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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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<!-- 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
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
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<!-- 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. -->
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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[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]
#### 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]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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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. -->
**BibTeX:**
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| {"library_name": "transformers", "tags": []} | swj0419/email_retrain_STEP0000006 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-16T05:29:22+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
| [
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automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small Cebuano - Sanchit Gandhi
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 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: 1e-05
- train_batch_size: 16
- 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: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"language": ["hi"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["ahoka/ceb-eng"], "base_model": "openai/whisper-small", "model-index": [{"name": "Whisper Small Cebuano - Sanchit Gandhi", "results": []}]} | ahoka/whisper-small-eng | null | [
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"automatic-speech-recognition",
"generated_from_trainer",
"hi",
"dataset:ahoka/ceb-eng",
"base_model:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T05:30:46+00:00 | [] | [
"hi"
] | TAGS
#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #hi #dataset-ahoka/ceb-eng #base_model-openai/whisper-small #license-apache-2.0 #endpoints_compatible #region-us
|
# Whisper Small Cebuano - Sanchit Gandhi
This model is a fine-tuned version of openai/whisper-small on the Common Voice 11.0 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: 1e-05
- train_batch_size: 16
- 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: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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] |
token-classification | transformers |
## Model Specification
- Model: XLM-RoBERTa (base-sized model)
- Training Data:
- Combined Afrikaans, Hebrew, & Bulgarian corpora (Top 3 Languages)
- Training Details:
- Base configurations with a minor adjustment in learning rate (4.5e-5)
## Evaluation
- Evaluation Dataset: Universal Dependencies Tagalog Ugnayan (Testing Set)
- Tested in a zero-shot cross-lingual scenario on a Universal Dependencies Tagalog Ugnayan testing dataset (with 76.81\% Accuracy)
## POS Tags
- ADJ – ADP – ADV – CCONJ – DET – INTJ – NOUN – NUM – PART – PRON – PROPN – PUNCT – SCONJ – VERB | {"language": ["tl"], "datasets": ["universal_dependencies"], "metrics": ["f1"], "pipeline_tag": "token-classification"} | iceman2434/xlm-roberta-base-ft-udpos213-top3lang | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"tl",
"dataset:universal_dependencies",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T05:32:31+00:00 | [] | [
"tl"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #tl #dataset-universal_dependencies #autotrain_compatible #endpoints_compatible #region-us
|
## Model Specification
- Model: XLM-RoBERTa (base-sized model)
- Training Data:
- Combined Afrikaans, Hebrew, & Bulgarian corpora (Top 3 Languages)
- Training Details:
- Base configurations with a minor adjustment in learning rate (4.5e-5)
## Evaluation
- Evaluation Dataset: Universal Dependencies Tagalog Ugnayan (Testing Set)
- Tested in a zero-shot cross-lingual scenario on a Universal Dependencies Tagalog Ugnayan testing dataset (with 76.81\% Accuracy)
## POS Tags
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] |
text-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. -->
# gpt2-finetuned-wikitext2
This model is a fine-tuned version of [openai-community/gpt2](https://huggingface.co/openai-community/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3926
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.4831 | 1.0 | 2334 | 3.4096 |
| 3.3661 | 2.0 | 4668 | 3.3944 |
| 3.3123 | 3.0 | 7002 | 3.3926 |
### 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"], "base_model": "openai-community/gpt2", "model-index": [{"name": "gpt2-finetuned-wikitext2", "results": []}]} | Anwesh0127/gpt2-finetuned-wikitext2 | null | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:openai-community/gpt2",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-16T05:32:41+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-openai-community/gpt2 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gpt2-finetuned-wikitext2
========================
This model is a fine-tuned version of openai-community/gpt2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 3.3926
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0
### Training results
### Framework versions
* Transformers 4.38.2
* Pytorch 2.2.1+cu121
* Datasets 2.18.0
* Tokenizers 0.15.2
| [
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] |
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": []} | swj0419/bbc_retrain_STEP0000060 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-16T05:35:00+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]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
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"## Training Details",
"### Training Data",
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"## Technical Specifications [optional]",
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"## Glossary [optional]",
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"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
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"## 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",
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"## Training Details",
"### Training Data",
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"#### Testing Data",
"#### Factors",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
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"## 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. -->
# NHS-bert-binary-random
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5693
- Accuracy: 0.8050
- Precision: 0.7984
- Recall: 0.8048
- F1: 0.8006
## 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.0554 | 1.0 | 397 | 0.4393 | 0.8120 | 0.8050 | 0.8082 | 0.8064 |
| 0.087 | 2.0 | 794 | 0.4810 | 0.7729 | 0.7804 | 0.7890 | 0.7721 |
| 2.1969 | 3.0 | 1191 | 0.5693 | 0.8050 | 0.7984 | 0.8048 | 0.8006 |
### 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", "precision", "recall", "f1"], "base_model": "bert-base-uncased", "model-index": [{"name": "NHS-bert-binary-random", "results": []}]} | intermezzo672/NHS-bert-binary-random | null | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T05:35:09+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| NHS-bert-binary-random
======================
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5693
* Accuracy: 0.8050
* Precision: 0.7984
* Recall: 0.8048
* F1: 0.8006
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: 3e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 6
### Training results
### Framework versions
* Transformers 4.38.2
* Pytorch 2.2.1+cu121
* Datasets 2.18.0
* Tokenizers 0.15.2
| [
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"### 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 |
# Uploaded model
- **Developed by:** codesagar
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "mistral", "trl"], "base_model": "unsloth/mistral-7b-bnb-4bit"} | codesagar/prompt-guard-classification-v2 | null | [
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"trl",
"en",
"base_model:unsloth/mistral-7b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T05:35:45+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #text-generation-inference #unsloth #mistral #trl #en #base_model-unsloth/mistral-7b-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us
|
# Uploaded model
- Developed by: codesagar
- License: apache-2.0
- Finetuned from model : unsloth/mistral-7b-bnb-4bit
This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.
<img src="URL width="200"/>
| [
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] | [
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] |
sentence-similarity | sentence-transformers |
# TongZh/distilroberta-base-NLI
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('TongZh/distilroberta-base-NLI')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('TongZh/distilroberta-base-NLI')
model = AutoModel.from_pretrained('TongZh/distilroberta-base-NLI')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=TongZh/distilroberta-base-NLI)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 34336 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.SoftmaxLoss.SoftmaxLoss`
Parameters of the fit()-Method:
```
{
"epochs": 4,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 100,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | {"library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | TongZh/distilroberta-base-NLI | null | [
"sentence-transformers",
"safetensors",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T05:36:03+00:00 | [] | [] | TAGS
#sentence-transformers #safetensors #roberta #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# TongZh/distilroberta-base-NLI
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can use the model like this:
## Usage (HuggingFace Transformers)
Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL
## Training
The model was trained with the parameters:
DataLoader:
'URL.dataloader.DataLoader' of length 34336 with parameters:
Loss:
'sentence_transformers.losses.SoftmaxLoss.SoftmaxLoss'
Parameters of the fit()-Method:
## Full Model Architecture
## Citing & Authors
| [
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"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.",
"## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL",
"## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 34336 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.SoftmaxLoss.SoftmaxLoss' \n\nParameters of the fit()-Method:",
"## Full Model Architecture",
"## Citing & Authors"
] | [
"TAGS\n#sentence-transformers #safetensors #roberta #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# TongZh/distilroberta-base-NLI\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.",
"## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL",
"## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 34336 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.SoftmaxLoss.SoftmaxLoss' \n\nParameters of the fit()-Method:",
"## Full Model Architecture",
"## Citing & Authors"
] |
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": []} | swj0419/bbc_STEP0000120 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-16T05:36:39+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]:",
"## 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",
"### 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 #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.",
"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
null | transformers |
# Uploaded model
- **Developed by:** codesagar
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "mistral", "trl"], "base_model": "unsloth/mistral-7b-bnb-4bit"} | codesagar/prompt-guard-reasoning-v2 | null | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:unsloth/mistral-7b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T05:36:46+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #text-generation-inference #unsloth #mistral #trl #en #base_model-unsloth/mistral-7b-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us
|
# Uploaded model
- Developed by: codesagar
- License: apache-2.0
- Finetuned from model : unsloth/mistral-7b-bnb-4bit
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: codesagar\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-bnb-4bit\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 #text-generation-inference #unsloth #mistral #trl #en #base_model-unsloth/mistral-7b-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Uploaded model\n\n- Developed by: codesagar\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-bnb-4bit\n\nThis mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] |
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. -->
# model_hh_shp3_dpo5
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 an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2982
- Rewards/chosen: -7.3754
- Rewards/rejected: -9.0651
- Rewards/accuracies: 0.5900
- Rewards/margins: 1.6897
- Logps/rejected: -254.8305
- Logps/chosen: -235.6870
- Logits/rejected: -1.0713
- Logits/chosen: -1.0440
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.0495 | 2.67 | 100 | 1.4386 | 0.3553 | -0.0876 | 0.5400 | 0.4429 | -236.8756 | -220.2257 | -0.5196 | -0.4726 |
| 0.0246 | 5.33 | 200 | 2.9978 | -3.8865 | -4.5338 | 0.5400 | 0.6473 | -245.7680 | -228.7092 | -0.6969 | -0.6601 |
| 0.0049 | 8.0 | 300 | 3.2131 | -5.2339 | -6.8669 | 0.5700 | 1.6330 | -250.4342 | -231.4039 | -0.9891 | -0.9685 |
| 0.0 | 10.67 | 400 | 3.2941 | -7.3754 | -9.0927 | 0.5900 | 1.7173 | -254.8858 | -235.6871 | -1.0720 | -1.0447 |
| 0.0 | 13.33 | 500 | 3.3095 | -7.4032 | -9.1002 | 0.5800 | 1.6970 | -254.9008 | -235.7425 | -1.0717 | -1.0441 |
| 0.0 | 16.0 | 600 | 3.2770 | -7.3721 | -9.0946 | 0.5900 | 1.7225 | -254.8896 | -235.6804 | -1.0714 | -1.0442 |
| 0.0 | 18.67 | 700 | 3.2884 | -7.3870 | -9.1102 | 0.5900 | 1.7231 | -254.9207 | -235.7102 | -1.0715 | -1.0443 |
| 0.0 | 21.33 | 800 | 3.3005 | -7.3860 | -9.0763 | 0.5900 | 1.6902 | -254.8528 | -235.7083 | -1.0716 | -1.0442 |
| 0.0 | 24.0 | 900 | 3.2838 | -7.3850 | -9.0845 | 0.5900 | 1.6994 | -254.8693 | -235.7063 | -1.0716 | -1.0441 |
| 0.0 | 26.67 | 1000 | 3.2982 | -7.3754 | -9.0651 | 0.5900 | 1.6897 | -254.8305 | -235.6870 | -1.0713 | -1.0440 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.39.1
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["trl", "dpo", "generated_from_trainer"], "base_model": "meta-llama/Llama-2-7b-chat-hf", "model-index": [{"name": "model_hh_shp3_dpo5", "results": []}]} | guoyu-zhang/model_hh_shp3_dpo5 | null | [
"peft",
"safetensors",
"trl",
"dpo",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | null | 2024-04-16T05:37:10+00:00 | [] | [] | TAGS
#peft #safetensors #trl #dpo #generated_from_trainer #base_model-meta-llama/Llama-2-7b-chat-hf #region-us
| model\_hh\_shp3\_dpo5
=====================
This model is a fine-tuned version of meta-llama/Llama-2-7b-chat-hf on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 3.2982
* Rewards/chosen: -7.3754
* Rewards/rejected: -9.0651
* Rewards/accuracies: 0.5900
* Rewards/margins: 1.6897
* Logps/rejected: -254.8305
* Logps/chosen: -235.6870
* Logits/rejected: -1.0713
* Logits/chosen: -1.0440
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0005
* train\_batch\_size: 4
* eval\_batch\_size: 1
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 100
* training\_steps: 1000
### Training results
### Framework versions
* PEFT 0.10.0
* Transformers 4.39.1
* 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: 0.0005\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* training\\_steps: 1000",
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] |
text-generation | transformers |
# h2omistral-1.8b-dare1
h2omistral-1.8b-dare1 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [h2oai/h2o-danube2-1.8b-base](https://huggingface.co/h2oai/h2o-danube2-1.8b-base)
* [tokyotech-llm/Swallow-MS-7b-v0.1](https://huggingface.co/tokyotech-llm/Swallow-MS-7b-v0.1)
## 🧩 Configuration
```yaml
slices:
- sources:
- layer_range: [0, 24]
model: h2oai/h2o-danube2-1.8b-base
parameters:
density: [1, 0.7, 0.1]
weight: 1.0
- layer_range: [0, 24]
model: tokyotech-llm/Swallow-MS-7b-v0.1
parameters:
density: 0.33
weight:
- filter: mlp
value: 0.5
- value: 0
merge_method: dare_ties
base_model: h2oai/h2o-danube2-1.8b-base
parameters:
normalize: true
int8_mask: true
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "aipib/h2omistral-1.8b-dare1"
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"])
``` | {"tags": ["merge", "mergekit", "lazymergekit", "h2oai/h2o-danube2-1.8b-base", "tokyotech-llm/Swallow-MS-7b-v0.1"], "base_model": ["h2oai/h2o-danube2-1.8b-base", "tokyotech-llm/Swallow-MS-7b-v0.1"]} | aipib/h2omistral-1.8b-dare1 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"h2oai/h2o-danube2-1.8b-base",
"tokyotech-llm/Swallow-MS-7b-v0.1",
"base_model:h2oai/h2o-danube2-1.8b-base",
"base_model:tokyotech-llm/Swallow-MS-7b-v0.1",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-16T05:37:10+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #h2oai/h2o-danube2-1.8b-base #tokyotech-llm/Swallow-MS-7b-v0.1 #base_model-h2oai/h2o-danube2-1.8b-base #base_model-tokyotech-llm/Swallow-MS-7b-v0.1 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# h2omistral-1.8b-dare1
h2omistral-1.8b-dare1 is a merge of the following models using LazyMergekit:
* h2oai/h2o-danube2-1.8b-base
* tokyotech-llm/Swallow-MS-7b-v0.1
## Configuration
## Usage
| [
"# h2omistral-1.8b-dare1\n\nh2omistral-1.8b-dare1 is a merge of the following models using LazyMergekit:\n* h2oai/h2o-danube2-1.8b-base\n* tokyotech-llm/Swallow-MS-7b-v0.1",
"## Configuration",
"## Usage"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #h2oai/h2o-danube2-1.8b-base #tokyotech-llm/Swallow-MS-7b-v0.1 #base_model-h2oai/h2o-danube2-1.8b-base #base_model-tokyotech-llm/Swallow-MS-7b-v0.1 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# h2omistral-1.8b-dare1\n\nh2omistral-1.8b-dare1 is a merge of the following models using LazyMergekit:\n* h2oai/h2o-danube2-1.8b-base\n* tokyotech-llm/Swallow-MS-7b-v0.1",
"## Configuration",
"## Usage"
] |
text-generation | transformers | ## Model Details
**Model Developers** : Taeeon Park, Gihong Lee
**dataset** : dpo medical dataset (AI-hub dataset 활용 자체 제작)
**Training Method Method** : DPO.
**Company** : MoAData
## Usage
```
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
repo = "MoaData/Myrrh_solar_10.7b_2.0"
model = AutoModelForCausalLM.from_pretrained(
repo,
return_dict=True,
torch_dtype=torch.float16,
device_map='auto'
)
tokenizer = AutoTokenizer.from_pretrained(repo)
``` | {"language": ["ko"], "license": "apache-2.0"} | MoaData/Myrrh_solar_10.7b_2.0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"ko",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-16T05:37:26+00:00 | [] | [
"ko"
] | TAGS
#transformers #safetensors #llama #text-generation #ko #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ## Model Details
Model Developers : Taeeon Park, Gihong Lee
dataset : dpo medical dataset (AI-hub dataset 활용 자체 제작)
Training Method Method : DPO.
Company : MoAData
## Usage
| [
"## Model Details\n\nModel Developers : Taeeon Park, Gihong Lee\n\ndataset : dpo medical dataset (AI-hub dataset 활용 자체 제작)\n\nTraining Method Method : DPO.\n\nCompany : MoAData",
"## Usage"
] | [
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"## Model Details\n\nModel Developers : Taeeon Park, Gihong Lee\n\ndataset : dpo medical dataset (AI-hub dataset 활용 자체 제작)\n\nTraining Method Method : DPO.\n\nCompany : MoAData",
"## Usage"
] |
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]
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#### Speeds, Sizes, Times [optional]
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **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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| {"library_name": "transformers", "tags": ["trl", "sft"]} | dbaek111/Llama-2-7b-chat-hf-Elon_Interview_407-merged | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-04-16T05:37:27+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #trl #sft #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]:
- Shared by [optional]:
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- Language(s) (NLP):
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## Uses
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### Recommendations
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## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
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#### Speeds, Sizes, Times [optional]
## Evaluation
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
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- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
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"## 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. -->
# ruBert-base-sberquad-0.005-len_4-filtered
This model is a fine-tuned version of [ai-forever/ruBert-base](https://huggingface.co/ai-forever/ruBert-base) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 5000
### Training results
### Framework versions
- PEFT 0.10.1.dev0
- Transformers 4.40.0.dev0
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 | {"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "ai-forever/ruBert-base", "model-index": [{"name": "ruBert-base-sberquad-0.005-len_4-filtered", "results": []}]} | Shalazary/ruBert-base-sberquad-0.005-len_4-filtered | null | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:ai-forever/ruBert-base",
"license:apache-2.0",
"region:us"
] | null | 2024-04-16T05:37:34+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #generated_from_trainer #base_model-ai-forever/ruBert-base #license-apache-2.0 #region-us
|
# ruBert-base-sberquad-0.005-len_4-filtered
This model is a fine-tuned version of ai-forever/ruBert-base on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 5000
### Training results
### Framework versions
- PEFT 0.10.1.dev0
- Transformers 4.40.0.dev0
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 | [
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"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
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"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0005\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- training_steps: 5000",
"### Training results",
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] |
text-generation | transformers |
# Model Card for Model ID
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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<!-- 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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<!-- This should link to a Dataset Card if possible. -->
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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[More Information Needed]
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<!-- 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]
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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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[More Information Needed]
| {"library_name": "transformers", "tags": []} | swj0419/email_retrain_STEP0000008 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-16T05:38:09+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
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"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
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"## Technical Specifications [optional]",
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"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
null | transformers | ## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/Kukedlc/NeuralSOTA-7B-slerp
<!-- 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/NeuralSOTA-7B-slerp-GGUF/resolve/main/NeuralSOTA-7B-slerp.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/NeuralSOTA-7B-slerp-GGUF/resolve/main/NeuralSOTA-7B-slerp.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/NeuralSOTA-7B-slerp-GGUF/resolve/main/NeuralSOTA-7B-slerp.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/NeuralSOTA-7B-slerp-GGUF/resolve/main/NeuralSOTA-7B-slerp.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/NeuralSOTA-7B-slerp-GGUF/resolve/main/NeuralSOTA-7B-slerp.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/NeuralSOTA-7B-slerp-GGUF/resolve/main/NeuralSOTA-7B-slerp.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/NeuralSOTA-7B-slerp-GGUF/resolve/main/NeuralSOTA-7B-slerp.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/NeuralSOTA-7B-slerp-GGUF/resolve/main/NeuralSOTA-7B-slerp.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/NeuralSOTA-7B-slerp-GGUF/resolve/main/NeuralSOTA-7B-slerp.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/NeuralSOTA-7B-slerp-GGUF/resolve/main/NeuralSOTA-7B-slerp.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/NeuralSOTA-7B-slerp-GGUF/resolve/main/NeuralSOTA-7B-slerp.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/NeuralSOTA-7B-slerp-GGUF/resolve/main/NeuralSOTA-7B-slerp.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/NeuralSOTA-7B-slerp-GGUF/resolve/main/NeuralSOTA-7B-slerp.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/NeuralSOTA-7B-slerp-GGUF/resolve/main/NeuralSOTA-7B-slerp.Q8_0.gguf) | Q8_0 | 7.8 | 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"], "library_name": "transformers", "tags": ["merge", "mergekit", "lazymergekit", "Kukedlc/NeuralSoTa-7b-v0.1", "Kukedlc/NeuralSynthesis-7B-v0.3", "Kukedlc/NeuralSirKrishna-7b"], "base_model": "Kukedlc/NeuralSOTA-7B-slerp", "quantized_by": "mradermacher"} | mradermacher/NeuralSOTA-7B-slerp-GGUF | null | [
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"Kukedlc/NeuralSoTa-7b-v0.1",
"Kukedlc/NeuralSynthesis-7B-v0.3",
"Kukedlc/NeuralSirKrishna-7b",
"en",
"base_model:Kukedlc/NeuralSOTA-7B-slerp",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T05:40:06+00:00 | [] | [
"en"
] | TAGS
#transformers #gguf #merge #mergekit #lazymergekit #Kukedlc/NeuralSoTa-7b-v0.1 #Kukedlc/NeuralSynthesis-7B-v0.3 #Kukedlc/NeuralSirKrishna-7b #en #base_model-Kukedlc/NeuralSOTA-7B-slerp #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 #merge #mergekit #lazymergekit #Kukedlc/NeuralSoTa-7b-v0.1 #Kukedlc/NeuralSynthesis-7B-v0.3 #Kukedlc/NeuralSirKrishna-7b #en #base_model-Kukedlc/NeuralSOTA-7B-slerp #endpoints_compatible #region-us \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.
- **Developed by:** [More Information Needed]
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## How to Get Started with the Model
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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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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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## 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]
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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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[More Information Needed]
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<!-- 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]
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[More Information Needed]
| {"library_name": "transformers", "tags": []} | swj0419/bbc_retrain_STEP0000080 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-16T05:43:36+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]:",
"## 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 #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"
] |
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. -->
# bert-base-uncased-issues-128
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2256
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 16
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.0959 | 1.0 | 291 | 1.6893 |
| 1.6328 | 2.0 | 582 | 1.5211 |
| 1.4969 | 3.0 | 873 | 1.3475 |
| 1.3972 | 4.0 | 1164 | 1.3374 |
| 1.3331 | 5.0 | 1455 | 1.2506 |
| 1.283 | 6.0 | 1746 | 1.3699 |
| 1.2326 | 7.0 | 2037 | 1.2969 |
| 1.2025 | 8.0 | 2328 | 1.3562 |
| 1.1665 | 9.0 | 2619 | 1.2218 |
| 1.1402 | 10.0 | 2910 | 1.1799 |
| 1.1265 | 11.0 | 3201 | 1.1268 |
| 1.1111 | 12.0 | 3492 | 1.1815 |
| 1.089 | 13.0 | 3783 | 1.2181 |
| 1.0756 | 14.0 | 4074 | 1.2133 |
| 1.0714 | 15.0 | 4365 | 1.2337 |
| 1.0641 | 16.0 | 4656 | 1.2256 |
### 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": "bert-base-uncased", "model-index": [{"name": "bert-base-uncased-issues-128", "results": []}]} | c4big/bert-base-uncased-issues-128 | null | [
"transformers",
"safetensors",
"bert",
"fill-mask",
"generated_from_trainer",
"base_model:bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T05:43:37+00:00 | [] | [] | TAGS
#transformers #safetensors #bert #fill-mask #generated_from_trainer #base_model-bert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| bert-base-uncased-issues-128
============================
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2256
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 32
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 16
### 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: 32\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: 16",
"### 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 #fill-mask #generated_from_trainer #base_model-bert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\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: 16",
"### 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"
] |
depth-estimation | transformers |
# security_model
security_model is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased)
* [venkycs/llama-v2-7b-32kC-Security](https://huggingface.co/venkycs/llama-v2-7b-32kC-Security)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: google-bert/bert-base-uncased
layer_range: [0, 32]
- model: venkycs/llama-v2-7b-32kC-Security
layer_range: [0, 32]
merge_method: slerp
base_model: google-bert/bert-base-uncased
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "nagayama0706/security_model"
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", "google-bert/bert-base-uncased", "venkycs/llama-v2-7b-32kC-Security"], "base_model": ["google-bert/bert-base-uncased", "venkycs/llama-v2-7b-32kC-Security"], "pipeline_tag": "depth-estimation"} | nagayama0706/security_model | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"google-bert/bert-base-uncased",
"venkycs/llama-v2-7b-32kC-Security",
"depth-estimation",
"base_model:google-bert/bert-base-uncased",
"base_model:venkycs/llama-v2-7b-32kC-Security",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-16T05:43:41+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #google-bert/bert-base-uncased #venkycs/llama-v2-7b-32kC-Security #depth-estimation #base_model-google-bert/bert-base-uncased #base_model-venkycs/llama-v2-7b-32kC-Security #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# security_model
security_model is a merge of the following models using LazyMergekit:
* google-bert/bert-base-uncased
* venkycs/llama-v2-7b-32kC-Security
## Configuration
## Usage
| [
"# security_model\n\nsecurity_model is a merge of the following models using LazyMergekit:\n* google-bert/bert-base-uncased\n* venkycs/llama-v2-7b-32kC-Security",
"## Configuration",
"## Usage"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #google-bert/bert-base-uncased #venkycs/llama-v2-7b-32kC-Security #depth-estimation #base_model-google-bert/bert-base-uncased #base_model-venkycs/llama-v2-7b-32kC-Security #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# security_model\n\nsecurity_model is a merge of the following models using LazyMergekit:\n* google-bert/bert-base-uncased\n* venkycs/llama-v2-7b-32kC-Security",
"## Configuration",
"## Usage"
] |
text-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. -->
# selfbiorag-7b-wo-kqa_golden-sft
This model is a fine-tuned version of [dmis-lab/selfbiorag_7b](https://huggingface.co/dmis-lab/selfbiorag_7b) on the HuggingFaceH4/deita-10k-v0-sft dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0941
## 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: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.5253 | 0.89 | 6 | 1.2364 |
| 1.2282 | 1.93 | 13 | 1.1103 |
| 1.1381 | 2.67 | 18 | 1.0941 |
### Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.1.2
- Datasets 2.14.6
- Tokenizers 0.15.2
| {"tags": ["alignment-handbook", "trl", "sft", "generated_from_trainer", "trl", "sft", "generated_from_trainer"], "datasets": ["HuggingFaceH4/deita-10k-v0-sft"], "base_model": "dmis-lab/selfbiorag_7b", "model-index": [{"name": "selfbiorag-7b-wo-kqa_golden-sft", "results": []}]} | Minbyul/selfbiorag-7b-wo-kqa_golden-sft | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"alignment-handbook",
"trl",
"sft",
"generated_from_trainer",
"dataset:HuggingFaceH4/deita-10k-v0-sft",
"base_model:dmis-lab/selfbiorag_7b",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-16T05:45:06+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #alignment-handbook #trl #sft #generated_from_trainer #dataset-HuggingFaceH4/deita-10k-v0-sft #base_model-dmis-lab/selfbiorag_7b #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| selfbiorag-7b-wo-kqa\_golden-sft
================================
This model is a fine-tuned version of dmis-lab/selfbiorag\_7b on the HuggingFaceH4/deita-10k-v0-sft dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0941
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: 4
* eval\_batch\_size: 4
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 4
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* total\_eval\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.39.0.dev0
* Pytorch 2.1.2
* Datasets 2.14.6
* 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: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 4\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* total\\_eval\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.39.0.dev0\n* Pytorch 2.1.2\n* Datasets 2.14.6\n* Tokenizers 0.15.2"
] | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 4\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* total\\_eval\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.39.0.dev0\n* Pytorch 2.1.2\n* Datasets 2.14.6\n* Tokenizers 0.15.2"
] |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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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. -->
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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]
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
### Results
[More Information Needed]
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## 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]
#### 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. -->
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[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]
## Model Card Contact
[More Information Needed]
| {"library_name": "transformers", "tags": []} | swj0419/bbc_STEP0000160 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-16T05:45:54+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:
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- Shared by [optional]:
- Model type:
- Language(s) (NLP):
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## Uses
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### 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
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- 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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"#### 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 #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
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"### 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]",
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"#### Speeds, Sizes, Times [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"
] |
null | null |
Clip coreml exported image encoder. Exported based on https://github.com/mazzzystar/Queryable/tree/main | {"license": "mit"} | larryliu0820/clip-coreml | null | [
"coreml",
"license:mit",
"region:us"
] | null | 2024-04-16T05:46:24+00:00 | [] | [] | TAGS
#coreml #license-mit #region-us
|
Clip coreml exported image encoder. Exported based on URL | [] | [
"TAGS\n#coreml #license-mit #region-us \n"
] |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- 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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## How to Get Started with the Model
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!-- 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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| {"library_name": "transformers", "tags": []} | swj0419/email_retrain_STEP0000010 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-16T05:46:52+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
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### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
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### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
## Evaluation
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
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- Cloud Provider:
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## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
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## 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]:",
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"## Model Card Contact"
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"# 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 | peft | ## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- _load_in_8bit: False
- _load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
- bnb_4bit_quant_storage: uint8
- load_in_4bit: True
- load_in_8bit: False
### Framework versions
- PEFT 0.4.0
| {"library_name": "peft"} | nk555/phi-2_experiment_lora | null | [
"peft",
"region:us"
] | null | 2024-04-16T05:47:15+00:00 | [] | [] | TAGS
#peft #region-us
| ## Training procedure
The following 'bitsandbytes' quantization config was used during training:
- quant_method: bitsandbytes
- _load_in_8bit: False
- _load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
- bnb_4bit_quant_storage: uint8
- load_in_4bit: True
- load_in_8bit: False
### Framework versions
- PEFT 0.4.0
| [
"## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- quant_method: bitsandbytes\n- _load_in_8bit: False\n- _load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: float16\n- bnb_4bit_quant_storage: uint8\n- load_in_4bit: True\n- load_in_8bit: False",
"### 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- quant_method: bitsandbytes\n- _load_in_8bit: False\n- _load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: float16\n- bnb_4bit_quant_storage: uint8\n- load_in_4bit: True\n- load_in_8bit: False",
"### Framework versions\n\n\n- PEFT 0.4.0"
] |
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. -->
# ruBert-base-sberquad-0.01-len_2-filtered-negative-v2
This model is a fine-tuned version of [ai-forever/ruBert-base](https://huggingface.co/ai-forever/ruBert-base) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 7000
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 | {"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "ai-forever/ruBert-base", "model-index": [{"name": "ruBert-base-sberquad-0.01-len_2-filtered-negative-v2", "results": []}]} | Shalazary/ruBert-base-sberquad-0.01-len_2-filtered-negative-v2 | null | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:ai-forever/ruBert-base",
"license:apache-2.0",
"region:us"
] | null | 2024-04-16T05:49:07+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #generated_from_trainer #base_model-ai-forever/ruBert-base #license-apache-2.0 #region-us
|
# ruBert-base-sberquad-0.01-len_2-filtered-negative-v2
This model is a fine-tuned version of ai-forever/ruBert-base on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 7000
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 | [
"# ruBert-base-sberquad-0.01-len_2-filtered-negative-v2\n\nThis model is a fine-tuned version of ai-forever/ruBert-base on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0005\n- train_batch_size: 32\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- training_steps: 7000",
"### Training results",
"### Framework versions\n\n- PEFT 0.10.0\n- Transformers 4.39.3\n- Pytorch 2.2.2+cu121\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] | [
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"# ruBert-base-sberquad-0.01-len_2-filtered-negative-v2\n\nThis model is a fine-tuned version of ai-forever/ruBert-base on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0005\n- train_batch_size: 32\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- training_steps: 7000",
"### Training results",
"### Framework versions\n\n- PEFT 0.10.0\n- Transformers 4.39.3\n- Pytorch 2.2.2+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. -->
# Pretraining_Test_v4
This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "microsoft/deberta-base", "model-index": [{"name": "Pretraining_Test_v4", "results": []}]} | JJ-Tae/Pretraining_Test_v4 | null | [
"transformers",
"tensorboard",
"safetensors",
"deberta",
"fill-mask",
"generated_from_trainer",
"base_model:microsoft/deberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T05:49:32+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #deberta #fill-mask #generated_from_trainer #base_model-microsoft/deberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# Pretraining_Test_v4
This model is a fine-tuned version of microsoft/deberta-base on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| [
"# Pretraining_Test_v4\n\nThis model is a fine-tuned version of microsoft/deberta-base 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: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.2.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
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"# Pretraining_Test_v4\n\nThis model is a fine-tuned version of microsoft/deberta-base 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: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.2.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] |
text-generation | transformers | <p align="left">
<img src="https://huggingface.co/crimsonjoo/Neversleep-3B-v0.1/resolve/main/neversleep_logo.webp" width="70%"/>
