Training
## Evaluation results
|
[
"# Adapter 'BigTMiami/AA_adp_seq_bn_P_micro' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/amazon_25M_10_000_condensed dataset and includes a prediction head for masked lm.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] |
[
"TAGS\n#adapter-transformers #roberta #dataset-BigTMiami/amazon_25M_10_000_condensed #region-us \n",
"# Adapter 'BigTMiami/AA_adp_seq_bn_P_micro' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/amazon_25M_10_000_condensed dataset and includes a prediction head for masked lm.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] |
null |
transformers
|
# Abirate/gemma-1.1-7b-it-finetuned-on-kaggle-writeups-Q4_K_M-GGUF
This model was converted to GGUF format from [`Abirate/gemma-1.1-7b-it-finetuned-on-kaggle-writeups`](https://huggingface.co/Abirate/gemma-1.1-7b-it-finetuned-on-kaggle-writeups) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Abirate/gemma-1.1-7b-it-finetuned-on-kaggle-writeups) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo Abirate/gemma-1.1-7b-it-finetuned-on-kaggle-writeups-Q4_K_M-GGUF --model gemma-1.1-7b-it-finetuned-on-kaggle-writeups.Q4_K_M.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo Abirate/gemma-1.1-7b-it-finetuned-on-kaggle-writeups-Q4_K_M-GGUF --model gemma-1.1-7b-it-finetuned-on-kaggle-writeups.Q4_K_M.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m gemma-1.1-7b-it-finetuned-on-kaggle-writeups.Q4_K_M.gguf -n 128
```
|
{"library_name": "transformers", "tags": ["llama-cpp", "gguf-my-repo"]}
|
Abirate/gemma-1.1-7b-it-finetuned-on-kaggle-writeups-Q4_K_M-GGUF
| null |
[
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"endpoints_compatible",
"region:us"
] | null |
2024-04-13T14:45:57+00:00
|
[] |
[] |
TAGS
#transformers #gguf #llama-cpp #gguf-my-repo #endpoints_compatible #region-us
|
# Abirate/gemma-1.1-7b-it-finetuned-on-kaggle-writeups-Q4_K_M-GGUF
This model was converted to GGUF format from 'Abirate/gemma-1.1-7b-it-finetuned-on-kaggle-writeups' using URL via the URL's GGUF-my-repo space.
Refer to the original model card for more details on the model.
## Use with URL
Install URL through brew.
Invoke the URL server or the CLI.
CLI:
Server:
Note: You can also use this checkpoint directly through the usage steps listed in the URL repo as well.
|
[
"# Abirate/gemma-1.1-7b-it-finetuned-on-kaggle-writeups-Q4_K_M-GGUF\nThis model was converted to GGUF format from 'Abirate/gemma-1.1-7b-it-finetuned-on-kaggle-writeups' using URL via the URL's GGUF-my-repo space.\nRefer to the original model card for more details on the model.",
"## Use with URL\n\nInstall URL through brew.\n\n\nInvoke the URL server or the CLI.\n\nCLI:\n\n\n\nServer:\n\n\n\nNote: You can also use this checkpoint directly through the usage steps listed in the URL repo as well."
] |
[
"TAGS\n#transformers #gguf #llama-cpp #gguf-my-repo #endpoints_compatible #region-us \n",
"# Abirate/gemma-1.1-7b-it-finetuned-on-kaggle-writeups-Q4_K_M-GGUF\nThis model was converted to GGUF format from 'Abirate/gemma-1.1-7b-it-finetuned-on-kaggle-writeups' using URL via the URL's GGUF-my-repo space.\nRefer to the original model card for more details on the model.",
"## Use with URL\n\nInstall URL through brew.\n\n\nInvoke the URL server or the CLI.\n\nCLI:\n\n\n\nServer:\n\n\n\nNote: You can also use this checkpoint directly through the usage steps listed in the URL repo as well."
] |
null |
adapter-transformers
|
# Adapter `BigTMiami/AA_adp_seq_bn_C_micro` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [BigTMiami/amazon_helpfulness](https://huggingface.co/datasets/BigTMiami/amazon_helpfulness/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("roberta-base")
adapter_name = model.load_adapter("BigTMiami/AA_adp_seq_bn_C_micro", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
{"tags": ["roberta", "adapter-transformers"], "datasets": ["BigTMiami/amazon_helpfulness"]}
|
BigTMiami/AA_adp_seq_bn_C_micro
| null |
[
"adapter-transformers",
"roberta",
"dataset:BigTMiami/amazon_helpfulness",
"region:us"
] | null |
2024-04-13T14:46:56+00:00
|
[] |
[] |
TAGS
#adapter-transformers #roberta #dataset-BigTMiami/amazon_helpfulness #region-us
|
# Adapter 'BigTMiami/AA_adp_seq_bn_C_micro' for roberta-base
An adapter for the 'roberta-base' model that was trained on the BigTMiami/amazon_helpfulness dataset and includes a prediction head for classification.
This adapter was created for usage with the Adapters library.
## Usage
First, install 'adapters':
Now, the adapter can be loaded and activated like this:
## Architecture & Training
## Evaluation results
|
[
"# Adapter 'BigTMiami/AA_adp_seq_bn_C_micro' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/amazon_helpfulness dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] |
[
"TAGS\n#adapter-transformers #roberta #dataset-BigTMiami/amazon_helpfulness #region-us \n",
"# Adapter 'BigTMiami/AA_adp_seq_bn_C_micro' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/amazon_helpfulness dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] |
image-classification
|
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. -->
# Psoriasis-M-vit-large-patch16-224-in21k
This model is a fine-tuned version of [google/vit-large-patch16-224-in21k](https://huggingface.co/google/vit-large-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1464
- Accuracy: 0.9792
## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.92 | 6 | 1.1103 | 0.6667 |
| 1.4249 | 2.0 | 13 | 0.5429 | 0.875 |
| 1.4249 | 2.92 | 19 | 0.3529 | 0.9167 |
| 0.3823 | 4.0 | 26 | 0.2861 | 0.9375 |
| 0.1072 | 4.62 | 30 | 0.2801 | 0.9375 |
| No log | 0.92 | 6 | 0.2647 | 0.9167 |
| 0.0527 | 2.0 | 13 | 0.1843 | 0.9792 |
| 0.0527 | 2.92 | 19 | 0.1604 | 0.9792 |
| 0.0138 | 4.0 | 26 | 0.1480 | 0.9792 |
| 0.0074 | 4.62 | 30 | 0.1464 | 0.9792 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "google/vit-large-patch16-224-in21k", "pipeline_tag": "image-classification", "model-index": [{"name": "Psoriasis-M-vit-large-patch16-224-in21k", "results": []}]}
|
ahmedesmail16/Psoriasis-M-vit-large-patch16-224-in21k
| null |
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"base_model:google/vit-large-patch16-224-in21k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-13T14:47:10+00:00
|
[] |
[] |
TAGS
#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #base_model-google/vit-large-patch16-224-in21k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
Psoriasis-M-vit-large-patch16-224-in21k
=======================================
This model is a fine-tuned version of google/vit-large-patch16-224-in21k on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1464
* Accuracy: 0.9792
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
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 5
### Training results
### Framework versions
* Transformers 4.39.3
* Pytorch 2.1.2
* Datasets 2.18.0
* Tokenizers 0.15.2
|
[
"### 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* gradient\\_accumulation\\_steps: 4\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\\_ratio: 0.1\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.39.3\n* Pytorch 2.1.2\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] |
[
"TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #base_model-google/vit-large-patch16-224-in21k #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* gradient\\_accumulation\\_steps: 4\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\\_ratio: 0.1\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.39.3\n* Pytorch 2.1.2\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] |
text-generation
|
transformers
|
# merge
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 SLERP merge method.
### Models Merged
The following models were included in the merge:
* [NousResearch/Hermes-2-Pro-Mistral-7B](https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B)
* [WizardLM/WizardMath-7B-V1.1](https://huggingface.co/WizardLM/WizardMath-7B-V1.1)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: NousResearch/Hermes-2-Pro-Mistral-7B
- model: WizardLM/WizardMath-7B-V1.1
merge_method: slerp
base_model: NousResearch/Hermes-2-Pro-Mistral-7B
dtype: bfloat16
parameters:
t: [0, 0.5, 1, 0.5, 0] # V shaped curve: Hermes for input & output, WizardMath in the middle layers
```
|
{"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["NousResearch/Hermes-2-Pro-Mistral-7B", "WizardLM/WizardMath-7B-V1.1"]}
|
mergekit-community/mergekit-slerp-ghxdzjf
| null |
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:NousResearch/Hermes-2-Pro-Mistral-7B",
"base_model:WizardLM/WizardMath-7B-V1.1",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-13T14:48:45+00:00
|
[] |
[] |
TAGS
#transformers #safetensors #mistral #text-generation #mergekit #merge #conversational #base_model-NousResearch/Hermes-2-Pro-Mistral-7B #base_model-WizardLM/WizardMath-7B-V1.1 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# merge
This is a merge of pre-trained language models created using mergekit.
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* NousResearch/Hermes-2-Pro-Mistral-7B
* WizardLM/WizardMath-7B-V1.1
### Configuration
The following YAML configuration was used to produce this model:
|
[
"# merge\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the SLERP merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* NousResearch/Hermes-2-Pro-Mistral-7B\n* WizardLM/WizardMath-7B-V1.1",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] |
[
"TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #conversational #base_model-NousResearch/Hermes-2-Pro-Mistral-7B #base_model-WizardLM/WizardMath-7B-V1.1 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# merge\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the SLERP merge method.",
"### Models Merged\n\nThe following models were included in the merge:\n* NousResearch/Hermes-2-Pro-Mistral-7B\n* WizardLM/WizardMath-7B-V1.1",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] |
null | null |
# Cran-May/Mistral-22B-v0.2-Q5_K_M-GGUF
This model was converted to GGUF format from [`Vezora/Mistral-22B-v0.2`](https://huggingface.co/Vezora/Mistral-22B-v0.2) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Vezora/Mistral-22B-v0.2) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo Cran-May/Mistral-22B-v0.2-Q5_K_M-GGUF --model mistral-22b-v0.2.Q5_K_M.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo Cran-May/Mistral-22B-v0.2-Q5_K_M-GGUF --model mistral-22b-v0.2.Q5_K_M.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m mistral-22b-v0.2.Q5_K_M.gguf -n 128
```
|
{"license": "apache-2.0", "tags": ["llama-cpp", "gguf-my-repo"]}
|
Cran-May/Mistral-22B-v0.2-Q5_K_M-GGUF
| null |
[
"gguf",
"llama-cpp",
"gguf-my-repo",
"license:apache-2.0",
"region:us"
] | null |
2024-04-13T14:56:35+00:00
|
[] |
[] |
TAGS
#gguf #llama-cpp #gguf-my-repo #license-apache-2.0 #region-us
|
# Cran-May/Mistral-22B-v0.2-Q5_K_M-GGUF
This model was converted to GGUF format from 'Vezora/Mistral-22B-v0.2' using URL via the URL's GGUF-my-repo space.
Refer to the original model card for more details on the model.
## Use with URL
Install URL through brew.
Invoke the URL server or the CLI.
CLI:
Server:
Note: You can also use this checkpoint directly through the usage steps listed in the URL repo as well.
|
[
"# Cran-May/Mistral-22B-v0.2-Q5_K_M-GGUF\nThis model was converted to GGUF format from 'Vezora/Mistral-22B-v0.2' using URL via the URL's GGUF-my-repo space.\nRefer to the original model card for more details on the model.",
"## Use with URL\n\nInstall URL through brew.\n\n\nInvoke the URL server or the CLI.\n\nCLI:\n\n\n\nServer:\n\n\n\nNote: You can also use this checkpoint directly through the usage steps listed in the URL repo as well."
] |
[
"TAGS\n#gguf #llama-cpp #gguf-my-repo #license-apache-2.0 #region-us \n",
"# Cran-May/Mistral-22B-v0.2-Q5_K_M-GGUF\nThis model was converted to GGUF format from 'Vezora/Mistral-22B-v0.2' using URL via the URL's GGUF-my-repo space.\nRefer to the original model card for more details on the model.",
"## Use with URL\n\nInstall URL through brew.\n\n\nInvoke the URL server or the CLI.\n\nCLI:\n\n\n\nServer:\n\n\n\nNote: You can also use this checkpoint directly through the usage steps listed in the URL repo as well."
] |
image-segmentation
|
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_model_3
This model is a fine-tuned version of [apple/deeplabv3-mobilevit-xx-small](https://huggingface.co/apple/deeplabv3-mobilevit-xx-small) on an FrsECM/CelebAHQ_mask dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2560
- Mean Iou: 0.5809
- Mean Accuracy: 0.6806
- Overall Accuracy: 0.9139
- Per Category Iou: [0.8919686118230816, 0.6685126480236523, 0.8747044562496686, 0.8833085720795604, 0.711340654853021, 0.0017797375551187376, 0.5999932597419707, 0.43503524672708965, 0.4621466655632662, 0.5295999530392416, 0.5872745246930384, 0.47678709050791274, 0.7930179988260829, 0.5446353384631151, 0.6272271444587322, 0.6052765573405614, 0.5696758390032162, 0.2785029706405308, 0.4957813263783734, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
- Per Category Accuracy: [0.9340795455586407, 0.7993107362784472, 0.9405089464670838, 0.9459331187430433, 0.8324080810556224, 0.0017886222269681519, 0.7019941140835427, 0.5005054410951127, 0.5404423454984336, 0.5945500675475304, 0.6696180612278237, 0.6095998812163179, 0.8718696974845856, 0.6992669162717129, 0.7405660623179267, 0.7106133092784797, 0.6685587984126187, 0.36160280101147635, 0.8080611214773792, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
## 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: 6e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| 0.5588 | 0.14 | 1000 | 0.4594 | 0.3034 | 0.3775 | 0.8700 | [0.8441280428123001, 0.5693282558240229, 0.7975983961437951, 0.8498378142283486, 0.5761384911910784, 0.0, 0.12270583146229427, 0.06161763107077589, 0.002664418305407915, 0.0014959742265011834, 0.0, 0.009876162443763535, 0.6699313576513388, 0.1611794683801089, 0.29689850050845384, 0.40252735203646844, 0.2101523887973641, 0.002859159901164843, 0.4893042689942933, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.9256661621269769, 0.681558875502008, 0.9326575335612457, 0.9256377345009891, 0.7357490216500608, 0.0, 0.12812084410207514, 0.06894853769663686, 0.002673759826136486, 0.001537667358616978, 0.0, 0.009941384302331466, 0.7811656980059632, 0.20498599904467435, 0.3748039210577058, 0.5131612623786742, 0.22835503272033136, 0.0029023638653139366, 0.6555286234432215, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 0.4188 | 0.28 | 2000 | 0.3415 | 0.4454 | 0.5268 | 0.8937 | [0.8764819541275325, 0.6302661063115487, 0.8387923788103758, 0.8669951068151325, 0.6498564698554723, 0.0, 0.4527335101762678, 0.24169950114561772, 0.21804654807774423, 0.08953576565973584, 0.27393886497928394, 0.2445465361251712, 0.7341006916653414, 0.3676319928106044, 0.5113383150644849, 0.5189963435433752, 0.4480486002340621, 0.03770827106993425, 0.4616427383079672, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.9263182199537994, 0.7792085676037483, 0.9323345405550992, 0.9417726033591793, 0.7782645488868637, 0.0, 0.5548963870796606, 0.28446630344424073, 0.24003008325430028, 0.09400090063373862, 0.29738839698979536, 0.2753689211944509, 0.8192402317337436, 0.4680323437799433, 0.6314341252924899, 0.6361238037453264, 0.521879010124351, 0.041334370744991245, 0.7874424845133146, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 0.2878 | 0.43 | 3000 | 0.2944 | 0.5206 | 0.6078 | 0.9047 | [0.8854988296644709, 0.6519540194302628, 0.8572464962098427, 0.8754505136415879, 0.6765368987181419, 0.0, 0.5327179018789144, 0.3243876143913752, 0.33155747754174003, 0.3568416085045081, 0.486924442178474, 0.37113329727675376, 0.7655284850568753, 0.4561619920055543, 0.5837876209545264, 0.5597729067188029, 0.5165728665497245, 0.17104933633859254, 0.4883077052383161, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.9273581392992951, 0.7939152878179384, 0.9405569705528336, 0.9451731224764754, 0.8077724254654342, 0.0, 0.6439414849572178, 0.3747563310479865, 0.3701401581029854, 0.39448856085318573, 0.5512627830234914, 0.428390649834479, 0.8369053909052186, 0.5806864043430134, 0.708576749163136, 0.6661744728604335, 0.6065226028304342, 