<p>
# "We must sleep, but AI Never Sleeps!"
## Simple-Usage
```python
# number_of_old_tokens is the size of tokenizer before vocab extension. For example, in case of EEVE-Korean-10.8B-v1.0, number_of_old_tokens is 32000.
def freeze_partial_embedding_hook(grad):
grad[:number_of_old_tokens] = 0
return grad
for name, param in model.named_parameters():
if ("lm_head" in name or "embed_tokens" in name) and "original" not in name:
param.requires_grad = True
if "embed_tokens" in name:
param.register_hook(freeze_partial_embedding_hook)
else:
param.requires_grad = False
```
## About the Model
First of all, Overwhelming gratitude to 'yanolja/EEVE' Model & Team!
This model is a Korean vocabulary-extended version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2), specifically fine-tuned on various Korean web-crawled datasets available on HuggingFace. Our approach was to expand the model's understanding of Korean by pre-training the embeddings for new tokens and partially fine-tuning the `lm_head` embeddings for the already existing tokens while preserving the original parameters of the base model.
### Technical Deep Dive (EEVE)
<p align="left">
<img src="https://huggingface.co/crimsonjoo/Neversleep-3B-v0.1/resolve/main/EEVE_figure.png" width="100%"/>
<p>
To adapt foundational models from English to Korean, we use subword-based embedding with a seven-stage training process involving parameter freezing.
This approach progressively trains from input embeddings to full parameters, efficiently extending the model's vocabulary to include Korean.
Our method enhances the model's cross-linguistic applicability by carefully integrating new linguistic tokens, focusing on causal language modeling pre-training.
We leverage the inherent capabilities of foundational models trained on English to efficiently transfer knowledge and reasoning to Korean, optimizing the adaptation process.
For more details, please refer to our technical report: [Efficient and Effective Vocabulary Expansion Towards Multilingual Large Language Models](https://arxiv.org/abs/2402.14714).
### Usage and Limitations
Keep in mind that this model hasn't been fine-tuned with instruction-based training. While it excels in Korean language tasks, we advise careful consideration and further training for specific applications.
### Training Details
Our model’s training was comprehensive and diverse:
- **Vocabulary Expansion:**
We meticulously selected 8,960 Korean tokens based on their frequency in our Korean web corpus. This process involved multiple rounds of tokenizer training, manual curation, and token frequency analysis, ensuring a rich and relevant vocabulary for our model.
1. **Initial Tokenizer Training:** We trained an intermediate tokenizer on a Korean web corpus, with a vocabulary of 40,000 tokens.
2. **Extraction of New Korean Tokens:** From the intermediate tokenizer, we identified all Korean tokens not present in the original SOLAR's tokenizer.
3. **Manual Tokenizer Construction:** We then built the target tokenizer, focusing on these new Korean tokens.
4. **Frequency Analysis:** Using the target tokenizer, we processed a 100GB Korean corpus to count each token's frequency.
5. **Refinement of Token List:** We removed tokens appearing less than 6,000 times, ensuring to secure enough tokens to train models later.
6. **Inclusion of Single-Letter Characters:** Counted missing Korean single-letter characters and added them to the target tokenizer that appeared more than 6,000 times.
7. **Iterative Refinement:** We repeated steps 2 to 6 until there were no tokens to drop or add.
8. **Training Bias Towards New Tokens:** Our training data was biased to include more texts with new tokens, for effective learning.
This rigorous approach ensured a comprehensive and contextually rich Korean vocabulary for the model. | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "yanolja/EEVE-Korean-2.8B-v1.0"} | crimsonjoo/Neversleep-3B-v0.1 | null | [
"transformers",
"safetensors",
"phi",
"text-generation",
"generated_from_trainer",
"conversational",
"custom_code",
"arxiv:2402.14714",
"base_model:yanolja/EEVE-Korean-2.8B-v1.0",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-16T05:50:18+00:00 | [
"2402.14714"
] | [] | TAGS
#transformers #safetensors #phi #text-generation #generated_from_trainer #conversational #custom_code #arxiv-2402.14714 #base_model-yanolja/EEVE-Korean-2.8B-v1.0 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| <p align="left">
<img src="URL width="70%"/>
<p>
# "We must sleep, but AI Never Sleeps!"
## Simple-Usage
## About the Model
First of all, Overwhelming gratitude to 'yanolja/EEVE' Model & Team!
This model is a Korean vocabulary-extended version of microsoft/phi-2, specifically fine-tuned on various Korean web-crawled datasets available on HuggingFace. Our approach was to expand the model's understanding of Korean by pre-training the embeddings for new tokens and partially fine-tuning the 'lm_head' embeddings for the already existing tokens while preserving the original parameters of the base model.
### Technical Deep Dive (EEVE)
<p align="left">
<img src="URL width="100%"/>
<p>
To adapt foundational models from English to Korean, we use subword-based embedding with a seven-stage training process involving parameter freezing.
This approach progressively trains from input embeddings to full parameters, efficiently extending the model's vocabulary to include Korean.
Our method enhances the model's cross-linguistic applicability by carefully integrating new linguistic tokens, focusing on causal language modeling pre-training.
We leverage the inherent capabilities of foundational models trained on English to efficiently transfer knowledge and reasoning to Korean, optimizing the adaptation process.
For more details, please refer to our technical report: Efficient and Effective Vocabulary Expansion Towards Multilingual Large Language Models.
### Usage and Limitations
Keep in mind that this model hasn't been fine-tuned with instruction-based training. While it excels in Korean language tasks, we advise careful consideration and further training for specific applications.
### Training Details
Our model’s training was comprehensive and diverse:
- Vocabulary Expansion:
We meticulously selected 8,960 Korean tokens based on their frequency in our Korean web corpus. This process involved multiple rounds of tokenizer training, manual curation, and token frequency analysis, ensuring a rich and relevant vocabulary for our model.
1. Initial Tokenizer Training: We trained an intermediate tokenizer on a Korean web corpus, with a vocabulary of 40,000 tokens.
2. Extraction of New Korean Tokens: From the intermediate tokenizer, we identified all Korean tokens not present in the original SOLAR's tokenizer.
3. Manual Tokenizer Construction: We then built the target tokenizer, focusing on these new Korean tokens.
4. Frequency Analysis: Using the target tokenizer, we processed a 100GB Korean corpus to count each token's frequency.
5. Refinement of Token List: We removed tokens appearing less than 6,000 times, ensuring to secure enough tokens to train models later.
6. Inclusion of Single-Letter Characters: Counted missing Korean single-letter characters and added them to the target tokenizer that appeared more than 6,000 times.
7. Iterative Refinement: We repeated steps 2 to 6 until there were no tokens to drop or add.
8. Training Bias Towards New Tokens: Our training data was biased to include more texts with new tokens, for effective learning.
This rigorous approach ensured a comprehensive and contextually rich Korean vocabulary for the model. | [
"# \"We must sleep, but AI Never Sleeps!\"",
"## Simple-Usage",
"## About the Model\n\nFirst of all, Overwhelming gratitude to 'yanolja/EEVE' Model & Team!\nThis model is a Korean vocabulary-extended version of microsoft/phi-2, specifically fine-tuned on various Korean web-crawled datasets available on HuggingFace. Our approach was to expand the model's understanding of Korean by pre-training the embeddings for new tokens and partially fine-tuning the 'lm_head' embeddings for the already existing tokens while preserving the original parameters of the base model.",
"### Technical Deep Dive (EEVE)\n<p align=\"left\">\n <img src=\"URL width=\"100%\"/>\n<p>\n\nTo adapt foundational models from English to Korean, we use subword-based embedding with a seven-stage training process involving parameter freezing. \nThis approach progressively trains from input embeddings to full parameters, efficiently extending the model's vocabulary to include Korean. \nOur method enhances the model's cross-linguistic applicability by carefully integrating new linguistic tokens, focusing on causal language modeling pre-training. \nWe leverage the inherent capabilities of foundational models trained on English to efficiently transfer knowledge and reasoning to Korean, optimizing the adaptation process.\n\nFor more details, please refer to our technical report: Efficient and Effective Vocabulary Expansion Towards Multilingual Large Language Models.",
"### Usage and Limitations\n\nKeep in mind that this model hasn't been fine-tuned with instruction-based training. While it excels in Korean language tasks, we advise careful consideration and further training for specific applications.",
"### Training Details\n\nOur model’s training was comprehensive and diverse:\n\n- Vocabulary Expansion:\n We meticulously selected 8,960 Korean tokens based on their frequency in our Korean web corpus. This process involved multiple rounds of tokenizer training, manual curation, and token frequency analysis, ensuring a rich and relevant vocabulary for our model.\n\n 1. Initial Tokenizer Training: We trained an intermediate tokenizer on a Korean web corpus, with a vocabulary of 40,000 tokens.\n \n 2. Extraction of New Korean Tokens: From the intermediate tokenizer, we identified all Korean tokens not present in the original SOLAR's tokenizer.\n\n 3. Manual Tokenizer Construction: We then built the target tokenizer, focusing on these new Korean tokens.\n\n 4. Frequency Analysis: Using the target tokenizer, we processed a 100GB Korean corpus to count each token's frequency.\n\n 5. Refinement of Token List: We removed tokens appearing less than 6,000 times, ensuring to secure enough tokens to train models later.\n\n 6. Inclusion of Single-Letter Characters: Counted missing Korean single-letter characters and added them to the target tokenizer that appeared more than 6,000 times.\n\n 7. Iterative Refinement: We repeated steps 2 to 6 until there were no tokens to drop or add.\n\n 8. Training Bias Towards New Tokens: Our training data was biased to include more texts with new tokens, for effective learning.\n\nThis rigorous approach ensured a comprehensive and contextually rich Korean vocabulary for the model."
] | [
"TAGS\n#transformers #safetensors #phi #text-generation #generated_from_trainer #conversational #custom_code #arxiv-2402.14714 #base_model-yanolja/EEVE-Korean-2.8B-v1.0 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# \"We must sleep, but AI Never Sleeps!\"",
"## Simple-Usage",
"## About the Model\n\nFirst of all, Overwhelming gratitude to 'yanolja/EEVE' Model & Team!\nThis model is a Korean vocabulary-extended version of microsoft/phi-2, specifically fine-tuned on various Korean web-crawled datasets available on HuggingFace. Our approach was to expand the model's understanding of Korean by pre-training the embeddings for new tokens and partially fine-tuning the 'lm_head' embeddings for the already existing tokens while preserving the original parameters of the base model.",
"### Technical Deep Dive (EEVE)\n<p align=\"left\">\n <img src=\"URL width=\"100%\"/>\n<p>\n\nTo adapt foundational models from English to Korean, we use subword-based embedding with a seven-stage training process involving parameter freezing. \nThis approach progressively trains from input embeddings to full parameters, efficiently extending the model's vocabulary to include Korean. \nOur method enhances the model's cross-linguistic applicability by carefully integrating new linguistic tokens, focusing on causal language modeling pre-training. \nWe leverage the inherent capabilities of foundational models trained on English to efficiently transfer knowledge and reasoning to Korean, optimizing the adaptation process.\n\nFor more details, please refer to our technical report: Efficient and Effective Vocabulary Expansion Towards Multilingual Large Language Models.",
"### Usage and Limitations\n\nKeep in mind that this model hasn't been fine-tuned with instruction-based training. While it excels in Korean language tasks, we advise careful consideration and further training for specific applications.",
"### Training Details\n\nOur model’s training was comprehensive and diverse:\n\n- Vocabulary Expansion:\n We meticulously selected 8,960 Korean tokens based on their frequency in our Korean web corpus. This process involved multiple rounds of tokenizer training, manual curation, and token frequency analysis, ensuring a rich and relevant vocabulary for our model.\n\n 1. Initial Tokenizer Training: We trained an intermediate tokenizer on a Korean web corpus, with a vocabulary of 40,000 tokens.\n \n 2. Extraction of New Korean Tokens: From the intermediate tokenizer, we identified all Korean tokens not present in the original SOLAR's tokenizer.\n\n 3. Manual Tokenizer Construction: We then built the target tokenizer, focusing on these new Korean tokens.\n\n 4. Frequency Analysis: Using the target tokenizer, we processed a 100GB Korean corpus to count each token's frequency.\n\n 5. Refinement of Token List: We removed tokens appearing less than 6,000 times, ensuring to secure enough tokens to train models later.\n\n 6. Inclusion of Single-Letter Characters: Counted missing Korean single-letter characters and added them to the target tokenizer that appeared more than 6,000 times.\n\n 7. Iterative Refinement: We repeated steps 2 to 6 until there were no tokens to drop or add.\n\n 8. Training Bias Towards New Tokens: Our training data was biased to include more texts with new tokens, for effective learning.\n\nThis rigorous approach ensured a comprehensive and contextually rich Korean vocabulary for the 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]
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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| {"library_name": "transformers", "tags": []} | swj0419/bbc_retrain_STEP0000100 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-16T05:52:10+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:
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## How to Get Started with the Model
Use the code below to get started with the model.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
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## Technical Specifications [optional]
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### Compute Infrastructure
#### Hardware
#### 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]:",
"## 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 #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 | 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. -->
# GUE_splice_reconstructed-seqsight_8192_512_30M-L32_all
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_8192_512_30M](https://huggingface.co/mahdibaghbanzadeh/seqsight_8192_512_30M) on the [mahdibaghbanzadeh/GUE_splice_reconstructed](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_splice_reconstructed) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9664
- F1 Score: 0.7014
- Accuracy: 0.7030
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 2048
- eval_batch_size: 2048
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:|
| 0.9343 | 11.11 | 200 | 0.8346 | 0.5809 | 0.6241 |
| 0.7818 | 22.22 | 400 | 0.7676 | 0.6464 | 0.6611 |
| 0.7135 | 33.33 | 600 | 0.7563 | 0.6612 | 0.6620 |
| 0.6651 | 44.44 | 800 | 0.7568 | 0.6691 | 0.6710 |
| 0.627 | 55.56 | 1000 | 0.7487 | 0.6738 | 0.6767 |
| 0.5948 | 66.67 | 1200 | 0.7433 | 0.6800 | 0.6850 |
| 0.5688 | 77.78 | 1400 | 0.7494 | 0.6815 | 0.6870 |
| 0.5437 | 88.89 | 1600 | 0.7587 | 0.6811 | 0.6815 |
| 0.5212 | 100.0 | 1800 | 0.7816 | 0.6787 | 0.6789 |
| 0.5014 | 111.11 | 2000 | 0.7846 | 0.6859 | 0.6857 |
| 0.4824 | 122.22 | 2200 | 0.7872 | 0.6852 | 0.6859 |
| 0.4664 | 133.33 | 2400 | 0.7993 | 0.6910 | 0.6951 |
| 0.4508 | 144.44 | 2600 | 0.8120 | 0.6901 | 0.6949 |
| 0.4369 | 155.56 | 2800 | 0.8342 | 0.6886 | 0.6898 |
| 0.4241 | 166.67 | 3000 | 0.8359 | 0.6855 | 0.6872 |
| 0.41 | 177.78 | 3200 | 0.8491 | 0.6895 | 0.6903 |
| 0.3981 | 188.89 | 3400 | 0.8528 | 0.6881 | 0.6874 |
| 0.3863 | 200.0 | 3600 | 0.8736 | 0.6874 | 0.6914 |
| 0.3748 | 211.11 | 3800 | 0.8668 | 0.6905 | 0.6940 |
| 0.3662 | 222.22 | 4000 | 0.8681 | 0.6891 | 0.6896 |
| 0.355 | 233.33 | 4200 | 0.8869 | 0.6923 | 0.6942 |
| 0.3465 | 244.44 | 4400 | 0.8918 | 0.6886 | 0.6911 |
| 0.3395 | 255.56 | 4600 | 0.9159 | 0.6880 | 0.6887 |
| 0.3315 | 266.67 | 4800 | 0.9279 | 0.6934 | 0.6953 |
| 0.3231 | 277.78 | 5000 | 0.9232 | 0.6917 | 0.6927 |
| 0.3162 | 288.89 | 5200 | 0.9350 | 0.6913 | 0.6925 |
| 0.3108 | 300.0 | 5400 | 0.9520 | 0.6959 | 0.6979 |
| 0.3042 | 311.11 | 5600 | 0.9396 | 0.6899 | 0.6918 |
| 0.3 | 322.22 | 5800 | 0.9521 | 0.6927 | 0.6982 |
| 0.2898 | 333.33 | 6000 | 0.9616 | 0.6928 | 0.6951 |
| 0.2887 | 344.44 | 6200 | 0.9716 | 0.6937 | 0.6960 |
| 0.2819 | 355.56 | 6400 | 0.9720 | 0.6886 | 0.6876 |
| 0.2774 | 366.67 | 6600 | 0.9822 | 0.6907 | 0.6918 |
| 0.2727 | 377.78 | 6800 | 0.9960 | 0.6888 | 0.6907 |
| 0.2694 | 388.89 | 7000 | 0.9855 | 0.6936 | 0.6957 |
| 0.2635 | 400.0 | 7200 | 0.9976 | 0.6907 | 0.6918 |
| 0.261 | 411.11 | 7400 | 1.0112 | 0.6915 | 0.6938 |
| 0.2582 | 422.22 | 7600 | 1.0083 | 0.6880 | 0.6885 |
| 0.2557 | 433.33 | 7800 | 1.0171 | 0.6923 | 0.6936 |
| 0.2534 | 444.44 | 8000 | 1.0165 | 0.6962 | 0.6977 |
| 0.2498 | 455.56 | 8200 | 1.0167 | 0.6922 | 0.6929 |
| 0.2468 | 466.67 | 8400 | 1.0320 | 0.6914 | 0.6927 |
| 0.2447 | 477.78 | 8600 | 1.0256 | 0.6906 | 0.6916 |
| 0.2424 | 488.89 | 8800 | 1.0227 | 0.6895 | 0.6903 |
| 0.2401 | 500.0 | 9000 | 1.0330 | 0.6923 | 0.6938 |
| 0.2397 | 511.11 | 9200 | 1.0332 | 0.6920 | 0.6933 |
| 0.2387 | 522.22 | 9400 | 1.0357 | 0.6937 | 0.6951 |
| 0.2369 | 533.33 | 9600 | 1.0390 | 0.6926 | 0.6940 |
| 0.236 | 544.44 | 9800 | 1.0405 | 0.6924 | 0.6933 |
| 0.2364 | 555.56 | 10000 | 1.0352 | 0.6910 | 0.6920 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_8192_512_30M", "model-index": [{"name": "GUE_splice_reconstructed-seqsight_8192_512_30M-L32_all", "results": []}]} | mahdibaghbanzadeh/GUE_splice_reconstructed-seqsight_8192_512_30M-L32_all | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_8192_512_30M",
"region:us"
] | null | 2024-04-16T05:52:36+00:00 | [] | [] | TAGS
#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_8192_512_30M #region-us
| GUE\_splice\_reconstructed-seqsight\_8192\_512\_30M-L32\_all
============================================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_8192\_512\_30M on the mahdibaghbanzadeh/GUE\_splice\_reconstructed dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9664
* F1 Score: 0.7014
* Accuracy: 0.7030
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0005
* train\_batch\_size: 2048
* eval\_batch\_size: 2048
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 10000
### Training results
### Framework versions
* PEFT 0.9.0
* Transformers 4.38.2
* Pytorch 2.2.0+cu121
* Datasets 2.17.1
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 2048\n* eval\\_batch\\_size: 2048\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2"
] | [
"TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_8192_512_30M #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 2048\n* eval\\_batch\\_size: 2048\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2"
] |
text-generation | transformers |
This is an experimental model.
The idea is :
- Calculate the difference in weights between a donor model(meta-math/MetaMath-Mistral-7B) and the base model(mistralai/Mistral-7B-v0.1). This difference represents how much each parameter needs to be adjusted to go from the base state to the donor state.
```
vector = math_model.state_dict()[k] - base_model.state_dict()[k]
```
- Vector retrieved from the result of step one, is added to third model(lex-hue/Delexa-7b). This should transfer **math** *skills* to our third model.
```
vector = new_math_model.state_dict()[k]
new_v = v + vector.to(v.device)
v.copy_(new_v)
```
### Example:
```
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_name = "aloobun/CosmicNoodle-7B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")
prompt = "For the natural number A, the quotient of A divided by 9 is 6 and the remainder is 5. What is the value of A?\n"
input_ids = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
tokens = model.generate(input_ids.to(device=model.device), max_new_tokens=128, temperature=0.99, top_p=0.95, do_sample=True)
out = tokenizer.decode(tokens[0], skip_special_tokens=True)
print(out)
``` | {"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["conversational", "math", "distillation", "mistral"]} | aloobun/CosmicNoodle-7B | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"math",
"distillation",
"custom_code",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-16T05:54:21+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #mistral #text-generation #conversational #math #distillation #custom_code #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
This is an experimental model.
The idea is :
- Calculate the difference in weights between a donor model(meta-math/MetaMath-Mistral-7B) and the base model(mistralai/Mistral-7B-v0.1). This difference represents how much each parameter needs to be adjusted to go from the base state to the donor state.
- Vector retrieved from the result of step one, is added to third model(lex-hue/Delexa-7b). This should transfer math *skills* to our third model.
### Example:
| [
"### Example:"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #conversational #math #distillation #custom_code #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Example:"
] |
text-generation | transformers |
# h2omistral-1.8b-dare3
h2omistral-1.8b-dare3 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [h2oai/h2o-danube2-1.8b-chat](https://huggingface.co/h2oai/h2o-danube2-1.8b-chat)
* [tokyotech-llm/Swallow-MS-7b-v0.1](https://huggingface.co/tokyotech-llm/Swallow-MS-7b-v0.1)
## 🧩 Configuration
```yaml
slices:
- sources:
- layer_range: [0, 24]
model: h2oai/h2o-danube2-1.8b-chat
parameters:
density: [1, 0.7, 0.1]
weight: 1.0
- layer_range: [0, 24]
model: tokyotech-llm/Swallow-MS-7b-v0.1
parameters:
density: 0.33
weight:
- filter: mlp
value: 0.5
- value: 0
merge_method: dare_ties
base_model: h2oai/h2o-danube2-1.8b-chat
parameters:
normalize: true
int8_mask: true
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "aipib/h2omistral-1.8b-dare3"
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"])
``` | {"tags": ["merge", "mergekit", "lazymergekit", "h2oai/h2o-danube2-1.8b-chat", "tokyotech-llm/Swallow-MS-7b-v0.1"], "base_model": ["h2oai/h2o-danube2-1.8b-chat", "tokyotech-llm/Swallow-MS-7b-v0.1"]} | aipib/h2omistral-1.8b-dare3 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"h2oai/h2o-danube2-1.8b-chat",
"tokyotech-llm/Swallow-MS-7b-v0.1",
"conversational",
"base_model:h2oai/h2o-danube2-1.8b-chat",
"base_model:tokyotech-llm/Swallow-MS-7b-v0.1",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-16T05:54:31+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #h2oai/h2o-danube2-1.8b-chat #tokyotech-llm/Swallow-MS-7b-v0.1 #conversational #base_model-h2oai/h2o-danube2-1.8b-chat #base_model-tokyotech-llm/Swallow-MS-7b-v0.1 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# h2omistral-1.8b-dare3
h2omistral-1.8b-dare3 is a merge of the following models using LazyMergekit:
* h2oai/h2o-danube2-1.8b-chat
* tokyotech-llm/Swallow-MS-7b-v0.1
## Configuration
## Usage
| [
"# h2omistral-1.8b-dare3\n\nh2omistral-1.8b-dare3 is a merge of the following models using LazyMergekit:\n* h2oai/h2o-danube2-1.8b-chat\n* tokyotech-llm/Swallow-MS-7b-v0.1",
"## Configuration",
"## Usage"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #h2oai/h2o-danube2-1.8b-chat #tokyotech-llm/Swallow-MS-7b-v0.1 #conversational #base_model-h2oai/h2o-danube2-1.8b-chat #base_model-tokyotech-llm/Swallow-MS-7b-v0.1 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# h2omistral-1.8b-dare3\n\nh2omistral-1.8b-dare3 is a merge of the following models using LazyMergekit:\n* h2oai/h2o-danube2-1.8b-chat\n* tokyotech-llm/Swallow-MS-7b-v0.1",
"## Configuration",
"## Usage"
] |
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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## Uses
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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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#### 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]
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[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]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [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]
| {"library_name": "transformers", "tags": []} | swj0419/bbc_STEP0000200 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-16T05:54:40+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]:",
"## 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.",
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"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
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"#### Speeds, Sizes, Times [optional]",
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"#### 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]",
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"## 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]:",
"### 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. -->
# NHS-roberta-binary-random
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5076
- Accuracy: 0.7937
- Precision: 0.7920
- Recall: 0.8022
- F1: 0.7915
## 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.0996 | 1.0 | 397 | 0.4221 | 0.8088 | 0.8018 | 0.8041 | 0.8029 |
| 0.0996 | 2.0 | 794 | 0.4597 | 0.7861 | 0.7913 | 0.8009 | 0.7851 |
| 1.9859 | 3.0 | 1191 | 0.5076 | 0.7937 | 0.7920 | 0.8022 | 0.7915 |
### 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", "precision", "recall", "f1"], "base_model": "roberta-base", "model-index": [{"name": "NHS-roberta-binary-random", "results": []}]} | intermezzo672/NHS-roberta-binary-random | null | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T05:55:23+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #base_model-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us
| NHS-roberta-binary-random
=========================
This model is a fine-tuned version of roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5076
* Accuracy: 0.7937
* Precision: 0.7920
* Recall: 0.8022
* F1: 0.7915
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: 3e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 6
### 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: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 6",
"### Training results",
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 6",
"### 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 | 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. -->
# GUE_tf_0-seqsight_8192_512_30M-L32_all
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_8192_512_30M](https://huggingface.co/mahdibaghbanzadeh/seqsight_8192_512_30M) on the [mahdibaghbanzadeh/GUE_tf_0](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_0) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6934
- F1 Score: 0.7132
- Accuracy: 0.715
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 2048
- eval_batch_size: 2048
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.6225 | 12.5 | 200 | 0.5892 | 0.6971 | 0.697 |
| 0.5357 | 25.0 | 400 | 0.5881 | 0.7101 | 0.71 |
| 0.4937 | 37.5 | 600 | 0.5850 | 0.7244 | 0.726 |
| 0.4603 | 50.0 | 800 | 0.6049 | 0.7187 | 0.72 |
| 0.4325 | 62.5 | 1000 | 0.6313 | 0.7047 | 0.705 |
| 0.4105 | 75.0 | 1200 | 0.6312 | 0.7209 | 0.721 |
| 0.3917 | 87.5 | 1400 | 0.6381 | 0.7121 | 0.713 |
| 0.3745 | 100.0 | 1600 | 0.6879 | 0.7190 | 0.719 |
| 0.3607 | 112.5 | 1800 | 0.6741 | 0.7225 | 0.724 |
| 0.3507 | 125.0 | 2000 | 0.6616 | 0.7256 | 0.726 |
| 0.3407 | 137.5 | 2200 | 0.6852 | 0.7266 | 0.727 |
| 0.329 | 150.0 | 2400 | 0.7090 | 0.7287 | 0.73 |
| 0.3201 | 162.5 | 2600 | 0.6944 | 0.7197 | 0.721 |
| 0.3093 | 175.0 | 2800 | 0.7109 | 0.7220 | 0.722 |
| 0.2984 | 187.5 | 3000 | 0.7240 | 0.7199 | 0.72 |
| 0.292 | 200.0 | 3200 | 0.7457 | 0.7209 | 0.721 |
| 0.2815 | 212.5 | 3400 | 0.7469 | 0.7159 | 0.716 |
| 0.2739 | 225.0 | 3600 | 0.7821 | 0.7110 | 0.711 |
| 0.2661 | 237.5 | 3800 | 0.7747 | 0.7100 | 0.71 |
| 0.2595 | 250.0 | 4000 | 0.7560 | 0.7100 | 0.71 |
| 0.2501 | 262.5 | 4200 | 0.7846 | 0.7109 | 0.711 |
| 0.2449 | 275.0 | 4400 | 0.7904 | 0.7110 | 0.711 |
| 0.2367 | 287.5 | 4600 | 0.7928 | 0.7116 | 0.712 |
| 0.2316 | 300.0 | 4800 | 0.8287 | 0.7093 | 0.71 |
| 0.2255 | 312.5 | 5000 | 0.8437 | 0.7106 | 0.711 |
| 0.2203 | 325.0 | 5200 | 0.8609 | 0.7096 | 0.71 |
| 0.2139 | 337.5 | 5400 | 0.8534 | 0.7067 | 0.707 |
| 0.2089 | 350.0 | 5600 | 0.8720 | 0.7120 | 0.712 |
| 0.2056 | 362.5 | 5800 | 0.8517 | 0.7091 | 0.709 |
| 0.1984 | 375.0 | 6000 | 0.8594 | 0.702 | 0.702 |
| 0.1969 | 387.5 | 6200 | 0.8928 | 0.7020 | 0.702 |
| 0.1917 | 400.0 | 6400 | 0.8901 | 0.7114 | 0.712 |
| 0.1882 | 412.5 | 6600 | 0.8833 | 0.7109 | 0.711 |
| 0.1848 | 425.0 | 6800 | 0.8861 | 0.6970 | 0.697 |
| 0.1803 | 437.5 | 7000 | 0.9046 | 0.7029 | 0.703 |
| 0.1772 | 450.0 | 7200 | 0.9143 | 0.6994 | 0.7 |
| 0.1751 | 462.5 | 7400 | 0.9243 | 0.6967 | 0.697 |
| 0.1732 | 475.0 | 7600 | 0.9390 | 0.7069 | 0.707 |
| 0.1699 | 487.5 | 7800 | 0.9518 | 0.7080 | 0.708 |
| 0.1662 | 500.0 | 8000 | 0.9361 | 0.7070 | 0.707 |
| 0.1659 | 512.5 | 8200 | 0.9330 | 0.6999 | 0.7 |
| 0.163 | 525.0 | 8400 | 0.9480 | 0.6989 | 0.699 |
| 0.1613 | 537.5 | 8600 | 0.9420 | 0.7050 | 0.705 |
| 0.1611 | 550.0 | 8800 | 0.9542 | 0.7070 | 0.707 |
| 0.1582 | 562.5 | 9000 | 0.9505 | 0.6958 | 0.696 |
| 0.157 | 575.0 | 9200 | 0.9491 | 0.7019 | 0.702 |
| 0.1555 | 587.5 | 9400 | 0.9579 | 0.7018 | 0.702 |
| 0.1554 | 600.0 | 9600 | 0.9698 | 0.6977 | 0.698 |
| 0.1548 | 612.5 | 9800 | 0.9704 | 0.6978 | 0.698 |
| 0.1543 | 625.0 | 10000 | 0.9668 | 0.6968 | 0.697 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_8192_512_30M", "model-index": [{"name": "GUE_tf_0-seqsight_8192_512_30M-L32_all", "results": []}]} | mahdibaghbanzadeh/GUE_tf_0-seqsight_8192_512_30M-L32_all | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_8192_512_30M",
"region:us"
] | null | 2024-04-16T05:56:56+00:00 | [] | [] | TAGS
#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_8192_512_30M #region-us
| GUE\_tf\_0-seqsight\_8192\_512\_30M-L32\_all
============================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_8192\_512\_30M on the mahdibaghbanzadeh/GUE\_tf\_0 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6934
* F1 Score: 0.7132
* Accuracy: 0.715
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0005
* train\_batch\_size: 2048
* eval\_batch\_size: 2048
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 10000
### Training results
### Framework versions
* PEFT 0.9.0
* Transformers 4.38.2
* Pytorch 2.2.0+cu121
* Datasets 2.17.1
* Tokenizers 0.15.2
| [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 2048\n* eval\\_batch\\_size: 2048\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2"
] |
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. -->
# mt5-finetuned-english-to-Finnish
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 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: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0 | 1.0 | 8100 | nan |
### 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": "google/mt5-small", "model-index": [{"name": "mt5-finetuned-english-to-Finnish", "results": []}]} | ElliottZ/mt5-finetuned-english-to-Finnish | null | [
"transformers",
"tensorboard",
"safetensors",
"mt5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/mt5-small",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-16T05:57:04+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #mt5 #text2text-generation #generated_from_trainer #base_model-google/mt5-small #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-finetuned-english-to-Finnish
================================
This model is a fine-tuned version of google/mt5-small on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 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: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1
* mixed\_precision\_training: Native AMP
### 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: 32\n* eval\\_batch\\_size: 32\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\n* mixed\\_precision\\_training: Native AMP",
"### 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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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\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\n* mixed\\_precision\\_training: Native AMP",
"### 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 | 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. -->
# GUE_tf_1-seqsight_8192_512_30M-L32_all
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_8192_512_30M](https://huggingface.co/mahdibaghbanzadeh/seqsight_8192_512_30M) on the [mahdibaghbanzadeh/GUE_tf_1](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_1) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5174
- F1 Score: 0.7418
- Accuracy: 0.746
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 2048
- eval_batch_size: 2048
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:|
| 0.6225 | 13.33 | 200 | 0.5973 | 0.6770 | 0.677 |
| 0.5426 | 26.67 | 400 | 0.6050 | 0.6616 | 0.663 |
| 0.5017 | 40.0 | 600 | 0.6217 | 0.6740 | 0.674 |
| 0.4668 | 53.33 | 800 | 0.6290 | 0.6902 | 0.692 |
| 0.4393 | 66.67 | 1000 | 0.6491 | 0.6888 | 0.689 |
| 0.4151 | 80.0 | 1200 | 0.6627 | 0.6889 | 0.689 |
| 0.3961 | 93.33 | 1400 | 0.6513 | 0.6840 | 0.684 |
| 0.3797 | 106.67 | 1600 | 0.6851 | 0.6879 | 0.688 |
| 0.3656 | 120.0 | 1800 | 0.7099 | 0.6855 | 0.686 |
| 0.3537 | 133.33 | 2000 | 0.7395 | 0.6800 | 0.68 |
| 0.3408 | 146.67 | 2200 | 0.7374 | 0.6830 | 0.683 |
| 0.3307 | 160.0 | 2400 | 0.7293 | 0.6840 | 0.684 |
| 0.3191 | 173.33 | 2600 | 0.7739 | 0.6810 | 0.681 |
| 0.3083 | 186.67 | 2800 | 0.7673 | 0.6770 | 0.677 |
| 0.2991 | 200.0 | 3000 | 0.8049 | 0.6789 | 0.679 |
| 0.289 | 213.33 | 3200 | 0.7730 | 0.6768 | 0.677 |
| 0.2784 | 226.67 | 3400 | 0.8322 | 0.6779 | 0.678 |
| 0.2716 | 240.0 | 3600 | 0.8422 | 0.6690 | 0.67 |
| 0.262 | 253.33 | 3800 | 0.8461 | 0.6730 | 0.673 |
| 0.2521 | 266.67 | 4000 | 0.8696 | 0.6776 | 0.678 |
| 0.2461 | 280.0 | 4200 | 0.8740 | 0.6739 | 0.674 |
| 0.2383 | 293.33 | 4400 | 0.9173 | 0.6850 | 0.685 |
| 0.2307 | 306.67 | 4600 | 0.9165 | 0.6779 | 0.678 |
| 0.2255 | 320.0 | 4800 | 0.9309 | 0.6857 | 0.686 |
| 0.2192 | 333.33 | 5000 | 0.9353 | 0.6709 | 0.671 |
| 0.2138 | 346.67 | 5200 | 0.9088 | 0.6780 | 0.678 |
| 0.2083 | 360.0 | 5400 | 0.9699 | 0.6704 | 0.671 |
| 0.2018 | 373.33 | 5600 | 0.9811 | 0.6769 | 0.677 |
| 0.1975 | 386.67 | 5800 | 0.9467 | 0.6687 | 0.669 |
| 0.1925 | 400.0 | 6000 | 0.9813 | 0.6755 | 0.676 |
| 0.1886 | 413.33 | 6200 | 0.9830 | 0.6779 | 0.678 |
| 0.184 | 426.67 | 6400 | 0.9905 | 0.6770 | 0.677 |
| 0.1806 | 440.0 | 6600 | 1.0004 | 0.6721 | 0.673 |
| 0.1771 | 453.33 | 6800 | 1.0257 | 0.6809 | 0.681 |
| 0.1726 | 466.67 | 7000 | 1.0673 | 0.6677 | 0.668 |
| 0.1702 | 480.0 | 7200 | 1.0637 | 0.6689 | 0.669 |
| 0.1674 | 493.33 | 7400 | 1.0590 | 0.6670 | 0.667 |
| 0.1655 | 506.67 | 7600 | 1.0730 | 0.6680 | 0.668 |
| 0.1629 | 520.0 | 7800 | 1.0953 | 0.6730 | 0.673 |
| 0.1594 | 533.33 | 8000 | 1.0809 | 0.6679 | 0.668 |
| 0.1588 | 546.67 | 8200 | 1.0749 | 0.6650 | 0.665 |
| 0.1565 | 560.0 | 8400 | 1.0858 | 0.6709 | 0.671 |
| 0.1543 | 573.33 | 8600 | 1.1003 | 0.6650 | 0.665 |
| 0.1528 | 586.67 | 8800 | 1.0985 | 0.6680 | 0.668 |
| 0.1504 | 600.0 | 9000 | 1.1135 | 0.6670 | 0.667 |
| 0.1502 | 613.33 | 9200 | 1.1064 | 0.6669 | 0.667 |
| 0.1491 | 626.67 | 9400 | 1.1020 | 0.6678 | 0.668 |
| 0.1492 | 640.0 | 9600 | 1.1107 | 0.6670 | 0.667 |
| 0.1482 | 653.33 | 9800 | 1.1083 | 0.6690 | 0.669 |
| 0.1475 | 666.67 | 10000 | 1.1123 | 0.6630 | 0.663 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_8192_512_30M", "model-index": [{"name": "GUE_tf_1-seqsight_8192_512_30M-L32_all", "results": []}]} | mahdibaghbanzadeh/GUE_tf_1-seqsight_8192_512_30M-L32_all | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_8192_512_30M",
"region:us"
] | null | 2024-04-16T05:58:13+00:00 | [] | [] | TAGS
#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_8192_512_30M #region-us
| GUE\_tf\_1-seqsight\_8192\_512\_30M-L32\_all
============================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_8192\_512\_30M on the mahdibaghbanzadeh/GUE\_tf\_1 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5174
* F1 Score: 0.7418
* Accuracy: 0.746
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0005
* train\_batch\_size: 2048
* eval\_batch\_size: 2048
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 10000
### Training results
### Framework versions
* PEFT 0.9.0
* Transformers 4.38.2
* Pytorch 2.2.0+cu121
* Datasets 2.17.1
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 2048\n* eval\\_batch\\_size: 2048\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
"### Training results",
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] | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 2048\n* eval\\_batch\\_size: 2048\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2"
] |
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. -->
# DataShield
This model is a fine-tuned version of [TheBloke/Mistral-7B-Instruct-v0.2-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GPTQ) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0984
## 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: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.6923 | 0.92 | 3 | 1.5242 |
| 1.6311 | 1.85 | 6 | 1.4465 |
| 1.535 | 2.77 | 9 | 1.3561 |
| 1.0871 | 4.0 | 13 | 1.2616 |
| 1.3893 | 4.92 | 16 | 1.2132 |
| 1.3384 | 5.85 | 19 | 1.1729 |
| 1.2877 | 6.77 | 22 | 1.1437 |
| 0.9564 | 8.0 | 26 | 1.1171 |
| 1.2534 | 8.92 | 29 | 1.1011 |
| 0.8827 | 9.23 | 30 | 1.0984 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 | {"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "TheBloke/Mistral-7B-Instruct-v0.2-GPTQ", "model-index": [{"name": "DataShield", "results": []}]} | rnaveensrinivas/DataShield | null | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:TheBloke/Mistral-7B-Instruct-v0.2-GPTQ",
"license:apache-2.0",
"region:us"
] | null | 2024-04-16T05:58:18+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #generated_from_trainer #base_model-TheBloke/Mistral-7B-Instruct-v0.2-GPTQ #license-apache-2.0 #region-us
| DataShield
==========
This model is a fine-tuned version of TheBloke/Mistral-7B-Instruct-v0.2-GPTQ on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0984
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: 4
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 2
* num\_epochs: 10
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* PEFT 0.10.0
* Transformers 4.38.2
* Pytorch 2.1.0+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: 0.0002\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 2\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.10.0\n* Transformers 4.38.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] | [
"TAGS\n#peft #tensorboard #safetensors #generated_from_trainer #base_model-TheBloke/Mistral-7B-Instruct-v0.2-GPTQ #license-apache-2.0 #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: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 2\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.10.0\n* Transformers 4.38.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] |
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. -->
# GUE_tf_4-seqsight_8192_512_30M-L32_all
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_8192_512_30M](https://huggingface.co/mahdibaghbanzadeh/seqsight_8192_512_30M) on the [mahdibaghbanzadeh/GUE_tf_4](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_4) dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1072
- F1 Score: 0.6985
- Accuracy: 0.7
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 2048
- eval_batch_size: 2048
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:|
| 0.5938 | 20.0 | 200 | 0.5839 | 0.6927 | 0.696 |
| 0.4624 | 40.0 | 400 | 0.5618 | 0.7407 | 0.741 |
| 0.3879 | 60.0 | 600 | 0.5554 | 0.7666 | 0.767 |
| 0.3327 | 80.0 | 800 | 0.5816 | 0.7678 | 0.771 |
| 0.2946 | 100.0 | 1000 | 0.5931 | 0.7744 | 0.776 |
| 0.2647 | 120.0 | 1200 | 0.5808 | 0.7855 | 0.787 |
| 0.2412 | 140.0 | 1400 | 0.6176 | 0.7794 | 0.781 |
| 0.2206 | 160.0 | 1600 | 0.6405 | 0.7669 | 0.77 |
| 0.2049 | 180.0 | 1800 | 0.6688 | 0.7695 | 0.772 |
| 0.1907 | 200.0 | 2000 | 0.6833 | 0.7732 | 0.775 |
| 0.1827 | 220.0 | 2200 | 0.6694 | 0.7772 | 0.779 |
| 0.1707 | 240.0 | 2400 | 0.7068 | 0.7844 | 0.786 |
| 0.1623 | 260.0 | 2600 | 0.6585 | 0.7922 | 0.793 |
| 0.1527 | 280.0 | 2800 | 0.7206 | 0.7775 | 0.78 |
| 0.1459 | 300.0 | 3000 | 0.7293 | 0.7797 | 0.782 |
| 0.1402 | 320.0 | 3200 | 0.6942 | 0.7992 | 0.8 |
| 0.1342 | 340.0 | 3400 | 0.7153 | 0.7863 | 0.788 |
| 0.1307 | 360.0 | 3600 | 0.7720 | 0.7765 | 0.779 |
| 0.1232 | 380.0 | 3800 | 0.7279 | 0.7822 | 0.784 |
| 0.1181 | 400.0 | 4000 | 0.7732 | 0.7808 | 0.783 |
| 0.1138 | 420.0 | 4200 | 0.7846 | 0.7840 | 0.786 |
| 0.1092 | 440.0 | 4400 | 0.7541 | 0.7829 | 0.785 |
| 0.1072 | 460.0 | 4600 | 0.7809 | 0.7938 | 0.796 |
| 0.102 | 480.0 | 4800 | 0.7725 | 0.7924 | 0.794 |
| 0.0999 | 500.0 | 5000 | 0.7435 | 0.7949 | 0.796 |
| 0.0964 | 520.0 | 5200 | 0.7584 | 0.7758 | 0.778 |
| 0.0933 | 540.0 | 5400 | 0.7664 | 0.7843 | 0.786 |
| 0.0899 | 560.0 | 5600 | 0.8301 | 0.7762 | 0.779 |
| 0.0883 | 580.0 | 5800 | 0.7747 | 0.7928 | 0.794 |
| 0.0857 | 600.0 | 6000 | 0.7789 | 0.7941 | 0.795 |
| 0.0847 | 620.0 | 6200 | 0.7575 | 0.7899 | 0.791 |
| 0.0822 | 640.0 | 6400 | 0.7835 | 0.7949 | 0.796 |
| 0.0781 | 660.0 | 6600 | 0.8146 | 0.7873 | 0.789 |
| 0.0774 | 680.0 | 6800 | 0.8272 | 0.7817 | 0.784 |
| 0.0749 | 700.0 | 7000 | 0.8346 | 0.7940 | 0.795 |
| 0.0741 | 720.0 | 7200 | 0.8273 | 0.7859 | 0.788 |
| 0.0726 | 740.0 | 7400 | 0.8139 | 0.7902 | 0.792 |
| 0.0712 | 760.0 | 7600 | 0.8389 | 0.7893 | 0.791 |
| 0.0689 | 780.0 | 7800 | 0.8566 | 0.7893 | 0.791 |
| 0.0686 | 800.0 | 8000 | 0.8251 | 0.7977 | 0.799 |
| 0.067 | 820.0 | 8200 | 0.8071 | 0.7884 | 0.79 |
| 0.0662 | 840.0 | 8400 | 0.8441 | 0.7874 | 0.789 |
| 0.0646 | 860.0 | 8600 | 0.8219 | 0.7937 | 0.795 |
| 0.0633 | 880.0 | 8800 | 0.8501 | 0.7894 | 0.791 |
| 0.0634 | 900.0 | 9000 | 0.8174 | 0.7862 | 0.788 |
| 0.0628 | 920.0 | 9200 | 0.8389 | 0.7884 | 0.79 |
| 0.0619 | 940.0 | 9400 | 0.8552 | 0.7861 | 0.788 |
| 0.0606 | 960.0 | 9600 | 0.8563 | 0.7891 | 0.791 |
| 0.0617 | 980.0 | 9800 | 0.8554 | 0.7862 | 0.788 |
| 0.0607 | 1000.0 | 10000 | 0.8497 | 0.7863 | 0.788 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_8192_512_30M", "model-index": [{"name": "GUE_tf_4-seqsight_8192_512_30M-L32_all", "results": []}]} | mahdibaghbanzadeh/GUE_tf_4-seqsight_8192_512_30M-L32_all | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_8192_512_30M",
"region:us"
] | null | 2024-04-16T05:58:52+00:00 | [] | [] | TAGS
#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_8192_512_30M #region-us
| GUE\_tf\_4-seqsight\_8192\_512\_30M-L32\_all
============================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_8192\_512\_30M on the mahdibaghbanzadeh/GUE\_tf\_4 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1072
* F1 Score: 0.6985
* Accuracy: 0.7
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0005
* train\_batch\_size: 2048
* eval\_batch\_size: 2048
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 10000
### Training results
### Framework versions
* PEFT 0.9.0
* Transformers 4.38.2
* Pytorch 2.2.0+cu121
* Datasets 2.17.1
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 2048\n* eval\\_batch\\_size: 2048\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
"### Training results",
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 2048\n* eval\\_batch\\_size: 2048\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2"
] |
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]
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## Uses
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### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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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]
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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. -->
[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]
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[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]
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[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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[More Information Needed]
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[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"} | Charishma27/sft_mistral_709_steps | null | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"region:us"
] | null | 2024-04-16T06:01:08+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"
] |
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. -->
# GUE_tf_3-seqsight_8192_512_30M-L32_all
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_8192_512_30M](https://huggingface.co/mahdibaghbanzadeh/seqsight_8192_512_30M) on the [mahdibaghbanzadeh/GUE_tf_3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_3) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6945
- F1 Score: 0.6306
- Accuracy: 0.634
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 2048
- eval_batch_size: 2048
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:|
| 0.6652 | 14.29 | 200 | 0.6261 | 0.6351 | 0.647 |
| 0.6027 | 28.57 | 400 | 0.6331 | 0.6487 | 0.655 |
| 0.5569 | 42.86 | 600 | 0.6640 | 0.6571 | 0.657 |
| 0.5209 | 57.14 | 800 | 0.6659 | 0.6667 | 0.667 |
| 0.494 | 71.43 | 1000 | 0.7023 | 0.6501 | 0.65 |
| 0.4694 | 85.71 | 1200 | 0.7381 | 0.646 | 0.646 |
| 0.452 | 100.0 | 1400 | 0.7667 | 0.6200 | 0.622 |
| 0.4332 | 114.29 | 1600 | 0.7595 | 0.6270 | 0.627 |
| 0.4193 | 128.57 | 1800 | 0.7789 | 0.6348 | 0.635 |
| 0.405 | 142.86 | 2000 | 0.7961 | 0.6230 | 0.623 |
| 0.393 | 157.14 | 2200 | 0.8005 | 0.6279 | 0.628 |
| 0.3814 | 171.43 | 2400 | 0.9150 | 0.6064 | 0.608 |
| 0.3679 | 185.71 | 2600 | 0.8467 | 0.6221 | 0.622 |
| 0.3581 | 200.0 | 2800 | 0.8222 | 0.6150 | 0.616 |
| 0.3458 | 214.29 | 3000 | 0.8990 | 0.616 | 0.616 |
| 0.3343 | 228.57 | 3200 | 0.9159 | 0.6185 | 0.619 |
| 0.3241 | 242.86 | 3400 | 0.9124 | 0.6011 | 0.601 |
| 0.3145 | 257.14 | 3600 | 0.9340 | 0.6141 | 0.614 |
| 0.3054 | 271.43 | 3800 | 0.9421 | 0.6161 | 0.618 |
| 0.2955 | 285.71 | 4000 | 0.9610 | 0.6050 | 0.605 |
| 0.2851 | 300.0 | 4200 | 0.9503 | 0.6132 | 0.614 |
| 0.2787 | 314.29 | 4400 | 0.9691 | 0.6088 | 0.609 |
| 0.2713 | 328.57 | 4600 | 0.9770 | 0.6107 | 0.611 |
| 0.2643 | 342.86 | 4800 | 1.0160 | 0.5997 | 0.6 |
| 0.2568 | 357.14 | 5000 | 1.0290 | 0.6181 | 0.618 |
| 0.2495 | 371.43 | 5200 | 1.0194 | 0.6058 | 0.606 |
| 0.2435 | 385.71 | 5400 | 1.0307 | 0.6058 | 0.606 |
| 0.2382 | 400.0 | 5600 | 1.0560 | 0.6014 | 0.602 |
| 0.2318 | 414.29 | 5800 | 1.0271 | 0.6011 | 0.601 |
| 0.2279 | 428.57 | 6000 | 1.0710 | 0.6041 | 0.604 |
| 0.2202 | 442.86 | 6200 | 1.1111 | 0.5997 | 0.6 |
| 0.218 | 457.14 | 6400 | 1.0763 | 0.6051 | 0.605 |
| 0.2131 | 471.43 | 6600 | 1.0867 | 0.6120 | 0.612 |