0.20277644120025798, 0.7689479953598335, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 0.2627 | 0.57 | 4000 | 0.2755 | 0.5614 | 0.6658 | 0.9096 | [0.8846708436689866, 0.6470210644465468, 0.8667446237928943, 0.8813140447326048, 0.6962080344743431, 0.0, 0.5602997262978417, 0.38799053760653296, 0.4312441727010948, 0.480262505138127, 0.5559833588627013, 0.4323176311512354, 0.7823147670212306, 0.5063772292885359, 0.6012913438917675, 0.593308147367188, 0.5506520810251795, 0.25707177962502276, 0.5505954536437292, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.9357588059598548, 0.7608224899598394, 0.9378491950141642, 0.9393328129837988, 0.8290981566432067, 0.0, 0.6714353821723404, 0.45083897428767566, 0.5205066595145205, 0.5582226762002043, 0.6542239792640028, 0.534071006995414, 0.8625244686719534, 0.6603388259791373, 0.7126103731189016, 0.7251929847169348, 0.6650287473113614, 0.32531045567624567, 0.9075535059468354, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 0.2641 | 0.71 | 5000 | 0.2623 | 0.5696 | 0.6668 | 0.9120 | [0.8892892227722897, 0.6648377331251554, 0.8720944303146551, 0.8803700123155216, 0.7057805874888583, 0.0, 0.5988695721747876, 0.4282063786694269, 0.4409408981430017, 0.4854531697402193, 0.5761256783558699, 0.4422358774694712, 0.7884019813396796, 0.5192982132734839, 0.6211020547667461, 0.596838916967618, 0.5553414881389638, 0.2587870728463894, 0.49913310800201643, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.9329897829365719, 0.7914778045515395, 0.9402721199368602, 0.9474222357759178, 0.8159128330410409, 0.0, 0.7512537145438056, 0.49848078020408815, 0.5132872314834528, 0.5390787175854229, 0.6596040121540824, 0.5534779659644257, 0.8649550428158117, 0.644598834731522, 0.7449974898421119, 0.7078248076417115, 0.6542216762491038, 0.325740435508144, 0.7837236038626532, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 0.2675 | 0.85 | 6000 | 0.2554 | 0.5795 | 0.6773 | 0.9134 | [0.8910400464786082, 0.6668531904215245, 0.8742927195270097, 0.8806797580973037, 0.7063579299285884, 0.0012783296492583024, 0.5852087012059962, 0.44287665224585815, 0.4730663238884368, 0.5327117185133179, 0.5859973071744566, 0.4591704694170641, 0.791660787618116, 0.5388588717907311, 0.6314070927422667, 0.5969867362256072, 0.5678845348501144, 0.26967988187340874, 0.5145654951798955, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.9260822591882713, 0.7923114323962517, 0.9380834914484188, 0.95558258877134, 0.8226199866745292, 0.0012814009984249445, 0.6700266335927391, 0.5147504800966276, 0.5657808512344364, 0.6083616154293936, 0.6635954738022892, 0.6322497696878892, 0.8717189867584043, 0.6871880532808572, 0.7611806254027979, 0.6947199238262968, 0.6595167529009897, 0.3356248528342837, 0.7681071598184418, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
| 0.2389 | 0.99 | 7000 | 0.2560 | 0.5809 | 0.6806 | 0.9139 | [0.8919686118230816, 0.6685126480236523, 0.8747044562496686, 0.8833085720795604, 0.711340654853021, 0.0017797375551187376, 0.5999932597419707, 0.43503524672708965, 0.4621466655632662, 0.5295999530392416, 0.5872745246930384, 0.47678709050791274, 0.7930179988260829, 0.5446353384631151, 0.6272271444587322, 0.6052765573405614, 0.5696758390032162, 0.2785029706405308, 0.4957813263783734, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] | [0.9340795455586407, 0.7993107362784472, 0.9405089464670838, 0.9459331187430433, 0.8324080810556224, 0.0017886222269681519, 0.7019941140835427, 0.5005054410951127, 0.5404423454984336, 0.5945500675475304, 0.6696180612278237, 0.6095998812163179, 0.8718696974845856, 0.6992669162717129, 0.7405660623179267, 0.7106133092784797, 0.6685587984126187, 0.36160280101147635, 0.8080611214773792, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan] |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"tags": ["generated_from_trainer"], "base_model": "apple/deeplabv3-mobilevit-xx-small", "pipeline_tag": "image-segmentation", "model-index": [{"name": "my_model_3", "results": []}]}
|
MatyasHajek/my_model_3
| null |
[
"transformers",
"tensorboard",
"onnx",
"safetensors",
"mobilevit",
"generated_from_trainer",
"image-segmentation",
"base_model:apple/deeplabv3-mobilevit-xx-small",
"endpoints_compatible",
"has_space",
"region:us"
] | null |
2024-04-13T14:58:54+00:00
|
[] |
[] |
TAGS
#transformers #tensorboard #onnx #safetensors #mobilevit #generated_from_trainer #image-segmentation #base_model-apple/deeplabv3-mobilevit-xx-small #endpoints_compatible #has_space #region-us
|
my\_model\_3
============
This model is a fine-tuned version of apple/deeplabv3-mobilevit-xx-small on an FrsECM/CelebAHQ\_mask dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2560
* Mean Iou: 0.5809
* Mean Accuracy: 0.6806
* Overall Accuracy: 0.9139
* Per Category Iou: [0.8919686118230816, 0.6685126480236523, 0.8747044562496686, 0.8833085720795604, 0.711340654853021, 0.0017797375551187376, 0.5999932597419707, 0.43503524672708965, 0.4621466655632662, 0.5295999530392416, 0.5872745246930384, 0.47678709050791274, 0.7930179988260829, 0.5446353384631151, 0.6272271444587322, 0.6052765573405614, 0.5696758390032162, 0.2785029706405308, 0.4957813263783734, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
* Per Category Accuracy: [0.9340795455586407, 0.7993107362784472, 0.9405089464670838, 0.9459331187430433, 0.8324080810556224, 0.0017886222269681519, 0.7019941140835427, 0.5005054410951127, 0.5404423454984336, 0.5945500675475304, 0.6696180612278237, 0.6095998812163179, 0.8718696974845856, 0.6992669162717129, 0.7405660623179267, 0.7106133092784797, 0.6685587984126187, 0.36160280101147635, 0.8080611214773792, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
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: 6e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1
### Training results
### Framework versions
* Transformers 4.39.3
* Pytorch 2.2.2
* Datasets 2.18.0
* Tokenizers 0.15.2
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\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 4.39.3\n* Pytorch 2.2.2\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] |
[
"TAGS\n#transformers #tensorboard #onnx #safetensors #mobilevit #generated_from_trainer #image-segmentation #base_model-apple/deeplabv3-mobilevit-xx-small #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\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 4.39.3\n* Pytorch 2.2.2\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] |
text-generation
|
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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<!-- 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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[More Information Needed]
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!-- This section describes the evaluation protocols and provides the results. -->
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#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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[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]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Model Card Contact
[More Information Needed]
|
{"library_name": "transformers", "tags": []}
|
minhcrafters/DialoGPT-medium-mental-health-finetuned
| null |
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-13T15:00:31+00:00
|
[
"1910.09700"
] |
[] |
TAGS
#transformers #safetensors #gpt2 #text-generation #conversational #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:
- 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 #gpt2 #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"
] |
text-generation
|
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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## Model Card Contact
[More Information Needed]
|
{"library_name": "transformers", "tags": []}
|
sabber/mi-chat-zephyr-7b-beta-zuri-convo
| null |
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-13T15:02:52+00:00
|
[
"1910.09700"
] |
[] |
TAGS
#transformers #safetensors #mistral #text-generation #conversational #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:
- 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 #mistral #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"
] |
text-generation
|
transformers
|
# Uploaded model
- **Developed by:** LeroyDyer
- **License:** apache-2.0
- **Finetuned from model :** LeroyDyer/Mixtral_AI_MiniTron_II
This is a smaller model easier for fine tuning !! (faster)
This model was created from a fresh untrained model and has only been trained with swahili : it is still training!