| 0.2079 | 485.71 | 6800 | 1.1044 | 0.6080 | 0.608 |
| 0.2051 | 500.0 | 7000 | 1.0884 | 0.6141 | 0.614 |
| 0.2003 | 514.29 | 7200 | 1.1269 | 0.6081 | 0.608 |
| 0.1964 | 528.57 | 7400 | 1.1436 | 0.6058 | 0.606 |
| 0.1954 | 542.86 | 7600 | 1.1151 | 0.6030 | 0.603 |
| 0.1917 | 557.14 | 7800 | 1.1323 | 0.6081 | 0.608 |
| 0.1886 | 571.43 | 8000 | 1.1501 | 0.5968 | 0.597 |
| 0.1874 | 585.71 | 8200 | 1.1396 | 0.6041 | 0.604 |
| 0.1845 | 600.0 | 8400 | 1.1702 | 0.6050 | 0.605 |
| 0.1821 | 614.29 | 8600 | 1.1690 | 0.6031 | 0.603 |
| 0.1804 | 628.57 | 8800 | 1.1632 | 0.5978 | 0.598 |
| 0.1786 | 642.86 | 9000 | 1.1731 | 0.6009 | 0.601 |
| 0.1776 | 657.14 | 9200 | 1.1736 | 0.6030 | 0.603 |
| 0.177 | 671.43 | 9400 | 1.1712 | 0.5960 | 0.596 |
| 0.1747 | 685.71 | 9600 | 1.1700 | 0.6050 | 0.605 |
| 0.1731 | 700.0 | 9800 | 1.1720 | 0.5990 | 0.599 |
| 0.1742 | 714.29 | 10000 | 1.1726 | 0.6000 | 0.6 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_8192_512_30M", "model-index": [{"name": "GUE_tf_3-seqsight_8192_512_30M-L32_all", "results": []}]} | mahdibaghbanzadeh/GUE_tf_3-seqsight_8192_512_30M-L32_all | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_8192_512_30M",
"region:us"
] | null | 2024-04-16T06:01:15+00:00 | [] | [] | TAGS
#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_8192_512_30M #region-us
| GUE\_tf\_3-seqsight\_8192\_512\_30M-L32\_all
============================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_8192\_512\_30M on the mahdibaghbanzadeh/GUE\_tf\_3 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6945
* F1 Score: 0.6306
* Accuracy: 0.634
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0005
* train\_batch\_size: 2048
* eval\_batch\_size: 2048
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 10000
### Training results
### Framework versions
* PEFT 0.9.0
* Transformers 4.38.2
* Pytorch 2.2.0+cu121
* Datasets 2.17.1
* Tokenizers 0.15.2
| [
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] |
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. -->
# T5-mask-100-beam-3
This model is a fine-tuned version of [mrm8488/t5-base-finetuned-common_gen](https://huggingface.co/mrm8488/t5-base-finetuned-common_gen) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6461
- Bleu: 5.5434
- Gen Len: 14.3534
## 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: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|
| 2.256 | 1.0 | 527 | 2.2936 | 6.9789 | 13.1364 |
| 2.2149 | 2.0 | 1054 | 2.2944 | 7.0421 | 13.1366 |
| 2.1975 | 3.0 | 1581 | 2.3005 | 6.9363 | 13.2412 |
| 2.1766 | 4.0 | 2108 | 2.3055 | 6.8015 | 13.2558 |
| 2.1635 | 5.0 | 2635 | 2.3066 | 6.9031 | 13.2852 |
| 2.145 | 6.0 | 3162 | 2.3105 | 6.7477 | 13.4291 |
| 2.1322 | 7.0 | 3689 | 2.3164 | 6.9102 | 13.3454 |
| 2.1147 | 8.0 | 4216 | 2.3218 | 6.7552 | 13.4181 |
| 2.1079 | 9.0 | 4743 | 2.3247 | 6.8419 | 13.4602 |
| 2.0914 | 10.0 | 5270 | 2.3329 | 6.751 | 13.4266 |
| 2.0803 | 11.0 | 5797 | 2.3354 | 6.6713 | 13.5381 |
| 2.0675 | 12.0 | 6324 | 2.3379 | 6.7464 | 13.4975 |
| 2.0565 | 13.0 | 6851 | 2.3399 | 6.7349 | 13.5582 |
| 2.0459 | 14.0 | 7378 | 2.3443 | 6.7243 | 13.5358 |
| 2.0351 | 15.0 | 7905 | 2.3470 | 6.7024 | 13.6242 |
| 2.0246 | 16.0 | 8432 | 2.3563 | 6.6921 | 13.5607 |
| 2.016 | 17.0 | 8959 | 2.3528 | 6.7559 | 13.6692 |
| 2.0053 | 18.0 | 9486 | 2.3603 | 6.8006 | 13.5881 |
| 1.9859 | 19.0 | 10013 | 2.3608 | 6.8255 | 13.7096 |
| 1.975 | 20.0 | 10540 | 2.3695 | 6.7947 | 13.6324 |
| 1.9674 | 21.0 | 11067 | 2.3731 | 6.8131 | 13.6732 |
| 1.9582 | 22.0 | 11594 | 2.3766 | 6.7819 | 13.7409 |
| 1.9483 | 23.0 | 12121 | 2.3754 | 6.8787 | 13.5938 |
| 1.9443 | 24.0 | 12648 | 2.3836 | 6.6645 | 13.6747 |
| 1.9337 | 25.0 | 13175 | 2.3865 | 6.7016 | 13.7514 |
| 1.9265 | 26.0 | 13702 | 2.3891 | 6.8102 | 13.7718 |
| 1.9184 | 27.0 | 14229 | 2.3962 | 6.7632 | 13.7377 |
| 1.9134 | 28.0 | 14756 | 2.3994 | 6.7438 | 13.8203 |
| 1.9027 | 29.0 | 15283 | 2.4079 | 6.6669 | 13.7855 |
| 1.901 | 30.0 | 15810 | 2.4085 | 6.7555 | 13.7292 |
| 1.8915 | 31.0 | 16337 | 2.4070 | 6.8025 | 13.7606 |
| 1.8841 | 32.0 | 16864 | 2.4078 | 6.769 | 13.828 |
| 1.8794 | 33.0 | 17391 | 2.4088 | 6.7529 | 13.825 |
| 1.8703 | 34.0 | 17918 | 2.4148 | 6.7795 | 13.8596 |
| 1.8651 | 35.0 | 18445 | 2.4122 | 6.7422 | 13.8233 |
| 1.8597 | 36.0 | 18972 | 2.4071 | 6.7784 | 13.8395 |
| 1.8568 | 37.0 | 19499 | 2.4106 | 6.7127 | 13.8599 |
| 1.8436 | 38.0 | 20026 | 2.4177 | 6.8216 | 13.8977 |
| 1.8386 | 39.0 | 20553 | 2.4212 | 6.72 | 13.8596 |
| 1.843 | 40.0 | 21080 | 2.3578 | 6.7825 | 13.7315 |
| 1.8861 | 41.0 | 21607 | 2.3585 | 6.7195 | 13.5811 |
| 1.9214 | 42.0 | 22134 | 2.3743 | 6.7537 | 13.7451 |
| 2.0399 | 43.0 | 22661 | 2.5768 | 5.1918 | 13.6165 |
| 2.2339 | 44.0 | 23188 | 2.5552 | 5.2251 | 13.7357 |
| 2.2102 | 45.0 | 23715 | 2.5288 | 5.2795 | 13.8405 |
| 2.1798 | 46.0 | 24242 | 2.5107 | 5.4188 | 13.9622 |
| 2.1667 | 47.0 | 24769 | 2.4992 | 5.4951 | 14.0577 |
| 2.1463 | 48.0 | 25296 | 2.4904 | 5.5393 | 14.1063 |
| 2.1284 | 49.0 | 25823 | 2.4842 | 5.6771 | 14.1812 |
| 2.1142 | 50.0 | 26350 | 2.4803 | 5.6807 | 14.3044 |
| 2.1067 | 51.0 | 26877 | 2.4775 | 5.7383 | 14.3387 |
| 2.0961 | 52.0 | 27404 | 2.4767 | 5.7043 | 14.3579 |
| 2.0891 | 53.0 | 27931 | 2.4771 | 5.7167 | 14.3853 |
| 2.0853 | 54.0 | 28458 | 2.4780 | 5.7627 | 14.4191 |
| 2.0783 | 55.0 | 28985 | 2.4774 | 5.7501 | 14.4121 |
| 2.0744 | 56.0 | 29512 | 2.4825 | 5.6738 | 14.3785 |
| 2.0746 | 57.0 | 30039 | 2.4889 | 5.6481 | 14.3435 |
| 2.0763 | 58.0 | 30566 | 2.4937 | 5.6288 | 14.3298 |
| 2.0696 | 59.0 | 31093 | 2.4985 | 5.6343 | 14.3293 |
| 2.0714 | 60.0 | 31620 | 2.5013 | 5.6636 | 14.3596 |
| 2.0706 | 61.0 | 32147 | 2.5043 | 5.6589 | 14.3544 |
| 2.065 | 62.0 | 32674 | 2.5072 | 5.6727 | 14.3691 |
| 2.0662 | 63.0 | 33201 | 2.5099 | 5.6883 | 14.3962 |
| 2.0653 | 64.0 | 33728 | 2.5170 | 5.6343 | 14.3604 |
| 2.0679 | 65.0 | 34255 | 2.5239 | 5.604 | 14.3328 |
| 2.0738 | 66.0 | 34782 | 2.5295 | 5.5741 | 14.3064 |
| 2.0741 | 67.0 | 35309 | 2.5347 | 5.5617 | 14.283 |
| 2.0717 | 68.0 | 35836 | 2.5392 | 5.5388 | 14.3044 |
| 2.0693 | 69.0 | 36363 | 2.5437 | 5.5111 | 14.2927 |
| 2.0739 | 70.0 | 36890 | 2.5479 | 5.5074 | 14.2651 |
| 2.074 | 71.0 | 37417 | 2.5554 | 5.4703 | 14.2598 |
| 2.0796 | 72.0 | 37944 | 2.5651 | 5.4628 | 14.2439 |
| 2.0775 | 73.0 | 38471 | 2.5742 | 5.4606 | 14.2668 |
| 2.0827 | 74.0 | 38998 | 2.5827 | 5.4494 | 14.2367 |
| 2.0928 | 75.0 | 39525 | 2.5906 | 5.4626 | 14.226 |
| 2.0995 | 76.0 | 40052 | 2.5979 | 5.4589 | 14.269 |
| 2.0984 | 77.0 | 40579 | 2.6057 | 5.4754 | 14.282 |
| 2.1017 | 78.0 | 41106 | 2.6138 | 5.5446 | 14.3079 |
| 2.1098 | 79.0 | 41633 | 2.6217 | 5.5664 | 14.3081 |
| 2.1164 | 80.0 | 42160 | 2.6296 | 5.5431 | 14.3285 |
| 2.118 | 81.0 | 42687 | 2.6369 | 5.5365 | 14.3342 |
| 2.1227 | 82.0 | 43214 | 2.6440 | 5.5201 | 14.3589 |
| 2.1291 | 83.0 | 43741 | 2.6463 | 5.5251 | 14.3654 |
| 2.125 | 84.0 | 44268 | 2.6462 | 5.5234 | 14.3736 |
| 2.1288 | 85.0 | 44795 | 2.6461 | 5.5387 | 14.3532 |
| 2.1266 | 86.0 | 45322 | 2.6461 | 5.5434 | 14.3534 |
| 2.1269 | 87.0 | 45849 | 2.6461 | 5.5434 | 14.3534 |
| 2.1301 | 88.0 | 46376 | 2.6461 | 5.5434 | 14.3534 |
| 2.1279 | 89.0 | 46903 | 2.6461 | 5.5434 | 14.3534 |
| 2.1267 | 90.0 | 47430 | 2.6461 | 5.5434 | 14.3534 |
| 2.1259 | 91.0 | 47957 | 2.6461 | 5.5434 | 14.3534 |
| 2.1281 | 92.0 | 48484 | 2.6461 | 5.5434 | 14.3534 |
| 2.1288 | 93.0 | 49011 | 2.6461 | 5.5434 | 14.3534 |
| 2.1263 | 94.0 | 49538 | 2.6461 | 5.5434 | 14.3534 |
| 2.1288 | 95.0 | 50065 | 2.6461 | 5.5434 | 14.3534 |
| 2.1264 | 96.0 | 50592 | 2.6461 | 5.5434 | 14.3534 |
| 2.127 | 97.0 | 51119 | 2.6461 | 5.5434 | 14.3534 |
| 2.1271 | 98.0 | 51646 | 2.6461 | 5.5434 | 14.3534 |
| 2.1307 | 99.0 | 52173 | 2.6461 | 5.5434 | 14.3534 |
| 2.1246 | 100.0 | 52700 | 2.6461 | 5.5434 | 14.3534 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.2+cu118
- Datasets 2.18.0
- Tokenizers 0.15.1
| {"tags": ["generated_from_trainer"], "metrics": ["bleu"], "base_model": "mrm8488/t5-base-finetuned-common_gen", "model-index": [{"name": "T5-mask-100-beam-3", "results": []}]} | Ziyi98/T5-mask-100-beam-3 | null | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:mrm8488/t5-base-finetuned-common_gen",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-16T06:01:17+00:00 | [] | [] | TAGS
#transformers #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-mrm8488/t5-base-finetuned-common_gen #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| T5-mask-100-beam-3
==================
This model is a fine-tuned version of mrm8488/t5-base-finetuned-common\_gen on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.6461
* Bleu: 5.5434
* Gen Len: 14.3534
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: 128
* eval\_batch\_size: 128
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 100
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.37.2
* Pytorch 2.2.2+cu118
* Datasets 2.18.0
* Tokenizers 0.15.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 100\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.2+cu118\n* Datasets 2.18.0\n* Tokenizers 0.15.1"
] | [
"TAGS\n#transformers #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-mrm8488/t5-base-finetuned-common_gen #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 100\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.2+cu118\n* Datasets 2.18.0\n* Tokenizers 0.15.1"
] |
text2text-generation | transformers | # Model Card for Model ID
This model is a fine-tuned version of mt5-small-finetuned-wikisql on an wikisql dataset
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
. It achieves the following results on the evaluation set:
### Model Description
Finetuned from model :MT5
### Results
Training Loss: 0.55
Validation Loss: 0.47
Bleu: 42.53
Gen Len : 16.32
[More Information Needed]
#### Summary
| {"license": "apache-2.0", "datasets": ["wikisql"], "metrics": ["bleu"]} | Akki-off/mt5-small-finetuned-wikisql2_v1 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"dataset:wikisql",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-16T06:01:29+00:00 | [] | [] | TAGS
#transformers #pytorch #mt5 #text2text-generation #dataset-wikisql #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Model Card for Model ID
This model is a fine-tuned version of mt5-small-finetuned-wikisql on an wikisql dataset
This modelcard aims to be a base template for new models. It has been generated using this raw template.
## Model Details
. It achieves the following results on the evaluation set:
### Model Description
Finetuned from model :MT5
### Results
Training Loss: 0.55
Validation Loss: 0.47
Bleu: 42.53
Gen Len : 16.32
#### Summary
| [
"# Model Card for Model ID\n\nThis model is a fine-tuned version of mt5-small-finetuned-wikisql on an wikisql dataset\n\n\n\nThis modelcard aims to be a base template for new models. It has been generated using this raw template.",
"## Model Details\n. It achieves the following results on the evaluation set:",
"### Model Description\n\n\nFinetuned from model :MT5",
"### Results\nTraining Loss: 0.55\nValidation Loss: 0.47\nBleu: 42.53\nGen Len : 16.32",
"#### Summary"
] | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #dataset-wikisql #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Model ID\n\nThis model is a fine-tuned version of mt5-small-finetuned-wikisql on an wikisql dataset\n\n\n\nThis modelcard aims to be a base template for new models. It has been generated using this raw template.",
"## Model Details\n. It achieves the following results on the evaluation set:",
"### Model Description\n\n\nFinetuned from model :MT5",
"### Results\nTraining Loss: 0.55\nValidation Loss: 0.47\nBleu: 42.53\nGen Len : 16.32",
"#### Summary"
] |
text-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. -->
# biomistral-7b-wo-kqa_silver_wogold-sft
This model is a fine-tuned version of [BioMistral/BioMistral-7B](https://huggingface.co/BioMistral/BioMistral-7B) on the HuggingFaceH4/deita-10k-v0-sft dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8051
## 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: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.3601 | 0.91 | 5 | 1.1555 |
| 1.0001 | 2.0 | 11 | 0.8497 |
| 0.7357 | 2.73 | 15 | 0.8051 |
### Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.1.2
- Datasets 2.14.6
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["alignment-handbook", "trl", "sft", "generated_from_trainer", "trl", "sft", "generated_from_trainer"], "datasets": ["HuggingFaceH4/deita-10k-v0-sft"], "base_model": "BioMistral/BioMistral-7B", "model-index": [{"name": "biomistral-7b-wo-kqa_silver_wogold-sft", "results": []}]} | Minbyul/biomistral-7b-wo-kqa_silver_wogold-sft | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"trl",
"sft",
"generated_from_trainer",
"conversational",
"dataset:HuggingFaceH4/deita-10k-v0-sft",
"base_model:BioMistral/BioMistral-7B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-16T06:02:18+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #alignment-handbook #trl #sft #generated_from_trainer #conversational #dataset-HuggingFaceH4/deita-10k-v0-sft #base_model-BioMistral/BioMistral-7B #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| biomistral-7b-wo-kqa\_silver\_wogold-sft
========================================
This model is a fine-tuned version of BioMistral/BioMistral-7B on the HuggingFaceH4/deita-10k-v0-sft dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8051
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: 4
* eval\_batch\_size: 4
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 4
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* total\_eval\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.39.0.dev0
* Pytorch 2.1.2
* Datasets 2.14.6
* 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: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 4\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* total\\_eval\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.39.0.dev0\n* Pytorch 2.1.2\n* Datasets 2.14.6\n* Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #alignment-handbook #trl #sft #generated_from_trainer #conversational #dataset-HuggingFaceH4/deita-10k-v0-sft #base_model-BioMistral/BioMistral-7B #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 4\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* total\\_eval\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.39.0.dev0\n* Pytorch 2.1.2\n* Datasets 2.14.6\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": "273.38 +/- 11.79", "name": "mean_reward", "verified": false}]}]}]} | ChakuChidiya/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2024-04-16T06:02:43+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 | null |
# DavidAU/roleplay-model-v2-Q6_K-GGUF
This model was converted to GGUF format from [`WendyHoang/roleplay-model-v2`](https://huggingface.co/WendyHoang/roleplay-model-v2) 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/WendyHoang/roleplay-model-v2) 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 DavidAU/roleplay-model-v2-Q6_K-GGUF --model roleplay-model-v2.Q6_K.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo DavidAU/roleplay-model-v2-Q6_K-GGUF --model roleplay-model-v2.Q6_K.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m roleplay-model-v2.Q6_K.gguf -n 128
```
| {"license": "cc-by-nc-4.0", "tags": ["not-for-all-audiences", "nsfw", "mistral", "pretrained", "llama-cpp", "gguf-my-repo"]} | DavidAU/roleplay-model-v2-Q6_K-GGUF | null | [
"gguf",
"not-for-all-audiences",
"nsfw",
"mistral",
"pretrained",
"llama-cpp",
"gguf-my-repo",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2024-04-16T06:06:32+00:00 | [] | [] | TAGS
#gguf #not-for-all-audiences #nsfw #mistral #pretrained #llama-cpp #gguf-my-repo #license-cc-by-nc-4.0 #region-us
|
# DavidAU/roleplay-model-v2-Q6_K-GGUF
This model was converted to GGUF format from 'WendyHoang/roleplay-model-v2' 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.
| [
"# DavidAU/roleplay-model-v2-Q6_K-GGUF\nThis model was converted to GGUF format from 'WendyHoang/roleplay-model-v2' 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 #not-for-all-audiences #nsfw #mistral #pretrained #llama-cpp #gguf-my-repo #license-cc-by-nc-4.0 #region-us \n",
"# DavidAU/roleplay-model-v2-Q6_K-GGUF\nThis model was converted to GGUF format from 'WendyHoang/roleplay-model-v2' 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 | 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. -->
# GUE_tf_2-seqsight_8192_512_30M-L32_all
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_8192_512_30M](https://huggingface.co/mahdibaghbanzadeh/seqsight_8192_512_30M) on the [mahdibaghbanzadeh/GUE_tf_2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_tf_2) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6338
- F1 Score: 0.6884
- Accuracy: 0.689
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 1536
- eval_batch_size: 1536
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:|
| 0.63 | 15.38 | 200 | 0.6302 | 0.6461 | 0.647 |
| 0.5337 | 30.77 | 400 | 0.6630 | 0.6424 | 0.644 |
| 0.4737 | 46.15 | 600 | 0.6934 | 0.6516 | 0.654 |
| 0.4271 | 61.54 | 800 | 0.7213 | 0.6735 | 0.674 |
| 0.3909 | 76.92 | 1000 | 0.7608 | 0.6702 | 0.671 |
| 0.362 | 92.31 | 1200 | 0.7714 | 0.6624 | 0.663 |
| 0.3431 | 107.69 | 1400 | 0.8214 | 0.6710 | 0.671 |
| 0.3246 | 123.08 | 1600 | 0.8769 | 0.6568 | 0.657 |
| 0.3089 | 138.46 | 1800 | 0.8430 | 0.6725 | 0.673 |
| 0.2939 | 153.85 | 2000 | 0.9266 | 0.6689 | 0.669 |
| 0.2794 | 169.23 | 2200 | 0.9087 | 0.6697 | 0.67 |
| 0.2673 | 184.62 | 2400 | 0.9141 | 0.6609 | 0.661 |
| 0.2546 | 200.0 | 2600 | 0.9812 | 0.6516 | 0.652 |
| 0.245 | 215.38 | 2800 | 0.9577 | 0.6570 | 0.657 |
| 0.2333 | 230.77 | 3000 | 0.9936 | 0.6489 | 0.649 |
| 0.2256 | 246.15 | 3200 | 0.9704 | 0.6550 | 0.655 |
| 0.2166 | 261.54 | 3400 | 1.0434 | 0.6478 | 0.648 |
| 0.208 | 276.92 | 3600 | 1.0574 | 0.664 | 0.664 |
| 0.1987 | 292.31 | 3800 | 1.1171 | 0.6540 | 0.654 |
| 0.191 | 307.69 | 4000 | 1.0810 | 0.6529 | 0.653 |
| 0.1841 | 323.08 | 4200 | 1.0971 | 0.6434 | 0.645 |
| 0.1783 | 338.46 | 4400 | 1.1030 | 0.6538 | 0.654 |
| 0.1729 | 353.85 | 4600 | 1.0723 | 0.6549 | 0.655 |
| 0.1663 | 369.23 | 4800 | 1.1525 | 0.6540 | 0.654 |
| 0.1611 | 384.62 | 5000 | 1.1418 | 0.6589 | 0.659 |
| 0.156 | 400.0 | 5200 | 1.1778 | 0.6520 | 0.652 |
| 0.1516 | 415.38 | 5400 | 1.1558 | 0.6560 | 0.656 |
| 0.1481 | 430.77 | 5600 | 1.1824 | 0.6470 | 0.647 |
| 0.1441 | 446.15 | 5800 | 1.1839 | 0.6510 | 0.651 |
| 0.1399 | 461.54 | 6000 | 1.1635 | 0.6460 | 0.646 |
| 0.1354 | 476.92 | 6200 | 1.2265 | 0.6527 | 0.653 |
| 0.1324 | 492.31 | 6400 | 1.2001 | 0.6590 | 0.659 |
| 0.1304 | 507.69 | 6600 | 1.2135 | 0.6508 | 0.651 |
| 0.1257 | 523.08 | 6800 | 1.2496 | 0.6550 | 0.655 |
| 0.1236 | 538.46 | 7000 | 1.2449 | 0.6470 | 0.647 |
| 0.1205 | 553.85 | 7200 | 1.2688 | 0.6550 | 0.655 |
| 0.1188 | 569.23 | 7400 | 1.2710 | 0.6639 | 0.664 |
| 0.1157 | 584.62 | 7600 | 1.2893 | 0.6540 | 0.654 |
| 0.1135 | 600.0 | 7800 | 1.2557 | 0.6520 | 0.652 |
| 0.1117 | 615.38 | 8000 | 1.2621 | 0.6490 | 0.649 |
| 0.1097 | 630.77 | 8200 | 1.2867 | 0.6460 | 0.646 |
| 0.1081 | 646.15 | 8400 | 1.2929 | 0.6510 | 0.651 |
| 0.1077 | 661.54 | 8600 | 1.2848 | 0.6598 | 0.66 |
| 0.1061 | 676.92 | 8800 | 1.2900 | 0.6479 | 0.648 |
| 0.1043 | 692.31 | 9000 | 1.2882 | 0.648 | 0.648 |
| 0.1062 | 707.69 | 9200 | 1.2893 | 0.6560 | 0.656 |
| 0.1035 | 723.08 | 9400 | 1.3024 | 0.6560 | 0.656 |
| 0.1025 | 738.46 | 9600 | 1.2972 | 0.6620 | 0.662 |
| 0.1017 | 753.85 | 9800 | 1.3034 | 0.6580 | 0.658 |
| 0.1013 | 769.23 | 10000 | 1.3126 | 0.6560 | 0.656 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_8192_512_30M", "model-index": [{"name": "GUE_tf_2-seqsight_8192_512_30M-L32_all", "results": []}]} | mahdibaghbanzadeh/GUE_tf_2-seqsight_8192_512_30M-L32_all | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_8192_512_30M",
"region:us"
] | null | 2024-04-16T06:08:07+00:00 | [] | [] | TAGS
#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_8192_512_30M #region-us
| GUE\_tf\_2-seqsight\_8192\_512\_30M-L32\_all
============================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_8192\_512\_30M on the mahdibaghbanzadeh/GUE\_tf\_2 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6338
* F1 Score: 0.6884
* Accuracy: 0.689
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0005
* train\_batch\_size: 1536
* eval\_batch\_size: 1536
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 10000
### Training results
### Framework versions
* PEFT 0.9.0
* Transformers 4.38.2
* Pytorch 2.2.0+cu121
* Datasets 2.17.1
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 1536\n* eval\\_batch\\_size: 1536\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2"
] | [
"TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_8192_512_30M #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 1536\n* eval\\_batch\\_size: 1536\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2"
] |
null | transformers | ## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/tvkkishore/Inspire-7B-slerp
<!-- 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/Inspire-7B-slerp-GGUF/resolve/main/Inspire-7B-slerp.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/Inspire-7B-slerp-GGUF/resolve/main/Inspire-7B-slerp.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/Inspire-7B-slerp-GGUF/resolve/main/Inspire-7B-slerp.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Inspire-7B-slerp-GGUF/resolve/main/Inspire-7B-slerp.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Inspire-7B-slerp-GGUF/resolve/main/Inspire-7B-slerp.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Inspire-7B-slerp-GGUF/resolve/main/Inspire-7B-slerp.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Inspire-7B-slerp-GGUF/resolve/main/Inspire-7B-slerp.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Inspire-7B-slerp-GGUF/resolve/main/Inspire-7B-slerp.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Inspire-7B-slerp-GGUF/resolve/main/Inspire-7B-slerp.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Inspire-7B-slerp-GGUF/resolve/main/Inspire-7B-slerp.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Inspire-7B-slerp-GGUF/resolve/main/Inspire-7B-slerp.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/Inspire-7B-slerp-GGUF/resolve/main/Inspire-7B-slerp.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Inspire-7B-slerp-GGUF/resolve/main/Inspire-7B-slerp.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Inspire-7B-slerp-GGUF/resolve/main/Inspire-7B-slerp.Q8_0.gguf) | Q8_0 | 7.8 | 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"], "library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": "tvkkishore/Inspire-7B-slerp", "quantized_by": "mradermacher"} | mradermacher/Inspire-7B-slerp-GGUF | null | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:tvkkishore/Inspire-7B-slerp",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T06:08:27+00:00 | [] | [
"en"
] | TAGS
#transformers #gguf #mergekit #merge #en #base_model-tvkkishore/Inspire-7B-slerp #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 #mergekit #merge #en #base_model-tvkkishore/Inspire-7B-slerp #endpoints_compatible #region-us \n"
] |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **CartPole-v1**
This is a trained model of a **PPO** agent playing **CartPole-v1**
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": ["CartPole-v1", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"type": "mean_reward", "value": "483.70 +/- 35.69", "name": "mean_reward", "verified": false}]}]}]} | Sumegh20/ppo-cart-pole-sb3 | null | [
"stable-baselines3",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2024-04-16T06:08:31+00:00 | [] | [] | TAGS
#stable-baselines3 #CartPole-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing CartPole-v1
This is a trained model of a PPO agent playing CartPole-v1
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing CartPole-v1\nThis is a trained model of a PPO agent playing CartPole-v1\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #CartPole-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing CartPole-v1\nThis is a trained model of a PPO agent playing CartPole-v1\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] |
text-generation | transformers |
### Model Description
This model is used to generate the template based on the body of any emails or messages. It uses Microsoft's Phi-2 as the base model and was finetuned for 2 epochs on Google Colab's Tesla T4 GPU.
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:** Anupam Wagle
- **Model type:** Text Generation
- **Language(s) (NLP):** PyTorch
- **License:** MIT
- **Finetuned from model:** Microsoft Phi-2
## Uses
Use to generate the message based on the previous ones.
## Bias, Risks, and Limitations
For better results, increase the size of the dataset and the training epochs.
## Training Details
### Training Data
The format of the dataset used for finetuning is as follows:
[{
"input_email": "Hello Adam,\n\nCan you come to the party tonight after 6 PM?\nBest,\nSubash",
"generated_email": "Hi Eve,\n\nThank you for the invitation. I'd love to come to the party tonight after 6 PM. Looking forward to it!\n\nBest,\nAdam"
},
...]
## Technical Specifications
This model was finetuned on Google colab's Tesla t4 GPU for a total of 2 epochs.
### Model Architecture and Objective
The base model for this was the Microsoft's Phi-2 which was quantized using Bits and Bytes. It's primray objective is to generate messages based on previous messages.
| {"language": ["en"], "license": "mit", "library_name": "transformers"} | anupam413/phi2_qlora_emailGen_bitsandbytes | null | [
"transformers",
"safetensors",
"phi",
"text-generation",
"custom_code",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-04-16T06:11:37+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #phi #text-generation #custom_code #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us
|
### Model Description
This model is used to generate the template based on the body of any emails or messages. It uses Microsoft's Phi-2 as the base model and was finetuned for 2 epochs on Google Colab's Tesla T4 GPU.
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: Anupam Wagle
- Model type: Text Generation
- Language(s) (NLP): PyTorch
- License: MIT
- Finetuned from model: Microsoft Phi-2
## Uses
Use to generate the message based on the previous ones.
## Bias, Risks, and Limitations
For better results, increase the size of the dataset and the training epochs.
## Training Details
### Training Data
The format of the dataset used for finetuning is as follows:
[{
"input_email": "Hello Adam,\n\nCan you come to the party tonight after 6 PM?\nBest,\nSubash",
"generated_email": "Hi Eve,\n\nThank you for the invitation. I'd love to come to the party tonight after 6 PM. Looking forward to it!\n\nBest,\nAdam"
},
...]
## Technical Specifications
This model was finetuned on Google colab's Tesla t4 GPU for a total of 2 epochs.
### Model Architecture and Objective
The base model for this was the Microsoft's Phi-2 which was quantized using Bits and Bytes. It's primray objective is to generate messages based on previous messages.
| [
"### Model Description\n\nThis model is used to generate the template based on the body of any emails or messages. It uses Microsoft's Phi-2 as the base model and was finetuned for 2 epochs on Google Colab's Tesla T4 GPU.\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: Anupam Wagle\n- Model type: Text Generation\n- Language(s) (NLP): PyTorch\n- License: MIT\n- Finetuned from model: Microsoft Phi-2",
"## Uses\nUse to generate the message based on the previous ones.",
"## Bias, Risks, and Limitations\nFor better results, increase the size of the dataset and the training epochs.",
"## Training Details",
"### Training Data\n\nThe format of the dataset used for finetuning is as follows:\n[{\n\"input_email\": \"Hello Adam,\\n\\nCan you come to the party tonight after 6 PM?\\nBest,\\nSubash\",\n\"generated_email\": \"Hi Eve,\\n\\nThank you for the invitation. I'd love to come to the party tonight after 6 PM. Looking forward to it!\\n\\nBest,\\nAdam\"\n},\n...]",
"## Technical Specifications \nThis model was finetuned on Google colab's Tesla t4 GPU for a total of 2 epochs.",
"### Model Architecture and Objective\nThe base model for this was the Microsoft's Phi-2 which was quantized using Bits and Bytes. It's primray objective is to generate messages based on previous messages."
] | [
"TAGS\n#transformers #safetensors #phi #text-generation #custom_code #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n",
"### Model Description\n\nThis model is used to generate the template based on the body of any emails or messages. It uses Microsoft's Phi-2 as the base model and was finetuned for 2 epochs on Google Colab's Tesla T4 GPU.\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: Anupam Wagle\n- Model type: Text Generation\n- Language(s) (NLP): PyTorch\n- License: MIT\n- Finetuned from model: Microsoft Phi-2",
"## Uses\nUse to generate the message based on the previous ones.",
"## Bias, Risks, and Limitations\nFor better results, increase the size of the dataset and the training epochs.",
"## Training Details",
"### Training Data\n\nThe format of the dataset used for finetuning is as follows:\n[{\n\"input_email\": \"Hello Adam,\\n\\nCan you come to the party tonight after 6 PM?\\nBest,\\nSubash\",\n\"generated_email\": \"Hi Eve,\\n\\nThank you for the invitation. I'd love to come to the party tonight after 6 PM. Looking forward to it!\\n\\nBest,\\nAdam\"\n},\n...]",
"## Technical Specifications \nThis model was finetuned on Google colab's Tesla t4 GPU for a total of 2 epochs.",
"### Model Architecture and Objective\nThe base model for this was the Microsoft's Phi-2 which was quantized using Bits and Bytes. It's primray objective is to generate messages based on previous messages."
] |
null | transformers | ## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/johnsnowlabs/JSL-MedMNX-7B
<!-- 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/JSL-MedMNX-7B-GGUF/resolve/main/JSL-MedMNX-7B.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/JSL-MedMNX-7B-GGUF/resolve/main/JSL-MedMNX-7B.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/JSL-MedMNX-7B-GGUF/resolve/main/JSL-MedMNX-7B.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/JSL-MedMNX-7B-GGUF/resolve/main/JSL-MedMNX-7B.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/JSL-MedMNX-7B-GGUF/resolve/main/JSL-MedMNX-7B.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/JSL-MedMNX-7B-GGUF/resolve/main/JSL-MedMNX-7B.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/JSL-MedMNX-7B-GGUF/resolve/main/JSL-MedMNX-7B.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/JSL-MedMNX-7B-GGUF/resolve/main/JSL-MedMNX-7B.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/JSL-MedMNX-7B-GGUF/resolve/main/JSL-MedMNX-7B.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/JSL-MedMNX-7B-GGUF/resolve/main/JSL-MedMNX-7B.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/JSL-MedMNX-7B-GGUF/resolve/main/JSL-MedMNX-7B.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/JSL-MedMNX-7B-GGUF/resolve/main/JSL-MedMNX-7B.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/JSL-MedMNX-7B-GGUF/resolve/main/JSL-MedMNX-7B.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/JSL-MedMNX-7B-GGUF/resolve/main/JSL-MedMNX-7B.Q8_0.gguf) | Q8_0 | 7.8 | 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": "cc-by-nc-nd-4.0", "library_name": "transformers", "tags": ["reward model", "RLHF", "medical"], "base_model": "johnsnowlabs/JSL-MedMNX-7B", "quantized_by": "mradermacher"} | mradermacher/JSL-MedMNX-7B-GGUF | null | [
"transformers",
"gguf",
"reward model",
"RLHF",
"medical",
"en",
"base_model:johnsnowlabs/JSL-MedMNX-7B",
"license:cc-by-nc-nd-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T06:12:26+00:00 | [] | [
"en"
] | TAGS
#transformers #gguf #reward model #RLHF #medical #en #base_model-johnsnowlabs/JSL-MedMNX-7B #license-cc-by-nc-nd-4.0 #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 #reward model #RLHF #medical #en #base_model-johnsnowlabs/JSL-MedMNX-7B #license-cc-by-nc-nd-4.0 #endpoints_compatible #region-us \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.
- **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. -->
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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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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
### Results
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#### 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]
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[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. -->
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[More Information Needed]
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## 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": []} | alixan/mistral7b_1 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-16T06:12:44+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"
] |
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. -->
# klubert_Fallrisk_NER
This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0686
- Precision: 0.7608
- Recall: 0.7498
- F1: 0.7553
- Accuracy: 0.9845
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 194 | 0.0806 | 0.7311 | 0.7199 | 0.7254 | 0.9830 |
| No log | 2.0 | 388 | 0.0686 | 0.7608 | 0.7498 | 0.7553 | 0.9845 |
### Framework versions
- Transformers 4.28.0
- Pytorch 1.7.1
- Datasets 2.18.0
- Tokenizers 0.13.3
| {"license": "cc-by-sa-4.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "klubert_Fallrisk_NER", "results": []}]} | Dongspam/klubert_Fallrisk_NER | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T06:13:33+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
| klubert\_Fallrisk\_NER
======================
This model is a fine-tuned version of klue/bert-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0686
* Precision: 0.7608
* Recall: 0.7498
* F1: 0.7553
* Accuracy: 0.9845
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2
### Training results
### Framework versions
* Transformers 4.28.0
* Pytorch 1.7.1
* Datasets 2.18.0
* Tokenizers 0.13.3
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.28.0\n* Pytorch 1.7.1\n* Datasets 2.18.0\n* Tokenizers 0.13.3"
] | [
"TAGS\n#transformers #pytorch #bert #token-classification #generated_from_trainer #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.28.0\n* Pytorch 1.7.1\n* Datasets 2.18.0\n* Tokenizers 0.13.3"
] |
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. -->
# dpo_harmlessharmless_gpt3_gamma0.0_beta0.1_subset20000_modelmistral7b_maxsteps5000_bz8_lr5e-06
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 1
- eval_batch_size: 8
- 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
- lr_scheduler_warmup_steps: 15
- training_steps: 5000
### Training results
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2 | {"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "dpo", "generated_from_trainer"], "base_model": "mistralai/Mistral-7B-v0.1", "model-index": [{"name": "dpo_harmlessharmless_gpt3_gamma0.0_beta0.1_subset20000_modelmistral7b_maxsteps5000_bz8_lr5e-06", "results": []}]} | Holarissun/dpo_harmlessharmless_gpt3_gamma0.0_beta0.1_subset20000_modelmistral7b_maxsteps5000_bz8_lr5e-06 | null | [
"peft",
"safetensors",
"trl",
"dpo",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"region:us"
] | null | 2024-04-16T06:13:49+00:00 | [] | [] | TAGS
#peft #safetensors #trl #dpo #generated_from_trainer #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #region-us
|
# dpo_harmlessharmless_gpt3_gamma0.0_beta0.1_subset20000_modelmistral7b_maxsteps5000_bz8_lr5e-06
This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 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-06
- train_batch_size: 1
- eval_batch_size: 8
- 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
- lr_scheduler_warmup_steps: 15
- training_steps: 5000
### Training results
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2 | [
"# dpo_harmlessharmless_gpt3_gamma0.0_beta0.1_subset20000_modelmistral7b_maxsteps5000_bz8_lr5e-06\n\nThis model is a fine-tuned version of mistralai/Mistral-7B-v0.1 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-06\n- train_batch_size: 1\n- eval_batch_size: 8\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- lr_scheduler_warmup_steps: 15\n- training_steps: 5000",
"### Training results",
"### Framework versions\n\n- PEFT 0.9.0\n- Transformers 4.38.2\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] | [
"TAGS\n#peft #safetensors #trl #dpo #generated_from_trainer #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #region-us \n",
"# dpo_harmlessharmless_gpt3_gamma0.0_beta0.1_subset20000_modelmistral7b_maxsteps5000_bz8_lr5e-06\n\nThis model is a fine-tuned version of mistralai/Mistral-7B-v0.1 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-06\n- train_batch_size: 1\n- eval_batch_size: 8\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- lr_scheduler_warmup_steps: 15\n- training_steps: 5000",
"### Training results",
"### Framework versions\n\n- PEFT 0.9.0\n- Transformers 4.38.2\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] |
text-generation | null | Non quantized GGUF version of the model : Trelis/Llama-2-7b-chat-hf-function-calling-v3
The 8-bits quantized and 4-bits quantized models fail to understand which arguments are required and which are optionals | {"language": ["en"], "pipeline_tag": "text-generation"} | JulienSantiago/Llama-2-7b-chat-hf-function-calling-v3-GGUF | null | [
"gguf",
"text-generation",
"en",
"region:us"
] | null | 2024-04-16T06:14:24+00:00 | [] | [
"en"
] | TAGS
#gguf #text-generation #en #region-us
| Non quantized GGUF version of the model : Trelis/Llama-2-7b-chat-hf-function-calling-v3
The 8-bits quantized and 4-bits quantized models fail to understand which arguments are required and which are optionals | [] | [
"TAGS\n#gguf #text-generation #en #region-us \n"
] |
text-generation | transformers |
# JupiterINEX12-12B-MoE
JupiterINEX12-12B-MoE is a Mixture of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [allknowingroger/JupiterMerge-7B-slerp](https://huggingface.co/allknowingroger/JupiterMerge-7B-slerp)
* [allknowingroger/RasGullaINEX12-7B-slerp](https://huggingface.co/allknowingroger/RasGullaINEX12-7B-slerp)
## 🧩 Configuration
```yaml
base_model: allknowingroger/JupiterMerge-7B-slerp
experts:
- source_model: allknowingroger/JupiterMerge-7B-slerp
positive_prompts: ["why"]
- source_model: allknowingroger/RasGullaINEX12-7B-slerp
positive_prompts: ["math"]
```
## 💻 Usage
```python
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "allknowingroger/JupiterINEX12-12B-MoE"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)
messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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": ["moe", "frankenmoe", "merge", "mergekit", "lazymergekit", "allknowingroger/JupiterMerge-7B-slerp", "allknowingroger/RasGullaINEX12-7B-slerp"], "base_model": ["allknowingroger/JupiterMerge-7B-slerp", "allknowingroger/RasGullaINEX12-7B-slerp"]} | allknowingroger/JupiterINEX12-12B-MoE | null | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"moe",
"frankenmoe",
"merge",
"mergekit",
"lazymergekit",
"allknowingroger/JupiterMerge-7B-slerp",
"allknowingroger/RasGullaINEX12-7B-slerp",
"conversational",
"base_model:allknowingroger/JupiterMerge-7B-slerp",
"base_model:allknowingroger/RasGullaINEX12-7B-slerp",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-16T06:15:01+00:00 | [] | [] | TAGS
#transformers #safetensors #mixtral #text-generation #moe #frankenmoe #merge #mergekit #lazymergekit #allknowingroger/JupiterMerge-7B-slerp #allknowingroger/RasGullaINEX12-7B-slerp #conversational #base_model-allknowingroger/JupiterMerge-7B-slerp #base_model-allknowingroger/RasGullaINEX12-7B-slerp #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# JupiterINEX12-12B-MoE
JupiterINEX12-12B-MoE is a Mixture of Experts (MoE) made with the following models using LazyMergekit:
* allknowingroger/JupiterMerge-7B-slerp
* allknowingroger/RasGullaINEX12-7B-slerp
## Configuration
## Usage
| [
"# JupiterINEX12-12B-MoE\n\nJupiterINEX12-12B-MoE is a Mixture of Experts (MoE) made with the following models using LazyMergekit:\n* allknowingroger/JupiterMerge-7B-slerp\n* allknowingroger/RasGullaINEX12-7B-slerp",
"## Configuration",
"## Usage"
] | [
"TAGS\n#transformers #safetensors #mixtral #text-generation #moe #frankenmoe #merge #mergekit #lazymergekit #allknowingroger/JupiterMerge-7B-slerp #allknowingroger/RasGullaINEX12-7B-slerp #conversational #base_model-allknowingroger/JupiterMerge-7B-slerp #base_model-allknowingroger/RasGullaINEX12-7B-slerp #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# JupiterINEX12-12B-MoE\n\nJupiterINEX12-12B-MoE is a Mixture of Experts (MoE) made with the following models using LazyMergekit:\n* allknowingroger/JupiterMerge-7B-slerp\n* allknowingroger/RasGullaINEX12-7B-slerp",
"## Configuration",
"## Usage"
] |
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 0.10.1.dev0 | {"library_name": "peft", "base_model": "mistralai/Mistral-7B-v0.1"} | VikrantRamesh/Mistral_CN_finetuned | null | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-v0.1",
"region:us"
] | null | 2024-04-16T06:15:57+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #safetensors #arxiv-1910.09700 #base_model-mistralai/Mistral-7B-v0.1 #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 0.10.1.dev0 | [
"# 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 0.10.1.dev0"
] | [
"TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-mistralai/Mistral-7B-v0.1 #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 0.10.1.dev0"
] |
text-generation | peft |
# lang-uk/dragoman-4bit
This model was converted to MLX format from the [`lang-uk/dragoman`](https://huggingface.co/lang-uk/dragoman) adapter fused into the [`mistralai/Mistral-7b-v0.1`](https://huggingface.co/mistralai/Mistral-7B-v0.1)
base model and quantized into 4 bits using mlx-lm version **0.4.0**.
Refer to the [original model card](https://huggingface.co/lang-uk/dragoman) for more details on the model.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("lang-uk/dragoman-4bit")
response = generate(model, tokenizer, prompt="[INST] who holds this neighborhood? [/INST]", verbose=True)
```
Or use from your shell:
```console
python -m mlx_lm.generate --model lang-uk/dragoman-4bit --prompt '[INST] who holds this neighborhood? [/INST]' --temp 0 --max-tokens 100
```
| {"language": ["uk", "en"], "license": "apache-2.0", "library_name": "peft", "tags": ["translation", "mlx"], "datasets": ["Helsinki-NLP/opus_paracrawl", "turuta/Multi30k-uk"], "metrics": ["bleu"], "pipeline_tag": "text-generation", "base_model": "mistralai/Mistral-7B-v0.1", "inference": false, "model-index": [{"name": "Dragoman", "results": [{"task": {"type": "translation", "name": "English-Ukrainian Translation"}, "dataset": {"name": "FLORES-101", "type": "facebook/flores", "config": "eng_Latn-ukr_Cyrl", "split": "devtest"}, "metrics": [{"type": "bleu", "value": 32.34, "name": "Test BLEU"}]}]}]} | lang-uk/dragoman-4bit | null | [
"peft",
"safetensors",
"mistral",
"translation",
"mlx",
"text-generation",
"uk",
"en",
"dataset:Helsinki-NLP/opus_paracrawl",
"dataset:turuta/Multi30k-uk",
"base_model:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"model-index",
"region:us"
] | null | 2024-04-16T06:17:26+00:00 | [] | [
"uk",
"en"
] | TAGS
#peft #safetensors #mistral #translation #mlx #text-generation #uk #en #dataset-Helsinki-NLP/opus_paracrawl #dataset-turuta/Multi30k-uk #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #model-index #region-us
|
# lang-uk/dragoman-4bit
This model was converted to MLX format from the 'lang-uk/dragoman' adapter fused into the 'mistralai/Mistral-7b-v0.1'
base model and quantized into 4 bits using mlx-lm version 0.4.0.
Refer to the original model card for more details on the model.
## Use with mlx
Or use from your shell:
| [
"# lang-uk/dragoman-4bit\nThis model was converted to MLX format from the 'lang-uk/dragoman' adapter fused into the 'mistralai/Mistral-7b-v0.1'\nbase model and quantized into 4 bits using mlx-lm version 0.4.0.\nRefer to the original model card for more details on the model.",
"## Use with mlx\n\n\n\n\n\nOr use from your shell:"
] | [
"TAGS\n#peft #safetensors #mistral #translation #mlx #text-generation #uk #en #dataset-Helsinki-NLP/opus_paracrawl #dataset-turuta/Multi30k-uk #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #model-index #region-us \n",
"# lang-uk/dragoman-4bit\nThis model was converted to MLX format from the 'lang-uk/dragoman' adapter fused into the 'mistralai/Mistral-7b-v0.1'\nbase model and quantized into 4 bits using mlx-lm version 0.4.0.\nRefer to the original model card for more details on the model.",
"## Use with mlx\n\n\n\n\n\nOr use from your shell:"
] |
text-generation | transformers |
# WizardLM-2-8x22B - EXL2 7.0bpw
This is a 7.0bpw EXL2 quant of [microsoft/WizardLM-2-8x22B](https://huggingface.co/microsoft/WizardLM-2-8x22B)
Details about the model can be found at the above model page.
## EXL2 Version
These quants were made with exllamav2 version 0.0.18. Quants made on this version of EXL2 may not work on older versions of the exllamav2 library.
If you have problems loading these models, please update Text Generation WebUI to the latest version.
## Perplexity Scoring
Below are the perplexity scores for the EXL2 models. A lower score is better.
| Quant Level | Perplexity Score |
|-------------|------------------|
| 7.0 | 4.5859 |
| 6.0 | 4.6252 |
| 5.5 | 4.6493 |
| 5.0 | 4.6937 |
| 4.5 | 4.8029 |
| 4.0 | 4.9372 |
| 3.5 | 5.1336 |
| 3.25 | 5.3636 |
| 3.0 | 5.5468 |
| 2.75 | 5.8255 |
| 2.5 | 6.3362 |
| 2.25 | 7.7763 |
### Perplexity Script
This was the script used for perplexity testing.
```bash
#!/bin/bash
# Activate the conda environment
source ~/miniconda3/etc/profile.d/conda.sh
conda activate exllamav2
DATA_SET=/root/wikitext/wikitext-2-v1.parquet
# Set the model name and bit size
MODEL_NAME="WizardLM-2-8x22B"
BIT_PRECISIONS=(6.0 5.5 5.0 4.5 4.0 3.5 3.25 3.0 2.75 2.5 2.25)
# Print the markdown table header
echo "| Quant Level | Perplexity Score |"
echo "|-------------|------------------|"
for BIT_PRECISION in "${BIT_PRECISIONS[@]}"
do
LOCAL_FOLDER="/root/models/${MODEL_NAME}_exl2_${BIT_PRECISION}bpw"
REMOTE_FOLDER="Dracones/${MODEL_NAME}_exl2_${BIT_PRECISION}bpw"
if [ ! -d "$LOCAL_FOLDER" ]; then
huggingface-cli download --local-dir-use-symlinks=False --local-dir "${LOCAL_FOLDER}" "${REMOTE_FOLDER}" >> /root/download.log 2>&1
fi
output=$(python test_inference.py -m "$LOCAL_FOLDER" -gs 40,40,40,40 -ed "$DATA_SET")
score=$(echo "$output" | grep -oP 'Evaluation perplexity: \K[\d.]+')
echo "| $BIT_PRECISION | $score |"
# rm -rf "${LOCAL_FOLDER}"
done
```
## Quant Details
This is the script used for quantization.
```bash
#!/bin/bash
# Activate the conda environment
source ~/miniconda3/etc/profile.d/conda.sh
conda activate exllamav2
# Set the model name and bit size
MODEL_NAME="WizardLM-2-8x22B"
# Define variables
MODEL_DIR="/mnt/storage/models/$MODEL_NAME"
OUTPUT_DIR="exl2_$MODEL_NAME"
MEASUREMENT_FILE="measurements/$MODEL_NAME.json"
# Create the measurement file if needed
if [ ! -f "$MEASUREMENT_FILE" ]; then
echo "Creating $MEASUREMENT_FILE"
# Create directories
if [ -d "$OUTPUT_DIR" ]; then
rm -r "$OUTPUT_DIR"
fi
mkdir "$OUTPUT_DIR"
python convert.py -i $MODEL_DIR -o $OUTPUT_DIR -nr -om $MEASUREMENT_FILE
fi
# Choose one of the below. Either create a single quant for testing or a batch of them.
# BIT_PRECISIONS=(2.25)
BIT_PRECISIONS=(5.0 4.5 4.0 3.5 3.0 2.75 2.5 2.25)
for BIT_PRECISION in "${BIT_PRECISIONS[@]}"
do
CONVERTED_FOLDER="models/${MODEL_NAME}_exl2_${BIT_PRECISION}bpw"
# If it doesn't already exist, make the quant
if [ ! -d "$CONVERTED_FOLDER" ]; then
echo "Creating $CONVERTED_FOLDER"
# Create directories
if [ -d "$OUTPUT_DIR" ]; then
rm -r "$OUTPUT_DIR"
fi
mkdir "$OUTPUT_DIR"
mkdir "$CONVERTED_FOLDER"
# Run conversion commands
python convert.py -i $MODEL_DIR -o $OUTPUT_DIR -nr -m $MEASUREMENT_FILE -b $BIT_PRECISION -cf $CONVERTED_FOLDER
fi
done
```
| {"language": ["en"], "license": "apache-2.0", "tags": ["exl2"], "base_model": "microsoft/WizardLM-2-8x22B"} | Dracones/WizardLM-2-8x22B_exl2_7.0bpw | null | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"exl2",
"en",
"base_model:microsoft/WizardLM-2-8x22B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"7-bit",
"region:us"
] | null | 2024-04-16T06:18:23+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #mixtral #text-generation #exl2 #en #base_model-microsoft/WizardLM-2-8x22B #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #7-bit #region-us
| WizardLM-2-8x22B - EXL2 7.0bpw
==============================
This is a 7.0bpw EXL2 quant of microsoft/WizardLM-2-8x22B
Details about the model can be found at the above model page.
EXL2 Version
------------
These quants were made with exllamav2 version 0.0.18. Quants made on this version of EXL2 may not work on older versions of the exllamav2 library.
If you have problems loading these models, please update Text Generation WebUI to the latest version.
Perplexity Scoring
------------------
Below are the perplexity scores for the EXL2 models. A lower score is better.
### Perplexity Script
This was the script used for perplexity testing.
Quant Details
-------------
This is the script used for quantization.
| [
"### Perplexity Script\n\n\nThis was the script used for perplexity testing.\n\n\nQuant Details\n-------------\n\n\nThis is the script used for quantization."
] | [
"TAGS\n#transformers #safetensors #mixtral #text-generation #exl2 #en #base_model-microsoft/WizardLM-2-8x22B #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #7-bit #region-us \n",
"### Perplexity Script\n\n\nThis was the script used for perplexity testing.\n\n\nQuant Details\n-------------\n\n\nThis is the script used for quantization."
] |
text-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. -->
# mistral-7b-wo-kqa_silver_wogold-sft
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the HuggingFaceH4/deita-10k-v0-sft dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8699
## 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: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.3819 | 0.91 | 5 | 1.1824 |
| 1.0491 | 2.0 | 11 | 0.9082 |
| 0.8189 | 2.73 | 15 | 0.8699 |
### Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.1.2
- Datasets 2.14.6
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["alignment-handbook", "trl", "sft", "generated_from_trainer", "trl", "sft", "generated_from_trainer"], "datasets": ["HuggingFaceH4/deita-10k-v0-sft"], "base_model": "mistralai/Mistral-7B-v0.1", "model-index": [{"name": "mistral-7b-wo-kqa_silver_wogold-sft", "results": []}]} | Minbyul/mistral-7b-wo-kqa_silver_wogold-sft | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"trl",
"sft",
"generated_from_trainer",
"dataset:HuggingFaceH4/deita-10k-v0-sft",
"base_model:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-16T06:18:41+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #alignment-handbook #trl #sft #generated_from_trainer #dataset-HuggingFaceH4/deita-10k-v0-sft #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mistral-7b-wo-kqa\_silver\_wogold-sft
=====================================
This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the HuggingFaceH4/deita-10k-v0-sft dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8699
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: 4
* eval\_batch\_size: 4
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 4
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* total\_eval\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.39.0.dev0
* Pytorch 2.1.2
* Datasets 2.14.6
* 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: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 4\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* total\\_eval\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.39.0.dev0\n* Pytorch 2.1.2\n* Datasets 2.14.6\n* Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #alignment-handbook #trl #sft #generated_from_trainer #dataset-HuggingFaceH4/deita-10k-v0-sft #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 4\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* total\\_eval\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.39.0.dev0\n* Pytorch 2.1.2\n* Datasets 2.14.6\n* Tokenizers 0.15.2"
] |
null | null | Diatrust ক্যাপসুল কি?
Diatrust ট্যাবলেট স্বাস্থ্যকর রক্তে শর্করার মাত্রাকে সমর্থন করার জন্য তৈরি করা একটি সতর্কতার সাথে তৈরি খাদ্যতালিকাগত সম্পূরক। এটি ইনসুলিন সংবেদনশীলতা বাড়াতে এবং গ্লুকোজ বিপাককে উন্নীত করার জন্য তাদের সম্ভাব্যতার জন্য সাবধানে নির্বাচিত প্রাকৃতিক উপাদানগুলির শক্তিকে কাজে লাগায়। একটি সুষম খাদ্য এবং জীবনধারা পরিপূরক করার জন্য ডিজাইন করা হয়েছে, Diatrust দাম সিন্থেটিক ওষুধের প্রয়োজন ছাড়াই ডায়াবেটিস ব্যবস্থাপনার জন্য একটি সামগ্রিক পদ্ধতির প্রস্তাব দেয়।
সরকারী ওয়েবসাইট:<a href="https://www.nutritionsee.com/Diatrbanga">www.Diatrust.com</a>
<p><a href="https://www.nutritionsee.com/Diatrbanga"> <img src="https://www.nutritionsee.com/wp-content/uploads/2024/04/Diatrust-Bangladesh-1.png" alt="enter image description here"> </a></p>
<a href="https://www.nutritionsee.com/Diatrbanga">এখন কেন!! আরও তথ্যের জন্য নীচের লিঙ্কে ক্লিক করুন এবং এখনই 50% ছাড় পান... তাড়াতাড়ি করুন</a>
সরকারী ওয়েবসাইট:<a href="https://www.nutritionsee.com/Diatrbanga">www.Diatrust.com</a> | {"license": "apache-2.0"} | DiatrustBangladesh/DiatrustBangladesh | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-04-16T06:18:55+00:00 | [] | [] | TAGS
#license-apache-2.0 #region-us
| Diatrust ক্যাপসুল কি?