Plus it will run and train on the laptop no problem ! (only with text corpuses the context needs to be low as it will force the gpu to consume memory so small articles only; later after intensive training the context can be re-extended etc:
)
This model will be fully swahili speaking despite being adapted from and english speaking model : All training applied will be in swahili or other dialects @
undergoing fine tuning stages as well as merging stages and retuning stages ! Searching for instruct datasets in swahili
this is a super fine tuned model .... but it may be behind other models: in the series : Hence this model is for applying lora adapter found on the hub and other created for other models : once applying a lora , set the model in train mode: model.train() And Train on a previoulsy trained dataset before merging the new lora : make sure the prvious dataset still is inline with the model : Often a lora can nudge the model the wrong way and loose some of its previous training as it applys weights on top of the odel which may net be consistant with your model especially if the lora was not trained for this model (but still for the same series (ie mistral))..
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", "sw"], "license": "apache-2.0", "library_name": "transformers", "tags": ["text-generation-inference", "transformers", "unsloth", "mistral", "trl"], "datasets": ["iamshnoo/alpaca-cleaned-swahili"], "base_model": "LeroyDyer/Mixtral_AI_MiniTron_II"}
|
LeroyDyer/Mixtral_AI_MiniTron_Swahili_3.75b
| null |
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"sw",
"dataset:iamshnoo/alpaca-cleaned-swahili",
"base_model:LeroyDyer/Mixtral_AI_MiniTron_II",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-13T15:03:51+00:00
|
[] |
[
"en",
"sw"
] |
TAGS
#transformers #safetensors #mistral #text-generation #text-generation-inference #unsloth #trl #conversational #en #sw #dataset-iamshnoo/alpaca-cleaned-swahili #base_model-LeroyDyer/Mixtral_AI_MiniTron_II #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Uploaded model
- Developed by: LeroyDyer
- License: apache-2.0
- Finetuned from model : LeroyDyer/Mixtral_AI_MiniTron_II
This is a smaller model easier for fine tuning !! (faster)
This model was created from a fresh untrained model and has only been trained with swahili : it is still training!
Plus it will run and train on the laptop no problem ! (only with text corpuses the context needs to be low as it will force the gpu to consume memory so small articles only; later after intensive training the context can be re-extended etc:
)
This model will be fully swahili speaking despite being adapted from and english speaking model : All training applied will be in swahili or other dialects @
undergoing fine tuning stages as well as merging stages and retuning stages ! Searching for instruct datasets in swahili
this is a super fine tuned model .... but it may be behind other models: in the series : Hence this model is for applying lora adapter found on the hub and other created for other models : once applying a lora , set the model in train mode: URL() And Train on a previoulsy trained dataset before merging the new lora : make sure the prvious dataset still is inline with the model : Often a lora can nudge the model the wrong way and loose some of its previous training as it applys weights on top of the odel which may net be consistant with your model especially if the lora was not trained for this model (but still for the same series (ie mistral))..
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: LeroyDyer\n- License: apache-2.0\n- Finetuned from model : LeroyDyer/Mixtral_AI_MiniTron_II\n\n\nThis is a smaller model easier for fine tuning !! (faster) \nThis model was created from a fresh untrained model and has only been trained with swahili : it is still training!\n\nPlus it will run and train on the laptop no problem ! (only with text corpuses the context needs to be low as it will force the gpu to consume memory so small articles only; later after intensive training the context can be re-extended etc: \n)\nThis model will be fully swahili speaking despite being adapted from and english speaking model : All training applied will be in swahili or other dialects @\n\nundergoing fine tuning stages as well as merging stages and retuning stages ! Searching for instruct datasets in swahili\n\nthis is a super fine tuned model .... but it may be behind other models: in the series : Hence this model is for applying lora adapter found on the hub and other created for other models : once applying a lora , set the model in train mode: URL() And Train on a previoulsy trained dataset before merging the new lora : make sure the prvious dataset still is inline with the model : Often a lora can nudge the model the wrong way and loose some of its previous training as it applys weights on top of the odel which may net be consistant with your model especially if the lora was not trained for this model (but still for the same series (ie mistral))..\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 #safetensors #mistral #text-generation #text-generation-inference #unsloth #trl #conversational #en #sw #dataset-iamshnoo/alpaca-cleaned-swahili #base_model-LeroyDyer/Mixtral_AI_MiniTron_II #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Uploaded model\n\n- Developed by: LeroyDyer\n- License: apache-2.0\n- Finetuned from model : LeroyDyer/Mixtral_AI_MiniTron_II\n\n\nThis is a smaller model easier for fine tuning !! (faster) \nThis model was created from a fresh untrained model and has only been trained with swahili : it is still training!\n\nPlus it will run and train on the laptop no problem ! (only with text corpuses the context needs to be low as it will force the gpu to consume memory so small articles only; later after intensive training the context can be re-extended etc: \n)\nThis model will be fully swahili speaking despite being adapted from and english speaking model : All training applied will be in swahili or other dialects @\n\nundergoing fine tuning stages as well as merging stages and retuning stages ! Searching for instruct datasets in swahili\n\nthis is a super fine tuned model .... but it may be behind other models: in the series : Hence this model is for applying lora adapter found on the hub and other created for other models : once applying a lora , set the model in train mode: URL() And Train on a previoulsy trained dataset before merging the new lora : make sure the prvious dataset still is inline with the model : Often a lora can nudge the model the wrong way and loose some of its previous training as it applys weights on top of the odel which may net be consistant with your model especially if the lora was not trained for this model (but still for the same series (ie mistral))..\n\nThis mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] |