Diatrust ট্যাবলেট স্বাস্থ্যকর রক্তে শর্করার মাত্রাকে সমর্থন করার জন্য তৈরি করা একটি সতর্কতার সাথে তৈরি খাদ্যতালিকাগত সম্পূরক। এটি ইনসুলিন সংবেদনশীলতা বাড়াতে এবং গ্লুকোজ বিপাককে উন্নীত করার জন্য তাদের সম্ভাব্যতার জন্য সাবধানে নির্বাচিত প্রাকৃতিক উপাদানগুলির শক্তিকে কাজে লাগায়। একটি সুষম খাদ্য এবং জীবনধারা পরিপূরক করার জন্য ডিজাইন করা হয়েছে, Diatrust দাম সিন্থেটিক ওষুধের প্রয়োজন ছাড়াই ডায়াবেটিস ব্যবস্থাপনার জন্য একটি সামগ্রিক পদ্ধতির প্রস্তাব দেয়।
সরকারী ওয়েবসাইট:<a href="URL
<p><a href="URL <img src="URL alt="enter image description here"> </a></p>
<a href="URL>এখন কেন!! আরও তথ্যের জন্য নীচের লিঙ্কে ক্লিক করুন এবং এখনই 50% ছাড় পান... তাড়াতাড়ি করুন</a>
সরকারী ওয়েবসাইট:<a href="URL | [] | [
"TAGS\n#license-apache-2.0 #region-us \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.
- **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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### 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]
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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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- **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]
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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]
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[More Information Needed]
| {"library_name": "transformers", "tags": []} | cilantro9246/0wfnfg2 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-16T06:19:47+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## 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
| [
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"## 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",
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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]",
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"#### Speeds, Sizes, Times [optional]",
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"#### 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 #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]",
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"#### Speeds, Sizes, Times [optional]",
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"### 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-generation | transformers |
# NeuralPipe-7B-slerp
NeuralPipe-7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [OpenPipe/mistral-ft-optimized-1218](https://huggingface.co/OpenPipe/mistral-ft-optimized-1218)
* [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: OpenPipe/mistral-ft-optimized-1218
layer_range: [0, 32]
- model: mlabonne/NeuralHermes-2.5-Mistral-7B
layer_range: [0, 32]
merge_method: slerp
base_model: OpenPipe/mistral-ft-optimized-1218
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "yuanzheng625/NeuralPipe-7B-slerp"
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"])
``` | {"tags": ["merge", "mergekit", "lazymergekit", "OpenPipe/mistral-ft-optimized-1218", "mlabonne/NeuralHermes-2.5-Mistral-7B"], "base_model": ["OpenPipe/mistral-ft-optimized-1218", "mlabonne/NeuralHermes-2.5-Mistral-7B"]} | yuanzheng625/NeuralPipe-7B-slerp | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"OpenPipe/mistral-ft-optimized-1218",
"mlabonne/NeuralHermes-2.5-Mistral-7B",
"base_model:OpenPipe/mistral-ft-optimized-1218",
"base_model:mlabonne/NeuralHermes-2.5-Mistral-7B",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-16T06:25:59+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #OpenPipe/mistral-ft-optimized-1218 #mlabonne/NeuralHermes-2.5-Mistral-7B #base_model-OpenPipe/mistral-ft-optimized-1218 #base_model-mlabonne/NeuralHermes-2.5-Mistral-7B #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# NeuralPipe-7B-slerp
NeuralPipe-7B-slerp is a merge of the following models using LazyMergekit:
* OpenPipe/mistral-ft-optimized-1218
* mlabonne/NeuralHermes-2.5-Mistral-7B
## Configuration
## Usage
| [
"# NeuralPipe-7B-slerp\n\nNeuralPipe-7B-slerp is a merge of the following models using LazyMergekit:\n* OpenPipe/mistral-ft-optimized-1218\n* mlabonne/NeuralHermes-2.5-Mistral-7B",
"## Configuration",
"## Usage"
] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #OpenPipe/mistral-ft-optimized-1218 #mlabonne/NeuralHermes-2.5-Mistral-7B #base_model-OpenPipe/mistral-ft-optimized-1218 #base_model-mlabonne/NeuralHermes-2.5-Mistral-7B #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# NeuralPipe-7B-slerp\n\nNeuralPipe-7B-slerp is a merge of the following models using LazyMergekit:\n* OpenPipe/mistral-ft-optimized-1218\n* mlabonne/NeuralHermes-2.5-Mistral-7B",
"## Configuration",
"## Usage"
] |
token-classification | transformers |
## Model Specification
- Model: XLM-RoBERTa (base-sized model)
- Training Data:
- Combined Afrikaans, Hebrew, Bulgarian, & Vietnamese corpora (Top 4 Languages)
- Training Details:
- Base configurations with a minor adjustment in learning rate (4.5e-5)
## Evaluation
- Evaluation Dataset: Universal Dependencies Tagalog Ugnayan (Testing Set)
- Tested in a zero-shot cross-lingual scenario on a Universal Dependencies Tagalog Ugnayan testing dataset (with 80.01\% Accuracy)
## POS Tags
- ADJ – ADP – ADV – CCONJ – DET – INTJ – NOUN – NUM – PART – PRON – PROPN – PUNCT – SCONJ – VERB | {"language": ["tl"], "datasets": ["universal_dependencies"], "metrics": ["f1"], "pipeline_tag": "token-classification"} | iceman2434/xlm-roberta-base-ft-udpos213-top4lang | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"tl",
"dataset:universal_dependencies",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T06:26:21+00:00 | [] | [
"tl"
] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #tl #dataset-universal_dependencies #autotrain_compatible #endpoints_compatible #region-us
|
## Model Specification
- Model: XLM-RoBERTa (base-sized model)
- Training Data:
- Combined Afrikaans, Hebrew, Bulgarian, & Vietnamese corpora (Top 4 Languages)
- Training Details:
- Base configurations with a minor adjustment in learning rate (4.5e-5)
## Evaluation
- Evaluation Dataset: Universal Dependencies Tagalog Ugnayan (Testing Set)
- Tested in a zero-shot cross-lingual scenario on a Universal Dependencies Tagalog Ugnayan testing dataset (with 80.01\% Accuracy)
## POS Tags
- ADJ – ADP – ADV – CCONJ – DET – INTJ – NOUN – NUM – PART – PRON – PROPN – PUNCT – SCONJ – VERB | [
"## Model Specification\n- Model: XLM-RoBERTa (base-sized model)\n- Training Data:\n - Combined Afrikaans, Hebrew, Bulgarian, & Vietnamese corpora (Top 4 Languages)\n- Training Details:\n - Base configurations with a minor adjustment in learning rate (4.5e-5)",
"## Evaluation\n- Evaluation Dataset: Universal Dependencies Tagalog Ugnayan (Testing Set)\n- Tested in a zero-shot cross-lingual scenario on a Universal Dependencies Tagalog Ugnayan testing dataset (with 80.01\\% Accuracy)",
"## POS Tags\n- ADJ – ADP – ADV – CCONJ – DET – INTJ – NOUN – NUM – PART – PRON – PROPN – PUNCT – SCONJ – VERB"
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #tl #dataset-universal_dependencies #autotrain_compatible #endpoints_compatible #region-us \n",
"## Model Specification\n- Model: XLM-RoBERTa (base-sized model)\n- Training Data:\n - Combined Afrikaans, Hebrew, Bulgarian, & Vietnamese corpora (Top 4 Languages)\n- Training Details:\n - Base configurations with a minor adjustment in learning rate (4.5e-5)",
"## Evaluation\n- Evaluation Dataset: Universal Dependencies Tagalog Ugnayan (Testing Set)\n- Tested in a zero-shot cross-lingual scenario on a Universal Dependencies Tagalog Ugnayan testing dataset (with 80.01\\% Accuracy)",
"## POS Tags\n- ADJ – ADP – ADV – CCONJ – DET – INTJ – NOUN – NUM – PART – PRON – PROPN – PUNCT – SCONJ – VERB"
] |
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. -->
# ruBert-base-sberquad-0.01-len_3-filtered-v2
This model is a fine-tuned version of [ai-forever/ruBert-base](https://huggingface.co/ai-forever/ruBert-base) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 7000
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 | {"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "ai-forever/ruBert-base", "model-index": [{"name": "ruBert-base-sberquad-0.01-len_3-filtered-v2", "results": []}]} | Shalazary/ruBert-base-sberquad-0.01-len_3-filtered-v2 | null | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:ai-forever/ruBert-base",
"license:apache-2.0",
"region:us"
] | null | 2024-04-16T06:26:56+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #generated_from_trainer #base_model-ai-forever/ruBert-base #license-apache-2.0 #region-us
|
# ruBert-base-sberquad-0.01-len_3-filtered-v2
This model is a fine-tuned version of ai-forever/ruBert-base on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 7000
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 | [
"# ruBert-base-sberquad-0.01-len_3-filtered-v2\n\nThis model is a fine-tuned version of ai-forever/ruBert-base on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0005\n- train_batch_size: 32\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- training_steps: 7000",
"### Training results",
"### Framework versions\n\n- PEFT 0.10.0\n- Transformers 4.39.3\n- Pytorch 2.2.2+cu121\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] | [
"TAGS\n#peft #tensorboard #safetensors #generated_from_trainer #base_model-ai-forever/ruBert-base #license-apache-2.0 #region-us \n",
"# ruBert-base-sberquad-0.01-len_3-filtered-v2\n\nThis model is a fine-tuned version of ai-forever/ruBert-base on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0005\n- train_batch_size: 32\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- training_steps: 7000",
"### Training results",
"### Framework versions\n\n- PEFT 0.10.0\n- Transformers 4.39.3\n- Pytorch 2.2.2+cu121\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] |
null | peft |
# umarigan/TURKCELL-LLM-7B-DPO-Q4_K_M-GGUF
This model was converted to GGUF format from [`umarigan/TURKCELL-LLM-7B-DPO`](https://huggingface.co/umarigan/TURKCELL-LLM-7B-DPO) 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/umarigan/TURKCELL-LLM-7B-DPO) 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 umarigan/TURKCELL-LLM-7B-DPO-Q4_K_M-GGUF --model turkcell-llm-7b-dpo.Q4_K_M.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo umarigan/TURKCELL-LLM-7B-DPO-Q4_K_M-GGUF --model turkcell-llm-7b-dpo.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 turkcell-llm-7b-dpo.Q4_K_M.gguf -n 128
```
| {"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "dpo", "unsloth", "generated_from_trainer", "llama-cpp", "gguf-my-repo"], "base_model": "umarigan/TURKCELL-LLM-7B-openhermes", "model-index": [{"name": "TURKCELL-LLM-7B-DPO", "results": []}]} | umarigan/TURKCELL-LLM-7B-DPO-Q4_K_M-GGUF | null | [
"peft",
"gguf",
"trl",
"dpo",
"unsloth",
"generated_from_trainer",
"llama-cpp",
"gguf-my-repo",
"base_model:umarigan/TURKCELL-LLM-7B-openhermes",
"license:apache-2.0",
"region:us"
] | null | 2024-04-16T06:27:01+00:00 | [] | [] | TAGS
#peft #gguf #trl #dpo #unsloth #generated_from_trainer #llama-cpp #gguf-my-repo #base_model-umarigan/TURKCELL-LLM-7B-openhermes #license-apache-2.0 #region-us
|
# umarigan/TURKCELL-LLM-7B-DPO-Q4_K_M-GGUF
This model was converted to GGUF format from 'umarigan/TURKCELL-LLM-7B-DPO' 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.
| [
"# umarigan/TURKCELL-LLM-7B-DPO-Q4_K_M-GGUF\nThis model was converted to GGUF format from 'umarigan/TURKCELL-LLM-7B-DPO' 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#peft #gguf #trl #dpo #unsloth #generated_from_trainer #llama-cpp #gguf-my-repo #base_model-umarigan/TURKCELL-LLM-7B-openhermes #license-apache-2.0 #region-us \n",
"# umarigan/TURKCELL-LLM-7B-DPO-Q4_K_M-GGUF\nThis model was converted to GGUF format from 'umarigan/TURKCELL-LLM-7B-DPO' 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 | 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. -->
# dpo_harmlessharmless_gpt3_gamma0.0_beta0.1_subset20000_modelmistral7b_maxsteps5000_bz8_lr1e-05
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on an unknown dataset.
## 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: 8
- 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
- lr_scheduler_warmup_steps: 15
- training_steps: 5000
### Training results
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2 | {"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "dpo", "generated_from_trainer"], "base_model": "mistralai/Mistral-7B-v0.1", "model-index": [{"name": "dpo_harmlessharmless_gpt3_gamma0.0_beta0.1_subset20000_modelmistral7b_maxsteps5000_bz8_lr1e-05", "results": []}]} | Holarissun/dpo_harmlessharmless_gpt3_gamma0.0_beta0.1_subset20000_modelmistral7b_maxsteps5000_bz8_lr1e-05 | null | [
"peft",
"safetensors",
"trl",
"dpo",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"region:us"
] | null | 2024-04-16T06:28:28+00:00 | [] | [] | TAGS
#peft #safetensors #trl #dpo #generated_from_trainer #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #region-us
|
# dpo_harmlessharmless_gpt3_gamma0.0_beta0.1_subset20000_modelmistral7b_maxsteps5000_bz8_lr1e-05
This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 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: 1e-05
- train_batch_size: 1
- eval_batch_size: 8
- 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
- lr_scheduler_warmup_steps: 15
- training_steps: 5000
### Training results
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2 | [
"# dpo_harmlessharmless_gpt3_gamma0.0_beta0.1_subset20000_modelmistral7b_maxsteps5000_bz8_lr1e-05\n\nThis model is a fine-tuned version of mistralai/Mistral-7B-v0.1 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: 1e-05\n- train_batch_size: 1\n- eval_batch_size: 8\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- lr_scheduler_warmup_steps: 15\n- training_steps: 5000",
"### Training results",
"### Framework versions\n\n- PEFT 0.9.0\n- Transformers 4.38.2\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] | [
"TAGS\n#peft #safetensors #trl #dpo #generated_from_trainer #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #region-us \n",
"# dpo_harmlessharmless_gpt3_gamma0.0_beta0.1_subset20000_modelmistral7b_maxsteps5000_bz8_lr1e-05\n\nThis model is a fine-tuned version of mistralai/Mistral-7B-v0.1 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: 1e-05\n- train_batch_size: 1\n- eval_batch_size: 8\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- lr_scheduler_warmup_steps: 15\n- training_steps: 5000",
"### Training results",
"### Framework versions\n\n- PEFT 0.9.0\n- Transformers 4.38.2\n- Pytorch 2.1.2\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": []} | ryanyeo/kirnect-KoAlpaca-7B | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T06:33:52+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:
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## 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.",
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"## Training Details",
"### Training Data",
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"#### Preprocessing [optional]",
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"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
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"## Technical Specifications [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",
"### Results",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
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"#### Hardware",
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"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
text-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. -->
# llama2-7b-wo-kqa_silver_wogold-sft
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the HuggingFaceH4/deita-10k-v0-sft dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8717
## 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: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.0891 | 0.87 | 5 | 1.0166 |
| 0.9432 | 1.91 | 11 | 0.8885 |
| 0.8405 | 2.61 | 15 | 0.8717 |
### Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.1.2
- Datasets 2.14.6
- Tokenizers 0.15.2
| {"tags": ["alignment-handbook", "trl", "sft", "generated_from_trainer", "trl", "sft", "generated_from_trainer"], "datasets": ["HuggingFaceH4/deita-10k-v0-sft"], "base_model": "meta-llama/Llama-2-7b-hf", "model-index": [{"name": "llama2-7b-wo-kqa_silver_wogold-sft", "results": []}]} | Minbyul/llama2-7b-wo-kqa_silver_wogold-sft | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"alignment-handbook",
"trl",
"sft",
"generated_from_trainer",
"dataset:HuggingFaceH4/deita-10k-v0-sft",
"base_model:meta-llama/Llama-2-7b-hf",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-16T06:34:55+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #alignment-handbook #trl #sft #generated_from_trainer #dataset-HuggingFaceH4/deita-10k-v0-sft #base_model-meta-llama/Llama-2-7b-hf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| llama2-7b-wo-kqa\_silver\_wogold-sft
====================================
This model is a fine-tuned version of meta-llama/Llama-2-7b-hf on the HuggingFaceH4/deita-10k-v0-sft dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8717
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: 4
* eval\_batch\_size: 4
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 4
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* total\_eval\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.39.0.dev0
* Pytorch 2.1.2
* Datasets 2.14.6
* Tokenizers 0.15.2
| [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 4\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* total\\_eval\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.39.0.dev0\n* Pytorch 2.1.2\n* Datasets 2.14.6\n* Tokenizers 0.15.2"
] |
null | transformers |
# DavidAU/dragonwar-7b-alpha-Q8_0-GGUF
This model was converted to GGUF format from [`maldv/dragonwar-7b-alpha`](https://huggingface.co/maldv/dragonwar-7b-alpha) 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/maldv/dragonwar-7b-alpha) 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 DavidAU/dragonwar-7b-alpha-Q8_0-GGUF --model dragonwar-7b-alpha.Q8_0.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo DavidAU/dragonwar-7b-alpha-Q8_0-GGUF --model dragonwar-7b-alpha.Q8_0.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 dragonwar-7b-alpha.Q8_0.gguf -n 128
```
| {"license": "cc-by-nc-4.0", "library_name": "transformers", "tags": ["unsloth", "book", "llama-cpp", "gguf-my-repo"]} | DavidAU/dragonwar-7b-alpha-Q8_0-GGUF | null | [
"transformers",
"gguf",
"unsloth",
"book",
"llama-cpp",
"gguf-my-repo",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T06:36:47+00:00 | [] | [] | TAGS
#transformers #gguf #unsloth #book #llama-cpp #gguf-my-repo #license-cc-by-nc-4.0 #endpoints_compatible #region-us
|
# DavidAU/dragonwar-7b-alpha-Q8_0-GGUF
This model was converted to GGUF format from 'maldv/dragonwar-7b-alpha' 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.
| [
"# DavidAU/dragonwar-7b-alpha-Q8_0-GGUF\nThis model was converted to GGUF format from 'maldv/dragonwar-7b-alpha' 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."
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"## 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 |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- 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": []} | Grayx/sad_pepe_30 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T06:37:18+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]:",
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"## Model Card Contact"
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"### 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",
"### 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-generation | 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. -->
# diaratechHf_llama
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on the generator dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_steps: 0.03
- training_steps: 2
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 | {"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "mistralai/Mistral-7B-Instruct-v0.2", "pipeline_tag": "text-generation", "model-index": [{"name": "diaratechHf_llama", "results": []}]} | Yash0109/diaratechHf_llama | null | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"text-generation",
"conversational",
"dataset:generator",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"license:apache-2.0",
"region:us"
] | null | 2024-04-16T06:37:42+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #text-generation #conversational #dataset-generator #base_model-mistralai/Mistral-7B-Instruct-v0.2 #license-apache-2.0 #region-us
|
# diaratechHf_llama
This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.2 on the generator dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_steps: 0.03
- training_steps: 2
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 | [
"# diaratechHf_llama\n\nThis model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.2 on the generator dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 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: constant\n- lr_scheduler_warmup_steps: 0.03\n- training_steps: 2",
"### Training results",
"### Framework versions\n\n- PEFT 0.10.0\n- Transformers 4.39.3\n- Pytorch 2.2.2+cu121\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] | [
"TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #text-generation #conversational #dataset-generator #base_model-mistralai/Mistral-7B-Instruct-v0.2 #license-apache-2.0 #region-us \n",
"# diaratechHf_llama\n\nThis model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.2 on the generator dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 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: constant\n- lr_scheduler_warmup_steps: 0.03\n- training_steps: 2",
"### Training results",
"### Framework versions\n\n- PEFT 0.10.0\n- Transformers 4.39.3\n- Pytorch 2.2.2+cu121\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] |
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. -->
# GUE_prom_prom_300_tata-seqsight_16384_512_22M-L32_all
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_22M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_22M) on the [mahdibaghbanzadeh/GUE_prom_prom_300_tata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_300_tata) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8354
- F1 Score: 0.5971
- Accuracy: 0.5971
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 2048
- eval_batch_size: 2048
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|:--------:|
| 0.594 | 66.67 | 200 | 0.7979 | 0.6313 | 0.6313 |
| 0.3758 | 133.33 | 400 | 1.0549 | 0.6299 | 0.6297 |
| 0.267 | 200.0 | 600 | 1.2311 | 0.6129 | 0.6134 |
| 0.2214 | 266.67 | 800 | 1.3614 | 0.6036 | 0.6036 |
| 0.1977 | 333.33 | 1000 | 1.3433 | 0.6031 | 0.6052 |
| 0.184 | 400.0 | 1200 | 1.3919 | 0.6169 | 0.6166 |
| 0.1721 | 466.67 | 1400 | 1.4290 | 0.6038 | 0.6036 |
| 0.1647 | 533.33 | 1600 | 1.4838 | 0.6071 | 0.6069 |
| 0.155 | 600.0 | 1800 | 1.4811 | 0.6168 | 0.6166 |
| 0.1459 | 666.67 | 2000 | 1.5943 | 0.6165 | 0.6166 |
| 0.1422 | 733.33 | 2200 | 1.6284 | 0.6087 | 0.6101 |
| 0.1319 | 800.0 | 2400 | 1.7008 | 0.6137 | 0.6134 |
| 0.1237 | 866.67 | 2600 | 1.5816 | 0.6006 | 0.6003 |
| 0.1161 | 933.33 | 2800 | 1.8001 | 0.6025 | 0.6036 |
| 0.1101 | 1000.0 | 3000 | 1.7079 | 0.6068 | 0.6069 |
| 0.1036 | 1066.67 | 3200 | 1.8471 | 0.6071 | 0.6085 |
| 0.097 | 1133.33 | 3400 | 1.7883 | 0.6006 | 0.6003 |
| 0.093 | 1200.0 | 3600 | 1.9631 | 0.6131 | 0.6134 |
| 0.0873 | 1266.67 | 3800 | 1.9510 | 0.6115 | 0.6117 |
| 0.0842 | 1333.33 | 4000 | 1.8361 | 0.6099 | 0.6101 |
| 0.0803 | 1400.0 | 4200 | 1.9078 | 0.6080 | 0.6085 |
| 0.076 | 1466.67 | 4400 | 1.9444 | 0.6227 | 0.6232 |
| 0.0732 | 1533.33 | 4600 | 1.9880 | 0.6077 | 0.6085 |
| 0.0688 | 1600.0 | 4800 | 2.1511 | 0.5987 | 0.6003 |
| 0.067 | 1666.67 | 5000 | 2.1142 | 0.6097 | 0.6101 |
| 0.0651 | 1733.33 | 5200 | 2.1860 | 0.6090 | 0.6101 |
| 0.0628 | 1800.0 | 5400 | 2.0372 | 0.6212 | 0.6215 |
| 0.0606 | 1866.67 | 5600 | 2.2769 | 0.6128 | 0.6150 |
| 0.0588 | 1933.33 | 5800 | 2.1388 | 0.6094 | 0.6101 |
| 0.0562 | 2000.0 | 6000 | 2.1657 | 0.6111 | 0.6117 |
| 0.0548 | 2066.67 | 6200 | 2.0734 | 0.6165 | 0.6166 |
| 0.0539 | 2133.33 | 6400 | 2.0996 | 0.6127 | 0.6134 |
| 0.051 | 2200.0 | 6600 | 2.1679 | 0.6130 | 0.6134 |
| 0.0513 | 2266.67 | 6800 | 2.1512 | 0.6188 | 0.6199 |
| 0.0489 | 2333.33 | 7000 | 2.1352 | 0.6129 | 0.6134 |
| 0.0471 | 2400.0 | 7200 | 2.3141 | 0.6175 | 0.6183 |
| 0.0468 | 2466.67 | 7400 | 2.1969 | 0.6144 | 0.6150 |
| 0.0448 | 2533.33 | 7600 | 2.2664 | 0.6144 | 0.6150 |
| 0.0445 | 2600.0 | 7800 | 2.2993 | 0.6124 | 0.6134 |
| 0.0435 | 2666.67 | 8000 | 2.2378 | 0.6083 | 0.6085 |
| 0.0439 | 2733.33 | 8200 | 2.1876 | 0.6081 | 0.6085 |
| 0.0417 | 2800.0 | 8400 | 2.2377 | 0.6115 | 0.6117 |
| 0.0409 | 2866.67 | 8600 | 2.2993 | 0.6106 | 0.6117 |
| 0.0412 | 2933.33 | 8800 | 2.2438 | 0.6130 | 0.6134 |
| 0.04 | 3000.0 | 9000 | 2.2970 | 0.6104 | 0.6117 |
| 0.0404 | 3066.67 | 9200 | 2.3617 | 0.6174 | 0.6183 |
| 0.0392 | 3133.33 | 9400 | 2.2748 | 0.6161 | 0.6166 |
| 0.0394 | 3200.0 | 9600 | 2.3875 | 0.6168 | 0.6183 |
| 0.0382 | 3266.67 | 9800 | 2.3591 | 0.6156 | 0.6166 |
| 0.0381 | 3333.33 | 10000 | 2.3524 | 0.6156 | 0.6166 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_22M", "model-index": [{"name": "GUE_prom_prom_300_tata-seqsight_16384_512_22M-L32_all", "results": []}]} | mahdibaghbanzadeh/GUE_prom_prom_300_tata-seqsight_16384_512_22M-L32_all | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_22M",
"region:us"
] | null | 2024-04-16T06:37:44+00:00 | [] | [] | TAGS
#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_16384_512_22M #region-us
| GUE\_prom\_prom\_300\_tata-seqsight\_16384\_512\_22M-L32\_all
=============================================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_16384\_512\_22M on the mahdibaghbanzadeh/GUE\_prom\_prom\_300\_tata dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8354
* F1 Score: 0.5971
* Accuracy: 0.5971
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0005
* train\_batch\_size: 2048
* eval\_batch\_size: 2048
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 10000
### Training results
### Framework versions
* PEFT 0.9.0
* Transformers 4.38.2
* Pytorch 2.2.0+cu121
* Datasets 2.17.1
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 2048\n* eval\\_batch\\_size: 2048\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2"
] | [
"TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_16384_512_22M #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 2048\n* eval\\_batch\\_size: 2048\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2"
] |
null | transformers |
# DavidAU/electric-mist-7b-Q8_0-GGUF
This model was converted to GGUF format from [`maldv/electric-mist-7b`](https://huggingface.co/maldv/electric-mist-7b) 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/maldv/electric-mist-7b) 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 DavidAU/electric-mist-7b-Q8_0-GGUF --model electric-mist-7b.Q8_0.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo DavidAU/electric-mist-7b-Q8_0-GGUF --model electric-mist-7b.Q8_0.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 electric-mist-7b.Q8_0.gguf -n 128
```
| {"language": ["en"], "license": "cc-by-nc-4.0", "tags": ["text-generation-inference", "transformers", "unsloth", "mistral", "llama-cpp", "gguf-my-repo"], "datasets": ["maldv/cyberpunk", "microsoft/orca-math-word-problems-200k", "Weyaxi/sci-datasets", "grimulkan/theory-of-mind", "ResplendentAI/Synthetic_Soul_1k", "GraphWiz/GraphInstruct-RFT-72K"], "base_model": "alpindale/Mistral-7B-v0.2-hf"} | DavidAU/electric-mist-7b-Q8_0-GGUF | null | [
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"mistral",
"llama-cpp",
"gguf-my-repo",
"en",
"dataset:maldv/cyberpunk",
"dataset:microsoft/orca-math-word-problems-200k",
"dataset:Weyaxi/sci-datasets",
"dataset:grimulkan/theory-of-mind",
"dataset:ResplendentAI/Synthetic_Soul_1k",
"dataset:GraphWiz/GraphInstruct-RFT-72K",
"base_model:alpindale/Mistral-7B-v0.2-hf",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T06:38:01+00:00 | [] | [
"en"
] | TAGS
#transformers #gguf #text-generation-inference #unsloth #mistral #llama-cpp #gguf-my-repo #en #dataset-maldv/cyberpunk #dataset-microsoft/orca-math-word-problems-200k #dataset-Weyaxi/sci-datasets #dataset-grimulkan/theory-of-mind #dataset-ResplendentAI/Synthetic_Soul_1k #dataset-GraphWiz/GraphInstruct-RFT-72K #base_model-alpindale/Mistral-7B-v0.2-hf #license-cc-by-nc-4.0 #endpoints_compatible #region-us
|
# DavidAU/electric-mist-7b-Q8_0-GGUF
This model was converted to GGUF format from 'maldv/electric-mist-7b' 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.
| [
"# DavidAU/electric-mist-7b-Q8_0-GGUF\nThis model was converted to GGUF format from 'maldv/electric-mist-7b' 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 #text-generation-inference #unsloth #mistral #llama-cpp #gguf-my-repo #en #dataset-maldv/cyberpunk #dataset-microsoft/orca-math-word-problems-200k #dataset-Weyaxi/sci-datasets #dataset-grimulkan/theory-of-mind #dataset-ResplendentAI/Synthetic_Soul_1k #dataset-GraphWiz/GraphInstruct-RFT-72K #base_model-alpindale/Mistral-7B-v0.2-hf #license-cc-by-nc-4.0 #endpoints_compatible #region-us \n",
"# DavidAU/electric-mist-7b-Q8_0-GGUF\nThis model was converted to GGUF format from 'maldv/electric-mist-7b' 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."
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Medium GA-EN Speech Translation
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, and Wikimedia dataset.
The best model checkpoint (this version) is at step 1400, epoch 1.84 (4 x 0.46), and it achieves the following results on the evaluation set:
- Loss: 1.0240
- Bleu: 33.55
- Chrf: 50.95
- Wer: 60.1981
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0.03
- training_steps: 2000
- mixed_precision_training: Native AMP
### Hardware
4 x A40 48GB VRAM, with batch size 4 per machine (total: 16)
### Training results
| Training Loss | Epoch | Step | Bleu | Chrf | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:-----:|:-----:|:---------------:|:--------:|
| 2.9468 | 0.03 | 100 | 4.72 | 20.55 | 2.2829 | 120.6213 |
| 2.5074 | 0.07 | 200 | 7.81 | 25.23 | 2.0136 | 114.8131 |
| 2.2406 | 0.1 | 300 | 11.24 | 29.39 | 1.8224 | 95.9928 |
| 2.2466 | 0.13 | 400 | 16.01 | 34.73 | 1.6530 | 83.4309 |
| 2.0276 | 0.16 | 500 | 16.69 | 34.76 | 1.5344 | 94.2368 |
| 1.8429 | 0.2 | 600 | 21.37 | 37.48 | 1.4923 | 78.5682 |
| 1.7621 | 0.23 | 700 | 23.4 | 40.89 | 1.3666 | 74.3359 |
| 1.5629 | 0.26 | 800 | 24.76 | 44.63 | 1.2876 | 76.6321 |
| 1.5458 | 0.3 | 900 | 25.81 | 44.59 | 1.2178 | 72.6249 |
| 1.2971 | 0.33 | 1000 | 27.63 | 46.91 | 1.1823 | 70.2837 |
| 1.3852 | 0.36 | 1100 | 27.18 | 46.16 | 1.2303 | 70.6889 |
| 1.309 | 0.39 | 1200 | 27.65 | 47.41 | 1.1573 | 72.0396 |
| 1.1818 | 0.43 | 1300 | 31.17 | 48.36 | 1.1304 | 61.6389 |
| 1.2711 | 0.46 | 1400 | 33.55 | 50.95 | 1.0839 | 60.1981 |
| 1.1305 | 0.49 | 1500 | 30.37 | 50.78 | 1.0718 | 68.6628 |
| 1.0544 | 0.53 | 1600 | 26.98 | 48.1 | 1.1109 | 73.7506 |
| 1.125 | 0.56 | 1700 | 30.76 | 50.19 | 1.0709 | 61.7740 |
| 1.1348 | 0.59 | 1800 | 33.71 | 50.6 | 1.0530 | 59.9280 |
| 1.14 | 0.62 | 1900 | 31.45 | 50.16 | 1.0392 | 66.9068 |
| 1.1059 | 0.66 | 2000 | 32.14 | 50.84 | 1.0240 | 65.9613 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.0.1+cu118
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"language": ["ga", "en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["ymoslem/IWSLT2023-GA-EN", "ymoslem/FLEURS-GA-EN", "ymoslem/BitesizeIrish-GA-EN", "ymoslem/SpokenWords-GA-EN-MTed"], "metrics": ["bleu", "wer"], "base_model": "openai/whisper-medium", "model-index": [{"name": "Whisper Medium GA-EN Speech Translation", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, and Wikimedia", "type": "ymoslem/IWSLT2023-GA-EN"}, "metrics": [{"type": "bleu", "value": 32.14, "name": "Bleu"}, {"type": "wer", "value": 65.96127870328681, "name": "Wer"}]}]}]} | ymoslem/whisper-medium-ga2en-v2 | null | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"ga",
"en",
"dataset:ymoslem/IWSLT2023-GA-EN",
"dataset:ymoslem/FLEURS-GA-EN",
"dataset:ymoslem/BitesizeIrish-GA-EN",
"dataset:ymoslem/SpokenWords-GA-EN-MTed",
"base_model:openai/whisper-medium",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T06:40:08+00:00 | [] | [
"ga",
"en"
] | TAGS
#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #ga #en #dataset-ymoslem/IWSLT2023-GA-EN #dataset-ymoslem/FLEURS-GA-EN #dataset-ymoslem/BitesizeIrish-GA-EN #dataset-ymoslem/SpokenWords-GA-EN-MTed #base_model-openai/whisper-medium #license-apache-2.0 #model-index #endpoints_compatible #region-us
| Whisper Medium GA-EN Speech Translation
=======================================
This model is a fine-tuned version of openai/whisper-medium on the IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, and Wikimedia dataset.
The best model checkpoint (this version) is at step 1400, epoch 1.84 (4 x 0.46), and it achieves the following results on the evaluation set:
* Loss: 1.0240
* Bleu: 33.55
* Chrf: 50.95
* Wer: 60.1981
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0001
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 0.03
* training\_steps: 2000
* mixed\_precision\_training: Native AMP
### Hardware
4 x A40 48GB VRAM, with batch size 4 per machine (total: 16)
### Training results
### Framework versions
* Transformers 4.39.3
* Pytorch 2.0.1+cu118
* Datasets 2.18.0
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 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: 0.03\n* training\\_steps: 2000\n* mixed\\_precision\\_training: Native AMP",
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"### Hardware\n\n\n4 x A40 48GB VRAM, with batch size 4 per machine (total: 16)",
"### Training results",
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] |
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. -->
# distilbert-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion 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: 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
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-emotion", "results": []}]} | Praveenna/distilbert-emotion | null | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T06:42:06+00:00 | [] | [] | TAGS
#transformers #safetensors #distilbert #text-classification #generated_from_trainer #dataset-emotion #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# distilbert-emotion
This model is a fine-tuned version of distilbert-base-uncased on the emotion 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: 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
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| [
"# distilbert-emotion\n\nThis model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.",
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"## 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: 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",
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"# distilbert-emotion\n\nThis model is a fine-tuned version of distilbert-base-uncased on the emotion 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: 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",
"### 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]
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### 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]
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[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": []} | OwOOwO/dumbo-krillin10 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-16T06:43:10+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.",
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"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
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"### Testing Data, Factors & Metrics",
"#### Testing Data",
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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:",
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"## Model Card Contact"
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"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]:",
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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:",
"#### Speeds, Sizes, Times [optional]",
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"#### Testing Data",
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"#### Metrics",
"### Results",
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"## Technical Specifications [optional]",
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"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
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. -->
# bert2bert-extabs-canonicalcleandata-lr-5e-05-batchsize-4-encmaxlen-512-decmaxlen-256
10 Epoch Extractive Training + 10 Epoch Abtractive Training
- Dev Set: Canonical Clean Data & Extreme Clean Data
- Encoder max length (input): 512
- Decoder max length (output): 256
This model was trained from scratch on the id_liputan6 dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0021
- R1 Precision: 0.3553
- R1 Recall: 0.2599
- R1 Fmeasure: 0.2974
- R2 Precision: 0.1458
- R2 Recall: 0.1039
- R2 Fmeasure: 0.12
- Rl Precision: 0.2925
- Rl Recall: 0.2139
- Rl Fmeasure: 0.2448
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 18
- eval_batch_size: 18
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | R1 Precision | R1 Recall | R1 Fmeasure | R2 Precision | R2 Recall | R2 Fmeasure | Rl Precision | Rl Recall | Rl Fmeasure |
|:-------------:|:-----:|:------:|:---------------:|:------------:|:---------:|:-----------:|:------------:|:---------:|:-----------:|:------------:|:---------:|:-----------:|
| 1.7393 | 1.0 | 10772 | 2.6782 | 0.3432 | 0.2497 | 0.2864 | 0.1375 | 0.0975 | 0.113 | 0.2828 | 0.206 | 0.2361 |
| 1.4091 | 2.0 | 21544 | 2.6063 | 0.3486 | 0.2534 | 0.2907 | 0.142 | 0.1004 | 0.1164 | 0.2878 | 0.2094 | 0.2401 |
| 1.246 | 3.0 | 32316 | 2.6079 | 0.3535 | 0.2578 | 0.2955 | 0.1457 | 0.1036 | 0.1199 | 0.2917 | 0.2131 | 0.244 |
| 1.1175 | 4.0 | 43088 | 2.6382 | 0.3579 | 0.2618 | 0.2996 | 0.1488 | 0.106 | 0.1225 | 0.2956 | 0.2163 | 0.2475 |
| 1.0102 | 5.0 | 53860 | 2.6818 | 0.3574 | 0.2609 | 0.2987 | 0.1478 | 0.1052 | 0.1217 | 0.2949 | 0.2154 | 0.2466 |
| 0.9141 | 6.0 | 64632 | 2.7428 | 0.3571 | 0.2616 | 0.2992 | 0.148 | 0.1056 | 0.122 | 0.2938 | 0.2152 | 0.2461 |
| 0.8261 | 7.0 | 75404 | 2.8255 | 0.3534 | 0.2582 | 0.2956 | 0.1457 | 0.1039 | 0.12 | 0.2906 | 0.2126 | 0.2432 |
| 0.7509 | 8.0 | 86176 | 2.8975 | 0.3517 | 0.2572 | 0.2943 | 0.1428 | 0.1016 | 0.1175 | 0.289 | 0.2113 | 0.2418 |
| 0.6822 | 9.0 | 96948 | 2.9586 | 0.3557 | 0.2599 | 0.2975 | 0.1466 | 0.1043 | 0.1206 | 0.2936 | 0.2145 | 0.2455 |
| 0.6289 | 10.0 | 107720 | 3.0021 | 0.3553 | 0.2599 | 0.2974 | 0.1458 | 0.1039 | 0.12 | 0.2925 | 0.2139 | 0.2448 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"tags": ["generated_from_trainer"], "datasets": ["id_liputan6"], "model-index": [{"name": "bert2bert-extabs-canonicalcleandata-lr-5e-05-batchsize-4-encmaxlen-512-decmaxlen-256", "results": []}]} | Alfahluzi/bert2bert-model2 | null | [
"transformers",
"tensorboard",
"safetensors",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"dataset:id_liputan6",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T06:43:11+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #encoder-decoder #text2text-generation #generated_from_trainer #dataset-id_liputan6 #autotrain_compatible #endpoints_compatible #region-us
| bert2bert-extabs-canonicalcleandata-lr-5e-05-batchsize-4-encmaxlen-512-decmaxlen-256
====================================================================================
10 Epoch Extractive Training + 10 Epoch Abtractive Training
* Dev Set: Canonical Clean Data & Extreme Clean Data
* Encoder max length (input): 512
* Decoder max length (output): 256
This model was trained from scratch on the id\_liputan6 dataset.
It achieves the following results on the evaluation set:
* Loss: 3.0021
* R1 Precision: 0.3553
* R1 Recall: 0.2599
* R1 Fmeasure: 0.2974
* R2 Precision: 0.1458
* R2 Recall: 0.1039
* R2 Fmeasure: 0.12
* Rl Precision: 0.2925
* Rl Recall: 0.2139
* Rl Fmeasure: 0.2448
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 18
* eval\_batch\_size: 18
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.39.3
* Pytorch 2.2.1
* Datasets 2.18.0
* Tokenizers 0.15.2
| [
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"### Framework versions\n\n\n* Transformers 4.39.3\n* Pytorch 2.2.1\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] |
text-generation | transformers | # model_storage
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:
* [davidkim205/nox-solar-10.7b-v2](https://huggingface.co/davidkim205/nox-solar-10.7b-v2)
* [chihoonlee10/T3Q-ko-solar-dpo-v6.0](https://huggingface.co/chihoonlee10/T3Q-ko-solar-dpo-v6.0)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
base_model:
model:
path: chihoonlee10/T3Q-ko-solar-dpo-v6.0
dtype: float16
merge_method: slerp
parameters:
t:
- filter: self_attn
value: [0.0, 0.5, 0.3, 0.7, 1.0]
- filter: mlp
value: [1.0, 0.5, 0.7, 0.3, 0.0]
- value: 0.5
slices:
- sources:
- layer_range: [0, 47]
model:
model:
path: chihoonlee10/T3Q-ko-solar-dpo-v6.0
- layer_range: [0, 47]
model:
model:
path: davidkim205/nox-solar-10.7b-v2
```
| {"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["davidkim205/nox-solar-10.7b-v2", "chihoonlee10/T3Q-ko-solar-dpo-v6.0"]} | nebchi/solar-merge-slerp | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"base_model:davidkim205/nox-solar-10.7b-v2",
"base_model:chihoonlee10/T3Q-ko-solar-dpo-v6.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-16T06:45:54+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #mergekit #merge #base_model-davidkim205/nox-solar-10.7b-v2 #base_model-chihoonlee10/T3Q-ko-solar-dpo-v6.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # model_storage
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:
* davidkim205/nox-solar-10.7b-v2
* chihoonlee10/T3Q-ko-solar-dpo-v6.0
### Configuration
The following YAML configuration was used to produce this model:
| [
"# model_storage\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* davidkim205/nox-solar-10.7b-v2\n* chihoonlee10/T3Q-ko-solar-dpo-v6.0",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #base_model-davidkim205/nox-solar-10.7b-v2 #base_model-chihoonlee10/T3Q-ko-solar-dpo-v6.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# model_storage\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* davidkim205/nox-solar-10.7b-v2\n* chihoonlee10/T3Q-ko-solar-dpo-v6.0",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] |
text-generation | transformers | # slerp_test2
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:
* [JY623/KoSOLAR-10.7B-merge-v3.1](https://huggingface.co/JY623/KoSOLAR-10.7B-merge-v3.1)
* [JY623/KoSOLAR-10.7B-merge-v3.0](https://huggingface.co/JY623/KoSOLAR-10.7B-merge-v3.0)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: JY623/KoSOLAR-10.7B-merge-v3.0
layer_range: [0, 48]
- model: JY623/KoSOLAR-10.7B-merge-v3.1
layer_range: [0, 48]
merge_method: slerp
base_model: JY623/KoSOLAR-10.7B-merge-v3.0
parameters:
t: 0.2
dtype: bfloat16
``` | {"license": "apache-2.0", "library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["JY623/KoSOLAR-10.7B-merge-v3.1", "JY623/KoSOLAR-10.7B-merge-v3.0"]} | JY623/KoSOLAR-10.7B-merge-v3.2 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"base_model:JY623/KoSOLAR-10.7B-merge-v3.1",
"base_model:JY623/KoSOLAR-10.7B-merge-v3.0",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-16T06:46:39+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #mergekit #merge #base_model-JY623/KoSOLAR-10.7B-merge-v3.1 #base_model-JY623/KoSOLAR-10.7B-merge-v3.0 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # slerp_test2
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:
* JY623/KoSOLAR-10.7B-merge-v3.1
* JY623/KoSOLAR-10.7B-merge-v3.0
### Configuration
The following YAML configuration was used to produce this model:
| [
"# slerp_test2\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* JY623/KoSOLAR-10.7B-merge-v3.1\n* JY623/KoSOLAR-10.7B-merge-v3.0",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #base_model-JY623/KoSOLAR-10.7B-merge-v3.1 #base_model-JY623/KoSOLAR-10.7B-merge-v3.0 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# slerp_test2\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* JY623/KoSOLAR-10.7B-merge-v3.1\n* JY623/KoSOLAR-10.7B-merge-v3.0",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] |
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. -->
# GUE_prom_prom_300_notata-seqsight_16384_512_22M-L32_all
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_22M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_22M) on the [mahdibaghbanzadeh/GUE_prom_prom_300_notata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_300_notata) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3362
- F1 Score: 0.8564
- Accuracy: 0.8564
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 2048
- eval_batch_size: 2048
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:|
| 0.5772 | 9.52 | 200 | 0.4902 | 0.7637 | 0.7665 |
| 0.4746 | 19.05 | 400 | 0.4525 | 0.7893 | 0.7893 |
| 0.4425 | 28.57 | 600 | 0.4262 | 0.8037 | 0.8042 |
| 0.4185 | 38.1 | 800 | 0.4097 | 0.8143 | 0.8146 |
| 0.3935 | 47.62 | 1000 | 0.3838 | 0.8330 | 0.8331 |
| 0.3596 | 57.14 | 1200 | 0.3649 | 0.8408 | 0.8408 |
| 0.3367 | 66.67 | 1400 | 0.3501 | 0.8490 | 0.8491 |
| 0.3212 | 76.19 | 1600 | 0.3435 | 0.8540 | 0.8540 |
| 0.3079 | 85.71 | 1800 | 0.3419 | 0.8551 | 0.8551 |
| 0.2958 | 95.24 | 2000 | 0.3274 | 0.8580 | 0.8581 |
| 0.285 | 104.76 | 2200 | 0.3248 | 0.8598 | 0.8598 |
| 0.2744 | 114.29 | 2400 | 0.3362 | 0.8634 | 0.8634 |
| 0.2663 | 123.81 | 2600 | 0.3264 | 0.8660 | 0.8660 |
| 0.2611 | 133.33 | 2800 | 0.3258 | 0.8668 | 0.8668 |
| 0.255 | 142.86 | 3000 | 0.3487 | 0.8593 | 0.8594 |
| 0.2503 | 152.38 | 3200 | 0.3257 | 0.8668 | 0.8668 |
| 0.2458 | 161.9 | 3400 | 0.3205 | 0.8696 | 0.8696 |
| 0.2415 | 171.43 | 3600 | 0.3327 | 0.8662 | 0.8662 |
| 0.238 | 180.95 | 3800 | 0.3281 | 0.8643 | 0.8643 |
| 0.2333 | 190.48 | 4000 | 0.3591 | 0.8614 | 0.8615 |
| 0.2314 | 200.0 | 4200 | 0.3332 | 0.8668 | 0.8668 |
| 0.2289 | 209.52 | 4400 | 0.3461 | 0.8629 | 0.8630 |
| 0.2251 | 219.05 | 4600 | 0.3436 | 0.8614 | 0.8615 |
| 0.2246 | 228.57 | 4800 | 0.3590 | 0.8602 | 0.8604 |
| 0.2222 | 238.1 | 5000 | 0.3480 | 0.8654 | 0.8655 |
| 0.218 | 247.62 | 5200 | 0.3483 | 0.8658 | 0.8658 |
| 0.2181 | 257.14 | 5400 | 0.3538 | 0.8634 | 0.8636 |
| 0.2162 | 266.67 | 5600 | 0.3556 | 0.8667 | 0.8668 |
| 0.214 | 276.19 | 5800 | 0.3546 | 0.8669 | 0.8670 |
| 0.2123 | 285.71 | 6000 | 0.3482 | 0.8673 | 0.8673 |
| 0.212 | 295.24 | 6200 | 0.3576 | 0.8670 | 0.8670 |
| 0.2104 | 304.76 | 6400 | 0.3614 | 0.8654 | 0.8655 |
| 0.2098 | 314.29 | 6600 | 0.3566 | 0.8662 | 0.8662 |
| 0.2078 | 323.81 | 6800 | 0.3523 | 0.8650 | 0.8651 |
| 0.2068 | 333.33 | 7000 | 0.3640 | 0.8637 | 0.8638 |
| 0.2052 | 342.86 | 7200 | 0.3609 | 0.8635 | 0.8636 |
| 0.2032 | 352.38 | 7400 | 0.3545 | 0.8651 | 0.8651 |
| 0.2029 | 361.9 | 7600 | 0.3507 | 0.8652 | 0.8653 |
| 0.203 | 371.43 | 7800 | 0.3536 | 0.8655 | 0.8655 |
| 0.2018 | 380.95 | 8000 | 0.3539 | 0.8671 | 0.8672 |
| 0.2013 | 390.48 | 8200 | 0.3544 | 0.8681 | 0.8681 |
| 0.2 | 400.0 | 8400 | 0.3586 | 0.8662 | 0.8662 |
| 0.1994 | 409.52 | 8600 | 0.3636 | 0.8632 | 0.8632 |
| 0.1992 | 419.05 | 8800 | 0.3594 | 0.8645 | 0.8645 |
| 0.1986 | 428.57 | 9000 | 0.3572 | 0.8656 | 0.8656 |
| 0.198 | 438.1 | 9200 | 0.3631 | 0.8633 | 0.8634 |
| 0.1969 | 447.62 | 9400 | 0.3601 | 0.8656 | 0.8656 |
| 0.1974 | 457.14 | 9600 | 0.3589 | 0.8662 | 0.8662 |
| 0.1975 | 466.67 | 9800 | 0.3581 | 0.8654 | 0.8655 |
| 0.1973 | 476.19 | 10000 | 0.3593 | 0.8647 | 0.8647 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_22M", "model-index": [{"name": "GUE_prom_prom_300_notata-seqsight_16384_512_22M-L32_all", "results": []}]} | mahdibaghbanzadeh/GUE_prom_prom_300_notata-seqsight_16384_512_22M-L32_all | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_22M",
"region:us"
] | null | 2024-04-16T06:48:51+00:00 | [] | [] | TAGS
#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_16384_512_22M #region-us
| GUE\_prom\_prom\_300\_notata-seqsight\_16384\_512\_22M-L32\_all
===============================================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_16384\_512\_22M on the mahdibaghbanzadeh/GUE\_prom\_prom\_300\_notata dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3362
* F1 Score: 0.8564
* Accuracy: 0.8564
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0005
* train\_batch\_size: 2048
* eval\_batch\_size: 2048
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 10000
### Training results
### Framework versions
* PEFT 0.9.0
* Transformers 4.38.2
* Pytorch 2.2.0+cu121
* Datasets 2.17.1
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 2048\n* eval\\_batch\\_size: 2048\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2"
] | [
"TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_16384_512_22M #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 2048\n* eval\\_batch\\_size: 2048\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2"
] |
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. -->
# GUE_prom_prom_core_all-seqsight_16384_512_22M-L32_all
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_22M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_22M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_all](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_all) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5969
- F1 Score: 0.7051
- Accuracy: 0.7051
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 2048
- eval_batch_size: 2048
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:|
| 0.6534 | 8.33 | 200 | 0.6127 | 0.6644 | 0.6647 |
| 0.6042 | 16.67 | 400 | 0.5937 | 0.6868 | 0.6878 |
| 0.585 | 25.0 | 600 | 0.5858 | 0.6930 | 0.6949 |
| 0.5691 | 33.33 | 800 | 0.5818 | 0.6998 | 0.7 |
| 0.5555 | 41.67 | 1000 | 0.5794 | 0.7025 | 0.7025 |
| 0.5451 | 50.0 | 1200 | 0.5739 | 0.7067 | 0.7068 |
| 0.5354 | 58.33 | 1400 | 0.5730 | 0.7064 | 0.7064 |
| 0.5287 | 66.67 | 1600 | 0.5770 | 0.7060 | 0.7061 |
| 0.5227 | 75.0 | 1800 | 0.5775 | 0.7028 | 0.7029 |
| 0.5168 | 83.33 | 2000 | 0.5750 | 0.7070 | 0.7071 |
| 0.5136 | 91.67 | 2200 | 0.5747 | 0.7031 | 0.7032 |
| 0.5066 | 100.0 | 2400 | 0.5749 | 0.7101 | 0.7101 |
| 0.5038 | 108.33 | 2600 | 0.5885 | 0.7066 | 0.7071 |
| 0.4998 | 116.67 | 2800 | 0.5957 | 0.7067 | 0.7068 |
| 0.4949 | 125.0 | 3000 | 0.5748 | 0.7087 | 0.7090 |
| 0.4919 | 133.33 | 3200 | 0.5937 | 0.7058 | 0.7064 |
| 0.4884 | 141.67 | 3400 | 0.5876 | 0.7029 | 0.7035 |
| 0.4857 | 150.0 | 3600 | 0.5799 | 0.7129 | 0.7132 |
| 0.4824 | 158.33 | 3800 | 0.5979 | 0.7080 | 0.7084 |
| 0.4806 | 166.67 | 4000 | 0.5895 | 0.7077 | 0.7088 |
| 0.4758 | 175.0 | 4200 | 0.5952 | 0.7046 | 0.7057 |
| 0.474 | 183.33 | 4400 | 0.5880 | 0.7131 | 0.7132 |
| 0.4708 | 191.67 | 4600 | 0.5841 | 0.7135 | 0.7139 |
| 0.4686 | 200.0 | 4800 | 0.5902 | 0.7125 | 0.7127 |
| 0.4649 | 208.33 | 5000 | 0.5926 | 0.7142 | 0.7144 |
| 0.464 | 216.67 | 5200 | 0.5935 | 0.7092 | 0.7098 |
| 0.4619 | 225.0 | 5400 | 0.6059 | 0.7022 | 0.7037 |
| 0.4583 | 233.33 | 5600 | 0.5904 | 0.7124 | 0.7125 |
| 0.4565 | 241.67 | 5800 | 0.6008 | 0.7126 | 0.7128 |
| 0.455 | 250.0 | 6000 | 0.5984 | 0.7116 | 0.7120 |
| 0.4519 | 258.33 | 6200 | 0.5892 | 0.7096 | 0.7100 |
| 0.4508 | 266.67 | 6400 | 0.5943 | 0.7098 | 0.7101 |
| 0.4493 | 275.0 | 6600 | 0.5935 | 0.7076 | 0.7078 |
| 0.4467 | 283.33 | 6800 | 0.6051 | 0.7071 | 0.7074 |
| 0.4457 | 291.67 | 7000 | 0.6103 | 0.7025 | 0.7035 |
| 0.4452 | 300.0 | 7200 | 0.5967 | 0.7079 | 0.7083 |
| 0.4421 | 308.33 | 7400 | 0.6110 | 0.7059 | 0.7071 |
| 0.4417 | 316.67 | 7600 | 0.6163 | 0.7014 | 0.7032 |
| 0.4399 | 325.0 | 7800 | 0.6253 | 0.7013 | 0.7025 |
| 0.4377 | 333.33 | 8000 | 0.6139 | 0.7053 | 0.7063 |
| 0.4368 | 341.67 | 8200 | 0.6145 | 0.7070 | 0.7073 |
| 0.4375 | 350.0 | 8400 | 0.6128 | 0.7045 | 0.7051 |
| 0.4356 | 358.33 | 8600 | 0.6098 | 0.7071 | 0.7074 |
| 0.4344 | 366.67 | 8800 | 0.6091 | 0.7024 | 0.7032 |
| 0.4331 | 375.0 | 9000 | 0.6130 | 0.7030 | 0.7037 |
| 0.4331 | 383.33 | 9200 | 0.6141 | 0.7057 | 0.7064 |
| 0.4321 | 391.67 | 9400 | 0.6160 | 0.7039 | 0.7047 |
| 0.4306 | 400.0 | 9600 | 0.6180 | 0.7042 | 0.7049 |
| 0.4304 | 408.33 | 9800 | 0.6200 | 0.7031 | 0.7041 |
| 0.4311 | 416.67 | 10000 | 0.6166 | 0.7045 | 0.7051 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_22M", "model-index": [{"name": "GUE_prom_prom_core_all-seqsight_16384_512_22M-L32_all", "results": []}]} | mahdibaghbanzadeh/GUE_prom_prom_core_all-seqsight_16384_512_22M-L32_all | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_22M",
"region:us"
] | null | 2024-04-16T06:49:37+00:00 | [] | [] | TAGS
#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_16384_512_22M #region-us
| GUE\_prom\_prom\_core\_all-seqsight\_16384\_512\_22M-L32\_all
=============================================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_16384\_512\_22M on the mahdibaghbanzadeh/GUE\_prom\_prom\_core\_all dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5969
* F1 Score: 0.7051
* Accuracy: 0.7051
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0005
* train\_batch\_size: 2048
* eval\_batch\_size: 2048
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 10000
### Training results
### Framework versions
* PEFT 0.9.0
* Transformers 4.38.2
* Pytorch 2.2.0+cu121
* Datasets 2.17.1
* Tokenizers 0.15.2
| [
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"### Training results",
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"TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_16384_512_22M #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 2048\n* eval\\_batch\\_size: 2048\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
"### Training results",
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] |
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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## 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. -->
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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#### 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]