token-classification
|
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. -->
# Medical-NER-finetuned-ner
This model is a fine-tuned version of [Clinical-AI-Apollo/Medical-NER](https://huggingface.co/Clinical-AI-Apollo/Medical-NER) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2045
- Precision: 0.9394
- Recall: 0.9282
- F1: 0.9338
- Accuracy: 0.9296
## 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-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 0.37 | 100 | 0.4486 | 0.8318 | 0.8662 | 0.8486 | 0.8331 |
| No log | 0.75 | 200 | 0.3747 | 0.8608 | 0.8834 | 0.8720 | 0.8646 |
| No log | 1.12 | 300 | 0.3245 | 0.8801 | 0.8932 | 0.8866 | 0.8828 |
| No log | 1.49 | 400 | 0.2846 | 0.9128 | 0.9038 | 0.9083 | 0.9028 |
| 0.4808 | 1.87 | 500 | 0.2554 | 0.9199 | 0.9067 | 0.9133 | 0.9083 |
| 0.4808 | 2.24 | 600 | 0.2480 | 0.9270 | 0.9073 | 0.9171 | 0.9102 |
| 0.4808 | 2.61 | 700 | 0.2269 | 0.9271 | 0.9172 | 0.9221 | 0.9171 |
| 0.4808 | 2.99 | 800 | 0.2319 | 0.9270 | 0.9089 | 0.9179 | 0.9129 |
| 0.4808 | 3.36 | 900 | 0.2303 | 0.9284 | 0.9088 | 0.9185 | 0.9133 |
| 0.2633 | 3.73 | 1000 | 0.2246 | 0.9311 | 0.9111 | 0.9210 | 0.9155 |
| 0.2633 | 4.1 | 1100 | 0.2120 | 0.9343 | 0.9218 | 0.9280 | 0.9236 |
| 0.2633 | 4.48 | 1200 | 0.2111 | 0.9361 | 0.9222 | 0.9291 | 0.9243 |
| 0.2633 | 4.85 | 1300 | 0.2152 | 0.9320 | 0.9185 | 0.9252 | 0.9208 |
| 0.2633 | 5.22 | 1400 | 0.2068 | 0.9333 | 0.9227 | 0.9280 | 0.9239 |
| 0.2218 | 5.6 | 1500 | 0.2070 | 0.9360 | 0.9256 | 0.9308 | 0.9267 |
| 0.2218 | 5.97 | 1600 | 0.2045 | 0.9394 | 0.9282 | 0.9338 | 0.9296 |
| 0.2218 | 6.34 | 1700 | 0.2020 | 0.9357 | 0.9275 | 0.9316 | 0.9284 |
| 0.2218 | 6.72 | 1800 | 0.2054 | 0.9354 | 0.9227 | 0.9290 | 0.9246 |
| 0.2218 | 7.09 | 1900 | 0.2053 | 0.9372 | 0.9253 | 0.9312 | 0.9269 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2
|
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "Clinical-AI-Apollo/Medical-NER", "model-index": [{"name": "Medical-NER-finetuned-ner", "results": []}]}
|
jaggernaut007/Medical-NER-finetuned-ner
| null |
[
"transformers",
"safetensors",
"deberta-v2",
"token-classification",
"generated_from_trainer",
"base_model:Clinical-AI-Apollo/Medical-NER",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-13T15:07:41+00:00
|
[] |
[] |
TAGS
#transformers #safetensors #deberta-v2 #token-classification #generated_from_trainer #base_model-Clinical-AI-Apollo/Medical-NER #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
Medical-NER-finetuned-ner
=========================
This model is a fine-tuned version of Clinical-AI-Apollo/Medical-NER on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2045
* Precision: 0.9394
* Recall: 0.9282
* F1: 0.9338
* Accuracy: 0.9296
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-06
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
### Training results
### Framework versions
* Transformers 4.39.3
* Pytorch 2.2.2+cu121
* Datasets 2.19.0
* Tokenizers 0.15.2
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-06\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\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 4.39.3\n* Pytorch 2.2.2+cu121\n* Datasets 2.19.0\n* Tokenizers 0.15.2"
] |
[
"TAGS\n#transformers #safetensors #deberta-v2 #token-classification #generated_from_trainer #base_model-Clinical-AI-Apollo/Medical-NER #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-06\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\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 4.39.3\n* Pytorch 2.2.2+cu121\n* Datasets 2.19.0\n* Tokenizers 0.15.2"
] |
text-generation
|
transformers
|
# Ognoexperiment27multi_verse_modelExperiment27pastiche-7B
Ognoexperiment27multi_verse_modelExperiment27pastiche-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration.
## 🧩 Configuration
```yaml
models:
- model: mistralai/Mistral-7B-v0.1
- model: automerger/Ognoexperiment27Multi_verse_model-7B
- model: automerger/Experiment27Pastiche-7B
merge_method: model_stock
base_model: mistralai/Mistral-7B-v0.1
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "automerger/Ognoexperiment27multi_verse_modelExperiment27pastiche-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", "automerger"]}
|
automerger/Ognoexperiment27multi_verse_modelExperiment27pastiche-7B
| null |
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"automerger",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-13T15:08:11+00:00
|
[] |
[] |
TAGS
#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #automerger #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Ognoexperiment27multi_verse_modelExperiment27pastiche-7B
Ognoexperiment27multi_verse_modelExperiment27pastiche-7B is an automated merge created by Maxime Labonne using the following configuration.
## Configuration
## Usage
|
[
"# Ognoexperiment27multi_verse_modelExperiment27pastiche-7B\n\nOgnoexperiment27multi_verse_modelExperiment27pastiche-7B is an automated merge created by Maxime Labonne using the following configuration.",
"## Configuration",
"## Usage"
] |
[
"TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #automerger #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Ognoexperiment27multi_verse_modelExperiment27pastiche-7B\n\nOgnoexperiment27multi_verse_modelExperiment27pastiche-7B is an automated merge created by Maxime Labonne using the following configuration.",
"## Configuration",
"## Usage"
] |
null |
adapter-transformers
|
# Adapter `BigTMiami/AA_adp_seq_bn_P_micro_seq_bn_C_micro` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [BigTMiami/amazon_helpfulness](https://huggingface.co/datasets/BigTMiami/amazon_helpfulness/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("roberta-base")
adapter_name = model.load_adapter("BigTMiami/AA_adp_seq_bn_P_micro_seq_bn_C_micro", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
{"tags": ["roberta", "adapter-transformers"], "datasets": ["BigTMiami/amazon_helpfulness"]}
|
BigTMiami/AA_adp_seq_bn_P_micro_seq_bn_C_micro
| null |
[
"adapter-transformers",
"roberta",
"dataset:BigTMiami/amazon_helpfulness",
"region:us"
] | null |
2024-04-13T15:09:13+00:00
|
[] |
[] |
TAGS
#adapter-transformers #roberta #dataset-BigTMiami/amazon_helpfulness #region-us
|
# Adapter 'BigTMiami/AA_adp_seq_bn_P_micro_seq_bn_C_micro' for roberta-base
An adapter for the 'roberta-base' model that was trained on the BigTMiami/amazon_helpfulness dataset and includes a prediction head for classification.
This adapter was created for usage with the Adapters library.
## Usage
First, install 'adapters':
Now, the adapter can be loaded and activated like this:
## Architecture & Training
## Evaluation results
|
[
"# Adapter 'BigTMiami/AA_adp_seq_bn_P_micro_seq_bn_C_micro' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/amazon_helpfulness dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] |
[
"TAGS\n#adapter-transformers #roberta #dataset-BigTMiami/amazon_helpfulness #region-us \n",
"# Adapter 'BigTMiami/AA_adp_seq_bn_P_micro_seq_bn_C_micro' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/amazon_helpfulness dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] |
null |
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: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
|
{"library_name": "peft"}
|
NBA55/Final_llama2-7B-learning_rate_schedular_cosine
| null |
[
"peft",
"region:us"
] | null |
2024-04-13T15:14:22+00:00
|
[] |
[] |
TAGS
#peft #region-us
|
## 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: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
|
[
"## 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: False\n- bnb_4bit_compute_dtype: float16",
"### Framework versions\n\n\n- PEFT 0.4.0"
] |
[
"TAGS\n#peft #region-us \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: False\n- bnb_4bit_compute_dtype: float16",
"### Framework versions\n\n\n- PEFT 0.4.0"
] |
text-generation
|
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
{"library_name": "transformers", "tags": []}
|
heyllm234/sc30
| null |
[
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-13T15:15:11+00:00
|
[
"1910.09700"
] |
[] |
TAGS
#transformers #safetensors #stablelm #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #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 #stablelm #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #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"
] |
null |
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
{"library_name": "transformers", "tags": []}
|
KaggleMasterX/mistral_dpo
| null |
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null |
2024-04-13T15:15:47+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",
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"### 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.",
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"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
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"### 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"
] |
null |
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
{"library_name": "transformers", "tags": []}
|
KaggleMasterX/mistral_dpo_tok
| null |
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null |
2024-04-13T15:16:00+00:00
|
[
"1910.09700"
] |
[] |
TAGS
#transformers #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:",
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"### 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 #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]",
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"### 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"
] |
null |
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: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
|
{"library_name": "peft"}
|
NBA55/Final_llama2-7B-learning_rate_schedular_linear
| null |
[
"peft",
"region:us"
] | null |
2024-04-13T15:17:52+00:00
|
[] |
[] |
TAGS
#peft #region-us
|
## 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: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
|
[
"## 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: False\n- bnb_4bit_compute_dtype: float16",
"### Framework versions\n\n\n- PEFT 0.4.0"
] |
[
"TAGS\n#peft #region-us \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: False\n- bnb_4bit_compute_dtype: float16",
"### Framework versions\n\n\n- PEFT 0.4.0"
] |
text2text-generation
|
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. -->
# pegasus-ft
This model is a fine-tuned version of [haesun/pegasus-samsum](https://huggingface.co/haesun/pegasus-samsum) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9779
## 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: 2
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.1074 | 0.19 | 100 | 2.0692 |
| 1.9296 | 0.37 | 200 | 2.0481 |
| 1.9456 | 0.56 | 300 | 2.0364 |
| 1.9674 | 0.74 | 400 | 2.0205 |
| 2.0887 | 0.93 | 500 | 2.0043 |
| 2.0733 | 1.11 | 600 | 2.0023 |
| 1.9111 | 1.3 | 700 | 1.9991 |
| 1.887 | 1.48 | 800 | 1.9917 |
| 2.0347 | 1.67 | 900 | 1.9855 |
| 1.8488 | 1.85 | 1000 | 1.9838 |
| 1.7699 | 2.04 | 1100 | 1.9800 |
| 1.7386 | 2.22 | 1200 | 1.9795 |
| 1.8828 | 2.41 | 1300 | 1.9804 |
| 1.8072 | 2.59 | 1400 | 1.9808 |
| 1.898 | 2.78 | 1500 | 1.9785 |
| 1.9452 | 2.96 | 1600 | 1.9779 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"tags": ["generated_from_trainer"], "base_model": "haesun/pegasus-samsum", "model-index": [{"name": "pegasus-ft", "results": []}]}
|
knlp/pegasus-ft
| null |
[
"transformers",
"tensorboard",
"safetensors",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"base_model:haesun/pegasus-samsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-13T15:19:22+00:00
|
[] |
[] |
TAGS
#transformers #tensorboard #safetensors #pegasus #text2text-generation #generated_from_trainer #base_model-haesun/pegasus-samsum #autotrain_compatible #endpoints_compatible #region-us
|
pegasus-ft
==========
This model is a fine-tuned version of haesun/pegasus-samsum on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9779
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: 2
* eval\_batch\_size: 1
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 100
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.38.2
* Pytorch 2.2.1+cu121
* Datasets 2.18.0
* Tokenizers 0.15.2
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 100\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.2.1+cu121\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] |
[
"TAGS\n#transformers #tensorboard #safetensors #pegasus #text2text-generation #generated_from_trainer #base_model-haesun/pegasus-samsum #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: 2\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 100\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.2.1+cu121\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] |
fill-mask
|
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. -->
# 7_epoch_edu_model-finetuned-fintech
This model was trained from scratch on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 4.8162
## 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: 64
- eval_batch_size: 64
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 5.1051 | 1.0 | 2069 | 4.8668 |
| 5.0208 | 2.0 | 4138 | 4.8162 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"tags": ["generated_from_trainer"], "datasets": ["generator"], "model-index": [{"name": "7_epoch_edu_model-finetuned-fintech", "results": []}]}
|
Pastushoc/7_epoch_edu_model-finetuned-fintech
| null |
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"fill-mask",
"generated_from_trainer",
"dataset:generator",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-13T15:24:00+00:00
|
[] |
[] |
TAGS
#transformers #tensorboard #safetensors #bert #fill-mask #generated_from_trainer #dataset-generator #autotrain_compatible #endpoints_compatible #region-us
|
7\_epoch\_edu\_model-finetuned-fintech
======================================
This model was trained from scratch on the generator dataset.
It achieves the following results on the evaluation set:
* Loss: 4.8162
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: 64
* eval\_batch\_size: 64
* 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 4.38.2
* Pytorch 2.2.1+cu121
* Datasets 2.18.0
* Tokenizers 0.15.2
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\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 4.38.2\n* Pytorch 2.2.1+cu121\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] |
[
"TAGS\n#transformers #tensorboard #safetensors #bert #fill-mask #generated_from_trainer #dataset-generator #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: 64\n* eval\\_batch\\_size: 64\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 4.38.2\n* Pytorch 2.2.1+cu121\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] |
image-classification
|
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. -->
# Psoriasis-Aug-M2-vit-large-patch16-224-in21k
This model is a fine-tuned version of [google/vit-large-patch16-224-in21k](https://huggingface.co/google/vit-large-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0268
- Accuracy: 0.9792
## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4421 | 0.99 | 36 | 0.2504 | 0.8958 |
| 0.0968 | 1.99 | 72 | 0.0631 | 0.9583 |
| 0.0321 | 2.98 | 108 | 0.0639 | 0.9792 |
| 0.0065 | 4.0 | 145 | 0.0234 | 1.0 |
| 0.0067 | 4.97 | 180 | 0.0268 | 0.9792 |
### Test results
| Classes | precision | recall | f1-score | support|
|:-------------------:|:---------:|:------:|:--------:|:------:|
| Erythromelal | 1.00 | 1.00 | 1.00 | 5 |
| Guttate | 1.00 | 1.00 | 1.00 | 7 |
| Inverse | 1.00 | 1.00 | 1.00 | 4 |
| Nail | 1.00 | 1.00 | 1.00 | 10 |
| Normal | 1.00 | 1.00 | 1.00 | 11 |
| Plaque | 1.00 | 1.00 | 1.00 | 10 |
| Psoriatic Arthritis | 1.00 | 1.00 | 1.00 | 6 |
| Pustular | 1.00 | 1.00 | 1.00 | 6 |
| | | | | |
| accuracy | | | 1.00 | 59|
| macro avg | 1.00 | 1.00 | 1.00 | 59 |
| weighted avg | 1.00 | 1.00 | 1.00 | 59 |
### confusion Matrix results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "google/vit-large-patch16-224-in21k", "model-index": [{"name": "Psoriasis-Aug-M2-vit-large-patch16-224-in21k", "results": []}]}
|
ahmedesmail16/Psoriasis-Aug-M2-vit-large-patch16-224-in21k
| null |
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"base_model:google/vit-large-patch16-224-in21k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-13T15:26:02+00:00
|
[] |
[] |
TAGS
#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #base_model-google/vit-large-patch16-224-in21k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
Psoriasis-Aug-M2-vit-large-patch16-224-in21k
============================================
This model is a fine-tuned version of google/vit-large-patch16-224-in21k on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0268
* Accuracy: 0.9792
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
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 5
### Training results
### Test results
### confusion Matrix results
!image/png
### Framework versions
* Transformers 4.39.3
* Pytorch 2.1.2
* Datasets 2.18.0
* Tokenizers 0.15.2
|
[
"### 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* gradient\\_accumulation\\_steps: 4\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\\_ratio: 0.1\n* num\\_epochs: 5",
"### Training results",
"### Test results",
"### confusion Matrix results\n\n\n!image/png",
"### Framework versions\n\n\n* Transformers 4.39.3\n* Pytorch 2.1.2\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] |
[
"TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #base_model-google/vit-large-patch16-224-in21k #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* gradient\\_accumulation\\_steps: 4\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\\_ratio: 0.1\n* num\\_epochs: 5",
"### Training results",
"### Test results",
"### confusion Matrix results\n\n\n!image/png",
"### Framework versions\n\n\n* Transformers 4.39.3\n* Pytorch 2.1.2\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] |
text-generation
|
transformers
|
# Paradigm
This is a 8bpw exl2 quant of the Paradigm 7B model.
ChatML or Alpaca instruct sequences both work.
----
An incredibly effective and intelligent RP model designed to be the best bot you've ever used. I hope you like it!
GGUF available here: https://huggingface.co/Lewdiculous/Paradigm_7B-GGUF-IQ-Imatrix
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_ResplendentAI__Paradigm_7B)
| Metric |Value|
|---------------------------------|----:|
|Avg. |75.47|
|AI2 Reasoning Challenge (25-Shot)|73.63|
|HellaSwag (10-Shot) |88.66|
|MMLU (5-Shot) |64.02|
|TruthfulQA (0-shot) |75.19|
|Winogrande (5-shot) |84.53|
|GSM8k (5-shot) |66.79|
### Configuration
The following YAML configuration was used to produce this model:
```yaml
merge_method: dare_ties
base_model: ChaoticNeutrals/Eris_Remix_7B
parameters:
normalize: true
models:
- model: ChaoticNeutrals/Eris_Remix_7B
parameters:
weight: 1
- model: ResplendentAI/Datura_7B
parameters:
weight: 1
- model: liminerity/Multiverse-Experiment-slerp-7b+jeiku/Alpaca_NSFW_Shuffled_Mistral
parameters:
weight: 0.33
dtype: float16
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_ResplendentAI__Paradigm_7B)
| Metric |Value|
|---------------------------------|----:|
|Avg. |75.47|
|AI2 Reasoning Challenge (25-Shot)|73.63|
|HellaSwag (10-Shot) |88.66|
|MMLU (5-Shot) |64.02|
|TruthfulQA (0-shot) |75.19|
|Winogrande (5-shot) |84.53|
|GSM8k (5-shot) |66.79|
|
{"language": ["en"], "license": "cc-by-sa-4.0", "library_name": "transformers", "tags": ["mergekit", "merge"], "datasets": ["ResplendentAI/Alpaca_NSFW_Shuffled", "unalignment/toxic-dpo-v0.2"], "base_model": ["liminerity/Multiverse-Experiment-slerp-7b", "jeiku/Alpaca_NSFW_Shuffled_Mistral", "ResplendentAI/Datura_7B", "ChaoticNeutrals/Eris_Remix_7B"], "model-index": [{"name": "Paradigm_7B", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "AI2 Reasoning Challenge (25-Shot)", "type": "ai2_arc", "config": "ARC-Challenge", "split": "test", "args": {"num_few_shot": 25}}, "metrics": [{"type": "acc_norm", "value": 73.63, "name": "normalized accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ResplendentAI/Paradigm_7B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "HellaSwag (10-Shot)", "type": "hellaswag", "split": "validation", "args": {"num_few_shot": 10}}, "metrics": [{"type": "acc_norm", "value": 88.66, "name": "normalized accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ResplendentAI/Paradigm_7B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "MMLU (5-Shot)", "type": "cais/mmlu", "config": "all", "split": "test", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 64.02, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ResplendentAI/Paradigm_7B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "TruthfulQA (0-shot)", "type": "truthful_qa", "config": "multiple_choice", "split": "validation", "args": {"num_few_shot": 0}}, "metrics": [{"type": "mc2", "value": 75.19}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ResplendentAI/Paradigm_7B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "Winogrande (5-shot)", "type": "winogrande", "config": "winogrande_xl", "split": "validation", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 84.53, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ResplendentAI/Paradigm_7B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "GSM8k (5-shot)", "type": "gsm8k", "config": "main", "split": "test", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 66.79, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ResplendentAI/Paradigm_7B", "name": "Open LLM Leaderboard"}}]}]}
|
RossAscends/Paradigm_7B_6bpw_exl2
| null |
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"en",
"dataset:ResplendentAI/Alpaca_NSFW_Shuffled",
"dataset:unalignment/toxic-dpo-v0.2",
"base_model:liminerity/Multiverse-Experiment-slerp-7b",
"base_model:jeiku/Alpaca_NSFW_Shuffled_Mistral",
"base_model:ResplendentAI/Datura_7B",
"base_model:ChaoticNeutrals/Eris_Remix_7B",
"license:cc-by-sa-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-13T15:27:13+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #safetensors #mistral #text-generation #mergekit #merge #en #dataset-ResplendentAI/Alpaca_NSFW_Shuffled #dataset-unalignment/toxic-dpo-v0.2 #base_model-liminerity/Multiverse-Experiment-slerp-7b #base_model-jeiku/Alpaca_NSFW_Shuffled_Mistral #base_model-ResplendentAI/Datura_7B #base_model-ChaoticNeutrals/Eris_Remix_7B #license-cc-by-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Paradigm
========
This is a 8bpw exl2 quant of the Paradigm 7B model.
ChatML or Alpaca instruct sequences both work.
---
!image/jpeg
An incredibly effective and intelligent RP model designed to be the best bot you've ever used. I hope you like it!
GGUF available here: URL
Open LLM Leaderboard Evaluation Results
=======================================
Detailed results can be found here
### Configuration
The following YAML configuration was used to produce this model:
Open LLM Leaderboard Evaluation Results
=======================================
Detailed results can be found here
|
[
"### Configuration\n\n\nThe following YAML configuration was used to produce this model:\n\n\nOpen LLM Leaderboard Evaluation Results\n=======================================\n\n\nDetailed results can be found here"
] |
[
"TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #en #dataset-ResplendentAI/Alpaca_NSFW_Shuffled #dataset-unalignment/toxic-dpo-v0.2 #base_model-liminerity/Multiverse-Experiment-slerp-7b #base_model-jeiku/Alpaca_NSFW_Shuffled_Mistral #base_model-ResplendentAI/Datura_7B #base_model-ChaoticNeutrals/Eris_Remix_7B #license-cc-by-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Configuration\n\n\nThe following YAML configuration was used to produce this model:\n\n\nOpen LLM Leaderboard Evaluation Results\n=======================================\n\n\nDetailed results can be found here"
] |
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. -->
# patent-instruct-finetune-model-ner-stablelm
This model is a fine-tuned version of [stabilityai/stablelm-2-1_6b](https://huggingface.co/stabilityai/stablelm-2-1_6b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7565
## 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
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.7503 | 1.0 | 2043 | 1.7837 |
| 1.7292 | 2.0 | 4086 | 1.7616 |
| 1.7212 | 3.0 | 6129 | 1.7565 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "other", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "stabilityai/stablelm-2-1_6b", "model-index": [{"name": "patent-instruct-finetune-model-ner-stablelm", "results": []}]}
|
shubhamgantayat/patent-instruct-finetune-model-ner-stablelm
| null |
[
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:stabilityai/stablelm-2-1_6b",
"license:other",
"region:us"
] | null |
2024-04-13T15:27:31+00:00
|
[] |
[] |
TAGS
#peft #tensorboard #safetensors #generated_from_trainer #base_model-stabilityai/stablelm-2-1_6b #license-other #region-us
|
patent-instruct-finetune-model-ner-stablelm
===========================================
This model is a fine-tuned version of stabilityai/stablelm-2-1\_6b on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7565
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
* num\_epochs: 3
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* PEFT 0.10.0
* Transformers 4.38.2
* Pytorch 2.2.1+cu121
* Datasets 2.18.0
* Tokenizers 0.15.2
|
[
"### 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* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.10.0\n* Transformers 4.38.2\n* Pytorch 2.2.1+cu121\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] |
[
"TAGS\n#peft #tensorboard #safetensors #generated_from_trainer #base_model-stabilityai/stablelm-2-1_6b #license-other #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* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.10.0\n* Transformers 4.38.2\n* Pytorch 2.2.1+cu121\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] |
token-classification
|
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. -->
# NeRUBioS_xlm_RoBERTa_base_Training_Development
This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3324
- Negref Precision: 0.5672
- Negref Recall: 0.5696
- Negref F1: 0.5684
- Neg Precision: 0.9480
- Neg Recall: 0.9760
- Neg F1: 0.9618
- Nsco Precision: 0.8685
- Nsco Recall: 0.9097
- Nsco F1: 0.8886
- Unc Precision: 0.8419
- Unc Recall: 0.8842
- Unc F1: 0.8625
- Usco Precision: 0.6429
- Usco Recall: 0.7383
- Usco F1: 0.6873
- Precision: 0.8190
- Recall: 0.8548
- F1: 0.8365
- Accuracy: 0.9520
## 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: 12
### Training results
| Training Loss | Epoch | Step | Validation Loss | Negref Precision | Negref Recall | Negref F1 | Neg Precision | Neg Recall | Neg F1 | Nsco Precision | Nsco Recall | Nsco F1 | Unc Precision | Unc Recall | Unc F1 | Usco Precision | Usco Recall | Usco F1 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------:|:-------------:|:----------:|:------:|:--------------:|:-----------:|:-------:|:-------------:|:----------:|:------:|:--------------:|:-----------:|:-------:|:---------:|:------:|:------:|:--------:|
| 0.2316 | 1.0 | 1729 | 0.2023 | 0.4328 | 0.4958 | 0.4621 | 0.8759 | 0.9629 | 0.9173 | 0.7259 | 0.8397 | 0.7786 | 0.7059 | 0.8340 | 0.7646 | 0.4408 | 0.6836 | 0.5360 | 0.6864 | 0.8063 | 0.7415 | 0.9364 |
| 0.1596 | 2.0 | 3458 | 0.1756 | 0.4771 | 0.5274 | 0.5010 | 0.9252 | 0.9727 | 0.9484 | 0.8113 | 0.8836 | 0.8459 | 0.8036 | 0.8687 | 0.8349 | 0.5615 | 0.6953 | 0.6213 | 0.7624 | 0.8329 | 0.7961 | 0.9480 |
| 0.1197 | 3.0 | 5187 | 0.1735 | 0.5214 | 0.5401 | 0.5306 | 0.9436 | 0.9672 | 0.9553 | 0.8449 | 0.8800 | 0.8621 | 0.8094 | 0.8687 | 0.8380 | 0.5705 | 0.6953 | 0.6268 | 0.7891 | 0.8322 | 0.8101 | 0.9510 |
| 0.1006 | 4.0 | 6916 | 0.2003 | 0.5324 | 0.5717 | 0.5514 | 0.9365 | 0.9814 | 0.9584 | 0.8510 | 0.8955 | 0.8727 | 0.7965 | 0.8764 | 0.8346 | 0.5755 | 0.7148 | 0.6376 | 0.7890 | 0.8497 | 0.8182 | 0.9508 |
| 0.0706 | 5.0 | 8645 | 0.2077 | 0.5434 | 0.5675 | 0.5552 | 0.9497 | 0.9683 | 0.9589 | 0.8821 | 0.9062 | 0.8940 | 0.8285 | 0.8764 | 0.8518 | 0.6013 | 0.7188 | 0.6548 | 0.8107 | 0.8482 | 0.8290 | 0.9531 |
| 0.0514 | 6.0 | 10374 | 0.2554 | 0.5282 | 0.5527 | 0.5402 | 0.9281 | 0.9716 | 0.9493 | 0.8476 | 0.9050 | 0.8754 | 0.8433 | 0.8726 | 0.8577 | 0.6131 | 0.7305 | 0.6667 | 0.7950 | 0.8471 | 0.8202 | 0.9496 |
| 0.039 | 7.0 | 12103 | 0.2547 | 0.5306 | 0.5675 | 0.5484 | 0.9508 | 0.9705 | 0.9606 | 0.8672 | 0.9074 | 0.8868 | 0.8582 | 0.9112 | 0.8839 | 0.6609 | 0.7461 | 0.7009 | 0.8136 | 0.8551 | 0.8339 | 0.9525 |
| 0.0273 | 8.0 | 13832 | 0.2796 | 0.5447 | 0.5401 | 0.5424 | 0.9459 | 0.9738 | 0.9597 | 0.8615 | 0.9086 | 0.8844 | 0.8088 | 0.8494 | 0.8286 | 0.6575 | 0.75 | 0.7007 | 0.8115 | 0.8464 | 0.8286 | 0.9497 |
| 0.0214 | 9.0 | 15561 | 0.3079 | 0.5429 | 0.5738 | 0.5579 | 0.9391 | 0.9771 | 0.9577 | 0.8707 | 0.9121 | 0.8910 | 0.8448 | 0.9035 | 0.8731 | 0.6084 | 0.7344 | 0.6655 | 0.8066 | 0.8580 | 0.8315 | 0.9514 |
| 0.0136 | 10.0 | 17290 | 0.3172 | 0.5524 | 0.5781 | 0.5649 | 0.9459 | 0.9738 | 0.9597 | 0.8848 | 0.9121 | 0.8982 | 0.8476 | 0.8803 | 0.8636 | 0.6519 | 0.7461 | 0.6958 | 0.8201 | 0.8566 | 0.8380 | 0.9520 |
| 0.0098 | 11.0 | 19019 | 0.3312 | 0.5729 | 0.5717 | 0.5723 | 0.9501 | 0.9771 | 0.9634 | 0.8753 | 0.9086 | 0.8916 | 0.8476 | 0.8803 | 0.8636 | 0.6574 | 0.7422 | 0.6972 | 0.8251 | 0.8551 | 0.8398 | 0.9516 |
| 0.008 | 12.0 | 20748 | 0.3324 | 0.5672 | 0.5696 | 0.5684 | 0.9480 | 0.9760 | 0.9618 | 0.8685 | 0.9097 | 0.8886 | 0.8419 | 0.8842 | 0.8625 | 0.6429 | 0.7383 | 0.6873 | 0.8190 | 0.8548 | 0.8365 | 0.9520 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "FacebookAI/xlm-roberta-base", "model-index": [{"name": "NeRUBioS_xlm_RoBERTa_base_Training_Development", "results": []}]}
|
ajtamayoh/NeRUBioS_xlm_RoBERTa_base_Training_Development
| null |
[
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-13T15:28:32+00:00
|
[] |
[] |
TAGS
#transformers #tensorboard #safetensors #xlm-roberta #token-classification #generated_from_trainer #base_model-FacebookAI/xlm-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
NeRUBioS\_xlm\_RoBERTa\_base\_Training\_Development
===================================================
This model is a fine-tuned version of FacebookAI/xlm-roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3324
* Negref Precision: 0.5672
* Negref Recall: 0.5696
* Negref F1: 0.5684
* Neg Precision: 0.9480
* Neg Recall: 0.9760
* Neg F1: 0.9618
* Nsco Precision: 0.8685
* Nsco Recall: 0.9097
* Nsco F1: 0.8886
* Unc Precision: 0.8419
* Unc Recall: 0.8842
* Unc F1: 0.8625
* Usco Precision: 0.6429
* Usco Recall: 0.7383
* Usco F1: 0.6873
* Precision: 0.8190
* Recall: 0.8548
* F1: 0.8365
* Accuracy: 0.9520
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: 12
### Training results
### Framework versions
* Transformers 4.38.2
* Pytorch 2.2.1+cu121
* Datasets 2.18.0
* Tokenizers 0.15.2
|
[
"### 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: 12",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.2.1+cu121\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] |
[
"TAGS\n#transformers #tensorboard #safetensors #xlm-roberta #token-classification #generated_from_trainer #base_model-FacebookAI/xlm-roberta-base #license-mit #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: 12",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.2.1+cu121\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] |
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