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## Technical Specifications [optional]
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[More Information Needed]
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<!-- 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": []} | swj0419/bbc_retrain_STEP0000040 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-16T06:50:34+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]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
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"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
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"#### Metrics",
"### Results",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
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"## Model Card Contact"
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"TAGS\n#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
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"## 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",
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"#### Metrics",
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"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
text-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. -->
# meditron-7b-wo-kqa_silver_wogold-sft
This model is a fine-tuned version of [epfl-llm/meditron-7b](https://huggingface.co/epfl-llm/meditron-7b) on the HuggingFaceH4/deita-10k-v0-sft dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8975
## 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: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.1532 | 0.87 | 5 | 1.0827 |
| 0.9871 | 1.91 | 11 | 0.9194 |
| 0.8631 | 2.61 | 15 | 0.8975 |
### Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.1.2
- Datasets 2.14.6
- Tokenizers 0.15.2
| {"license": "llama2", "tags": ["alignment-handbook", "trl", "sft", "generated_from_trainer", "trl", "sft", "generated_from_trainer"], "datasets": ["HuggingFaceH4/deita-10k-v0-sft"], "base_model": "epfl-llm/meditron-7b", "model-index": [{"name": "meditron-7b-wo-kqa_silver_wogold-sft", "results": []}]} | Minbyul/meditron-7b-wo-kqa_silver_wogold-sft | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"alignment-handbook",
"trl",
"sft",
"generated_from_trainer",
"dataset:HuggingFaceH4/deita-10k-v0-sft",
"base_model:epfl-llm/meditron-7b",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-16T06:51:29+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #alignment-handbook #trl #sft #generated_from_trainer #dataset-HuggingFaceH4/deita-10k-v0-sft #base_model-epfl-llm/meditron-7b #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| meditron-7b-wo-kqa\_silver\_wogold-sft
======================================
This model is a fine-tuned version of epfl-llm/meditron-7b on the HuggingFaceH4/deita-10k-v0-sft dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8975
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: 4
* eval\_batch\_size: 4
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 4
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* total\_eval\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.39.0.dev0
* Pytorch 2.1.2
* Datasets 2.14.6
* 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: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 4\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* total\\_eval\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 3",
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] |
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. -->
# GUE_prom_prom_core_notata-seqsight_16384_512_22M-L32_all
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_22M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_22M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_notata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_notata) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5802
- F1 Score: 0.7160
- Accuracy: 0.7160
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 2048
- eval_batch_size: 2048
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:|
| 0.6477 | 9.52 | 200 | 0.5985 | 0.6851 | 0.6863 |
| 0.5922 | 19.05 | 400 | 0.5761 | 0.7034 | 0.7036 |
| 0.5682 | 28.57 | 600 | 0.5697 | 0.7135 | 0.7138 |
| 0.5501 | 38.1 | 800 | 0.5640 | 0.7177 | 0.7177 |
| 0.5344 | 47.62 | 1000 | 0.5587 | 0.7222 | 0.7224 |
| 0.5229 | 57.14 | 1200 | 0.5560 | 0.7313 | 0.7313 |
| 0.5141 | 66.67 | 1400 | 0.5495 | 0.7339 | 0.7339 |
| 0.5065 | 76.19 | 1600 | 0.5478 | 0.7364 | 0.7364 |
| 0.499 | 85.71 | 1800 | 0.5484 | 0.7344 | 0.7345 |
| 0.4927 | 95.24 | 2000 | 0.5577 | 0.7367 | 0.7368 |
| 0.4874 | 104.76 | 2200 | 0.5572 | 0.7345 | 0.7345 |
| 0.4795 | 114.29 | 2400 | 0.5518 | 0.7351 | 0.7351 |
| 0.4759 | 123.81 | 2600 | 0.5569 | 0.7362 | 0.7362 |
| 0.4712 | 133.33 | 2800 | 0.5571 | 0.7339 | 0.7339 |
| 0.4664 | 142.86 | 3000 | 0.5575 | 0.7281 | 0.7287 |
| 0.4608 | 152.38 | 3200 | 0.5622 | 0.7355 | 0.7354 |
| 0.457 | 161.9 | 3400 | 0.5571 | 0.7335 | 0.7336 |
| 0.4518 | 171.43 | 3600 | 0.5716 | 0.7281 | 0.7287 |
| 0.4479 | 180.95 | 3800 | 0.5673 | 0.7228 | 0.7239 |
| 0.4435 | 190.48 | 4000 | 0.5713 | 0.7215 | 0.7221 |
| 0.4398 | 200.0 | 4200 | 0.5829 | 0.7345 | 0.7345 |
| 0.435 | 209.52 | 4400 | 0.5769 | 0.7265 | 0.7270 |
| 0.4326 | 219.05 | 4600 | 0.5762 | 0.7282 | 0.7285 |
| 0.4286 | 228.57 | 4800 | 0.5749 | 0.7311 | 0.7311 |
| 0.4247 | 238.1 | 5000 | 0.5846 | 0.7303 | 0.7307 |
| 0.4231 | 247.62 | 5200 | 0.5876 | 0.7311 | 0.7313 |
| 0.4192 | 257.14 | 5400 | 0.5797 | 0.7317 | 0.7321 |
| 0.4168 | 266.67 | 5600 | 0.5908 | 0.7295 | 0.7296 |
| 0.4138 | 276.19 | 5800 | 0.6108 | 0.7205 | 0.7217 |
| 0.4109 | 285.71 | 6000 | 0.5874 | 0.7271 | 0.7273 |
| 0.4087 | 295.24 | 6200 | 0.6094 | 0.7274 | 0.7279 |
| 0.4056 | 304.76 | 6400 | 0.6137 | 0.7237 | 0.7251 |
| 0.4029 | 314.29 | 6600 | 0.5969 | 0.7229 | 0.7234 |
| 0.4006 | 323.81 | 6800 | 0.6054 | 0.7284 | 0.7288 |
| 0.3983 | 333.33 | 7000 | 0.6050 | 0.7279 | 0.7283 |
| 0.3954 | 342.86 | 7200 | 0.6094 | 0.7223 | 0.7230 |
| 0.3946 | 352.38 | 7400 | 0.6067 | 0.7260 | 0.7262 |
| 0.3935 | 361.9 | 7600 | 0.6080 | 0.7259 | 0.7262 |
| 0.3907 | 371.43 | 7800 | 0.6118 | 0.7259 | 0.7262 |
| 0.3907 | 380.95 | 8000 | 0.6142 | 0.7264 | 0.7268 |
| 0.3881 | 390.48 | 8200 | 0.6193 | 0.7239 | 0.7243 |
| 0.3867 | 400.0 | 8400 | 0.6040 | 0.7234 | 0.7236 |
| 0.3856 | 409.52 | 8600 | 0.6176 | 0.7213 | 0.7221 |
| 0.3813 | 419.05 | 8800 | 0.6185 | 0.7230 | 0.7236 |
| 0.3836 | 428.57 | 9000 | 0.6124 | 0.7203 | 0.7207 |
| 0.3816 | 438.1 | 9200 | 0.6200 | 0.7243 | 0.7249 |
| 0.3811 | 447.62 | 9400 | 0.6194 | 0.7227 | 0.7232 |
| 0.3805 | 457.14 | 9600 | 0.6214 | 0.7218 | 0.7224 |
| 0.3799 | 466.67 | 9800 | 0.6197 | 0.7208 | 0.7213 |
| 0.3801 | 476.19 | 10000 | 0.6192 | 0.7211 | 0.7217 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_22M", "model-index": [{"name": "GUE_prom_prom_core_notata-seqsight_16384_512_22M-L32_all", "results": []}]} | mahdibaghbanzadeh/GUE_prom_prom_core_notata-seqsight_16384_512_22M-L32_all | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_22M",
"region:us"
] | null | 2024-04-16T06:51:38+00:00 | [] | [] | TAGS
#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_16384_512_22M #region-us
| GUE\_prom\_prom\_core\_notata-seqsight\_16384\_512\_22M-L32\_all
================================================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_16384\_512\_22M on the mahdibaghbanzadeh/GUE\_prom\_prom\_core\_notata dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5802
* F1 Score: 0.7160
* Accuracy: 0.7160
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0005
* train\_batch\_size: 2048
* eval\_batch\_size: 2048
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 10000
### Training results
### Framework versions
* PEFT 0.9.0
* Transformers 4.38.2
* Pytorch 2.2.0+cu121
* Datasets 2.17.1
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 2048\n* eval\\_batch\\_size: 2048\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2"
] | [
"TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_16384_512_22M #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 2048\n* eval\\_batch\\_size: 2048\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2"
] |
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. -->
# GUE_prom_prom_core_tata-seqsight_16384_512_22M-L32_all
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_22M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_22M) on the [mahdibaghbanzadeh/GUE_prom_prom_core_tata](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_core_tata) dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2856
- F1 Score: 0.7125
- Accuracy: 0.7129
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 2048
- eval_batch_size: 2048
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|:--------:|
| 0.5676 | 66.67 | 200 | 0.6626 | 0.6813 | 0.6819 |
| 0.3539 | 133.33 | 400 | 0.7576 | 0.6819 | 0.6819 |
| 0.2535 | 200.0 | 600 | 0.8903 | 0.6920 | 0.6933 |
| 0.2066 | 266.67 | 800 | 0.9645 | 0.6785 | 0.6786 |
| 0.1824 | 333.33 | 1000 | 0.9986 | 0.6899 | 0.6900 |
| 0.1673 | 400.0 | 1200 | 1.0135 | 0.6980 | 0.6982 |
| 0.1528 | 466.67 | 1400 | 1.0369 | 0.6975 | 0.6982 |
| 0.1427 | 533.33 | 1600 | 1.0808 | 0.6998 | 0.6998 |
| 0.1303 | 600.0 | 1800 | 1.0985 | 0.6946 | 0.6949 |
| 0.1212 | 666.67 | 2000 | 1.1009 | 0.7055 | 0.7064 |
| 0.1113 | 733.33 | 2200 | 1.1829 | 0.7015 | 0.7015 |
| 0.1057 | 800.0 | 2400 | 1.1634 | 0.7139 | 0.7145 |
| 0.0966 | 866.67 | 2600 | 1.1133 | 0.7076 | 0.7080 |
| 0.0919 | 933.33 | 2800 | 1.1767 | 0.7144 | 0.7145 |
| 0.0861 | 1000.0 | 3000 | 1.1778 | 0.7128 | 0.7129 |
| 0.0809 | 1066.67 | 3200 | 1.2290 | 0.7142 | 0.7145 |
| 0.0749 | 1133.33 | 3400 | 1.2717 | 0.7112 | 0.7113 |
| 0.0693 | 1200.0 | 3600 | 1.1900 | 0.7338 | 0.7341 |
| 0.0659 | 1266.67 | 3800 | 1.2033 | 0.7418 | 0.7423 |
| 0.061 | 1333.33 | 4000 | 1.2243 | 0.7323 | 0.7325 |
| 0.0579 | 1400.0 | 4200 | 1.2337 | 0.7194 | 0.7194 |
| 0.0537 | 1466.67 | 4400 | 1.2379 | 0.7292 | 0.7292 |
| 0.0506 | 1533.33 | 4600 | 1.3006 | 0.7322 | 0.7325 |
| 0.0485 | 1600.0 | 4800 | 1.3530 | 0.7259 | 0.7259 |
| 0.0454 | 1666.67 | 5000 | 1.3203 | 0.7274 | 0.7276 |
| 0.0433 | 1733.33 | 5200 | 1.2862 | 0.7307 | 0.7308 |
| 0.0415 | 1800.0 | 5400 | 1.3767 | 0.7341 | 0.7341 |
| 0.0388 | 1866.67 | 5600 | 1.3645 | 0.7292 | 0.7292 |
| 0.0382 | 1933.33 | 5800 | 1.3704 | 0.7357 | 0.7357 |
| 0.0354 | 2000.0 | 6000 | 1.4379 | 0.7357 | 0.7357 |
| 0.0352 | 2066.67 | 6200 | 1.3992 | 0.7322 | 0.7325 |
| 0.0337 | 2133.33 | 6400 | 1.3997 | 0.7341 | 0.7341 |
| 0.0322 | 2200.0 | 6600 | 1.3643 | 0.7275 | 0.7276 |
| 0.0319 | 2266.67 | 6800 | 1.4137 | 0.7341 | 0.7341 |
| 0.03 | 2333.33 | 7000 | 1.4727 | 0.7275 | 0.7276 |
| 0.0294 | 2400.0 | 7200 | 1.4124 | 0.7308 | 0.7308 |
| 0.029 | 2466.67 | 7400 | 1.3733 | 0.7259 | 0.7259 |
| 0.028 | 2533.33 | 7600 | 1.4484 | 0.7276 | 0.7276 |
| 0.0276 | 2600.0 | 7800 | 1.3802 | 0.7406 | 0.7406 |
| 0.0265 | 2666.67 | 8000 | 1.4590 | 0.7259 | 0.7259 |
| 0.0262 | 2733.33 | 8200 | 1.5033 | 0.7308 | 0.7308 |
| 0.0256 | 2800.0 | 8400 | 1.4550 | 0.7276 | 0.7276 |
| 0.0242 | 2866.67 | 8600 | 1.4723 | 0.7324 | 0.7325 |
| 0.0248 | 2933.33 | 8800 | 1.4258 | 0.7276 | 0.7276 |
| 0.025 | 3000.0 | 9000 | 1.4105 | 0.7341 | 0.7341 |
| 0.0238 | 3066.67 | 9200 | 1.4746 | 0.7308 | 0.7308 |
| 0.0239 | 3133.33 | 9400 | 1.4528 | 0.7325 | 0.7325 |
| 0.0235 | 3200.0 | 9600 | 1.4520 | 0.7357 | 0.7357 |
| 0.0231 | 3266.67 | 9800 | 1.4640 | 0.7325 | 0.7325 |
| 0.0221 | 3333.33 | 10000 | 1.4663 | 0.7308 | 0.7308 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_22M", "model-index": [{"name": "GUE_prom_prom_core_tata-seqsight_16384_512_22M-L32_all", "results": []}]} | mahdibaghbanzadeh/GUE_prom_prom_core_tata-seqsight_16384_512_22M-L32_all | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_22M",
"region:us"
] | null | 2024-04-16T06:52:11+00:00 | [] | [] | TAGS
#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_16384_512_22M #region-us
| GUE\_prom\_prom\_core\_tata-seqsight\_16384\_512\_22M-L32\_all
==============================================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_16384\_512\_22M on the mahdibaghbanzadeh/GUE\_prom\_prom\_core\_tata dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2856
* F1 Score: 0.7125
* Accuracy: 0.7129
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0005
* train\_batch\_size: 2048
* eval\_batch\_size: 2048
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 10000
### Training results
### Framework versions
* PEFT 0.9.0
* Transformers 4.38.2
* Pytorch 2.2.0+cu121
* Datasets 2.17.1
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 2048\n* eval\\_batch\\_size: 2048\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2"
] | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 2048\n* eval\\_batch\\_size: 2048\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2"
] |
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. -->
# GUE_prom_prom_300_all-seqsight_16384_512_22M-L32_all
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_22M](https://huggingface.co/mahdibaghbanzadeh/seqsight_16384_512_22M) on the [mahdibaghbanzadeh/GUE_prom_prom_300_all](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_prom_prom_300_all) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4221
- F1 Score: 0.8280
- Accuracy: 0.8280
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 1536
- eval_batch_size: 1536
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:|
| 0.5993 | 6.45 | 200 | 0.5279 | 0.7420 | 0.7424 |
| 0.511 | 12.9 | 400 | 0.5012 | 0.7558 | 0.7568 |
| 0.4874 | 19.35 | 600 | 0.4878 | 0.7699 | 0.7703 |
| 0.4675 | 25.81 | 800 | 0.4767 | 0.7743 | 0.7752 |
| 0.4472 | 32.26 | 1000 | 0.4560 | 0.7845 | 0.7850 |
| 0.4209 | 38.71 | 1200 | 0.4419 | 0.7936 | 0.7943 |
| 0.4041 | 45.16 | 1400 | 0.4286 | 0.8064 | 0.8064 |
| 0.391 | 51.61 | 1600 | 0.4183 | 0.8108 | 0.8108 |
| 0.3826 | 58.06 | 1800 | 0.4144 | 0.8116 | 0.8117 |
| 0.3731 | 64.52 | 2000 | 0.4179 | 0.8139 | 0.8140 |
| 0.3664 | 70.97 | 2200 | 0.4126 | 0.8133 | 0.8135 |
| 0.36 | 77.42 | 2400 | 0.4184 | 0.8099 | 0.8103 |
| 0.3538 | 83.87 | 2600 | 0.4093 | 0.8168 | 0.8169 |
| 0.3482 | 90.32 | 2800 | 0.4159 | 0.8165 | 0.8166 |
| 0.3418 | 96.77 | 3000 | 0.4082 | 0.8214 | 0.8215 |
| 0.3369 | 103.23 | 3200 | 0.4192 | 0.8204 | 0.8206 |
| 0.3321 | 109.68 | 3400 | 0.4123 | 0.8200 | 0.8203 |
| 0.3266 | 116.13 | 3600 | 0.4095 | 0.8210 | 0.8211 |
| 0.3241 | 122.58 | 3800 | 0.4094 | 0.8224 | 0.8225 |
| 0.3213 | 129.03 | 4000 | 0.4024 | 0.8233 | 0.8235 |
| 0.3168 | 135.48 | 4200 | 0.4072 | 0.8249 | 0.825 |
| 0.3121 | 141.94 | 4400 | 0.4084 | 0.8259 | 0.8260 |
| 0.3107 | 148.39 | 4600 | 0.4125 | 0.8266 | 0.8267 |
| 0.3074 | 154.84 | 4800 | 0.4168 | 0.8231 | 0.8233 |
| 0.3051 | 161.29 | 5000 | 0.4144 | 0.8260 | 0.8262 |
| 0.3034 | 167.74 | 5200 | 0.4244 | 0.8241 | 0.8243 |
| 0.2992 | 174.19 | 5400 | 0.4163 | 0.8295 | 0.8296 |
| 0.2985 | 180.65 | 5600 | 0.4101 | 0.8268 | 0.8269 |
| 0.2959 | 187.1 | 5800 | 0.4233 | 0.8252 | 0.8253 |
| 0.2944 | 193.55 | 6000 | 0.4147 | 0.8268 | 0.8269 |
| 0.2926 | 200.0 | 6200 | 0.4145 | 0.8309 | 0.8309 |
| 0.2907 | 206.45 | 6400 | 0.4186 | 0.8252 | 0.8253 |
| 0.2891 | 212.9 | 6600 | 0.4275 | 0.8265 | 0.8267 |
| 0.288 | 219.35 | 6800 | 0.4174 | 0.8264 | 0.8265 |
| 0.2861 | 225.81 | 7000 | 0.4149 | 0.8270 | 0.8270 |
| 0.2833 | 232.26 | 7200 | 0.4089 | 0.8287 | 0.8287 |
| 0.2842 | 238.71 | 7400 | 0.4158 | 0.8267 | 0.8267 |
| 0.2828 | 245.16 | 7600 | 0.4135 | 0.8286 | 0.8287 |
| 0.2819 | 251.61 | 7800 | 0.4157 | 0.8272 | 0.8272 |
| 0.2797 | 258.06 | 8000 | 0.4160 | 0.8296 | 0.8296 |
| 0.2785 | 264.52 | 8200 | 0.4180 | 0.8249 | 0.825 |
| 0.2785 | 270.97 | 8400 | 0.4247 | 0.8269 | 0.8270 |
| 0.278 | 277.42 | 8600 | 0.4147 | 0.8271 | 0.8272 |
| 0.2767 | 283.87 | 8800 | 0.4157 | 0.8261 | 0.8262 |
| 0.2769 | 290.32 | 9000 | 0.4172 | 0.8249 | 0.825 |
| 0.2757 | 296.77 | 9200 | 0.4173 | 0.8258 | 0.8258 |
| 0.2763 | 303.23 | 9400 | 0.4180 | 0.8259 | 0.8260 |
| 0.2755 | 309.68 | 9600 | 0.4202 | 0.8269 | 0.8270 |
| 0.2764 | 316.13 | 9800 | 0.4165 | 0.8258 | 0.8258 |
| 0.2741 | 322.58 | 10000 | 0.4192 | 0.8258 | 0.8258 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_22M", "model-index": [{"name": "GUE_prom_prom_300_all-seqsight_16384_512_22M-L32_all", "results": []}]} | mahdibaghbanzadeh/GUE_prom_prom_300_all-seqsight_16384_512_22M-L32_all | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_22M",
"region:us"
] | null | 2024-04-16T06:53:12+00:00 | [] | [] | TAGS
#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_16384_512_22M #region-us
| GUE\_prom\_prom\_300\_all-seqsight\_16384\_512\_22M-L32\_all
============================================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_16384\_512\_22M on the mahdibaghbanzadeh/GUE\_prom\_prom\_300\_all dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4221
* F1 Score: 0.8280
* Accuracy: 0.8280
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0005
* train\_batch\_size: 1536
* eval\_batch\_size: 1536
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* training\_steps: 10000
### Training results
### Framework versions
* PEFT 0.9.0
* Transformers 4.38.2
* Pytorch 2.2.0+cu121
* Datasets 2.17.1
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 1536\n* eval\\_batch\\_size: 1536\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2"
] | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 1536\n* eval\\_batch\\_size: 1536\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.38.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.1\n* Tokenizers 0.15.2"
] |
null | null | test
---
license: apache-2.0
---
Description coming soon. | {} | kolinko/mistral-buckets | null | [
"region:us"
] | null | 2024-04-16T06:53:26+00:00 | [] | [] | TAGS
#region-us
| test
---
license: apache-2.0
---
Description coming soon. | [] | [
"TAGS\n#region-us \n"
] |
text-generation | transformers |
# StarlingMaths-12B-MoE
StarlingMaths-12B-MoE is a Mixture of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [allknowingroger/StarlingMaxLimmy2-7B-slerp](https://huggingface.co/allknowingroger/StarlingMaxLimmy2-7B-slerp)
* [Kukedlc/NeuralMaths-Experiment-7b](https://huggingface.co/Kukedlc/NeuralMaths-Experiment-7b)
## 🧩 Configuration
```yaml
base_model: allknowingroger/StarlingMaxLimmy2-7B-slerp
experts:
- source_model: allknowingroger/StarlingMaxLimmy2-7B-slerp
positive_prompts: ["why"]
- source_model: Kukedlc/NeuralMaths-Experiment-7b
positive_prompts: ["math"]
```
## 💻 Usage
```python
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "allknowingroger/StarlingMaths-12B-MoE"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)
messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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": ["moe", "frankenmoe", "merge", "mergekit", "lazymergekit", "allknowingroger/StarlingMaxLimmy2-7B-slerp", "Kukedlc/NeuralMaths-Experiment-7b"], "base_model": ["allknowingroger/StarlingMaxLimmy2-7B-slerp", "Kukedlc/NeuralMaths-Experiment-7b"]} | allknowingroger/StarlingMaths-12B-MoE | null | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"moe",
"frankenmoe",
"merge",
"mergekit",
"lazymergekit",
"allknowingroger/StarlingMaxLimmy2-7B-slerp",
"Kukedlc/NeuralMaths-Experiment-7b",
"base_model:allknowingroger/StarlingMaxLimmy2-7B-slerp",
"base_model:Kukedlc/NeuralMaths-Experiment-7b",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-16T06:53:27+00:00 | [] | [] | TAGS
#transformers #safetensors #mixtral #text-generation #moe #frankenmoe #merge #mergekit #lazymergekit #allknowingroger/StarlingMaxLimmy2-7B-slerp #Kukedlc/NeuralMaths-Experiment-7b #base_model-allknowingroger/StarlingMaxLimmy2-7B-slerp #base_model-Kukedlc/NeuralMaths-Experiment-7b #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# StarlingMaths-12B-MoE
StarlingMaths-12B-MoE is a Mixture of Experts (MoE) made with the following models using LazyMergekit:
* allknowingroger/StarlingMaxLimmy2-7B-slerp
* Kukedlc/NeuralMaths-Experiment-7b
## Configuration
## Usage
| [
"# StarlingMaths-12B-MoE\n\nStarlingMaths-12B-MoE is a Mixture of Experts (MoE) made with the following models using LazyMergekit:\n* allknowingroger/StarlingMaxLimmy2-7B-slerp\n* Kukedlc/NeuralMaths-Experiment-7b",
"## Configuration",
"## Usage"
] | [
"TAGS\n#transformers #safetensors #mixtral #text-generation #moe #frankenmoe #merge #mergekit #lazymergekit #allknowingroger/StarlingMaxLimmy2-7B-slerp #Kukedlc/NeuralMaths-Experiment-7b #base_model-allknowingroger/StarlingMaxLimmy2-7B-slerp #base_model-Kukedlc/NeuralMaths-Experiment-7b #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# StarlingMaths-12B-MoE\n\nStarlingMaths-12B-MoE is a Mixture of Experts (MoE) made with the following models using LazyMergekit:\n* allknowingroger/StarlingMaxLimmy2-7B-slerp\n* Kukedlc/NeuralMaths-Experiment-7b",
"## Configuration",
"## Usage"
] |
text-generation | peft |
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
``` | {"license": "other", "library_name": "peft", "tags": ["autotrain", "text-generation-inference", "text-generation", "peft"], "widget": [{"messages": [{"role": "user", "content": "What is your favorite condiment?"}]}]} | Erdajt/CodeVerse | null | [
"peft",
"tensorboard",
"safetensors",
"autotrain",
"text-generation-inference",
"text-generation",
"conversational",
"license:other",
"region:us"
] | null | 2024-04-16T06:53:50+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #autotrain #text-generation-inference #text-generation #conversational #license-other #region-us
|
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit AutoTrain.
# Usage
| [
"# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.",
"# Usage"
] | [
"TAGS\n#peft #tensorboard #safetensors #autotrain #text-generation-inference #text-generation #conversational #license-other #region-us \n",
"# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.",
"# Usage"
] |
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": []} | luuluumando/zephyr-7b-finetune | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-16T06:53:53+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-generation | transformers | Check G-reen/EXPERIMENT-ORPO-m7b2-1-merged (https://huggingface.co/G-reen/EXPERIMENT-ORPO-m7b2-1-merged) for details. | {} | G-reen/EXPERIMENT-ORPO-m7b2-1-lora | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-04-16T06:54:14+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us
| Check G-reen/EXPERIMENT-ORPO-m7b2-1-merged (URL for details. | [] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n"
] |
text-generation | transformers |
*This model was trained as part of a series of experiments testing the performance of pure DPO vs SFT vs ORPO, all supported by Unsloth/Huggingface TRL.*
**Benchmarks**
Average 59.62
ARC 59.39
HellaSwag 82.48
MMLU 62.61
TruthfulQA 40.38
Winogrande 78.37
GSM8K 34.5
**Training Details**
Duration: ~9 hours on one Kaggle T4 with Unsloth
Model: https://huggingface.co/unsloth/mistral-7b-v0.2-bnb-4bit
Dataset: https://huggingface.co/datasets/argilla/dpo-mix-7k
Rank: 8
Alpha: 16
Learning rate: 5e-5
Beta: 0.1
Batch size: 8
Epochs: 1
Learning rate scheduler: Linear
Prompt Format: ChatML
```
<|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
Why is the sky blue?<|im_end|>
<|im_start|>assistant
```
**WanDB Reports**
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth) | {"license": "apache-2.0"} | G-reen/EXPERIMENT-ORPO-m7b2-1-merged | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-04-16T06:55:01+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #conversational #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us
|
*This model was trained as part of a series of experiments testing the performance of pure DPO vs SFT vs ORPO, all supported by Unsloth/Huggingface TRL.*
Benchmarks
Average 59.62
ARC 59.39
HellaSwag 82.48
MMLU 62.61
TruthfulQA 40.38
Winogrande 78.37
GSM8K 34.5
Training Details
Duration: ~9 hours on one Kaggle T4 with Unsloth
Model: URL
Dataset: URL
Rank: 8
Alpha: 16
Learning rate: 5e-5
Beta: 0.1
Batch size: 8
Epochs: 1
Learning rate scheduler: Linear
Prompt Format: ChatML
WanDB Reports
!image/png
!image/png
!image/png
<img src="URL width="200"/> | [] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #conversational #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #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. -->
# with_board_turn_conversation
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 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: 1.41e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.39.2
- Pytorch 2.2.2
- Datasets 2.18.0
- 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": "with_board_turn_conversation", "results": []}]} | wenshicheng97/with_board_turn_conversation | null | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | null | 2024-04-16T06:56:10+00:00 | [] | [] | TAGS
#peft #safetensors #trl #sft #generated_from_trainer #base_model-meta-llama/Llama-2-7b-chat-hf #region-us
|
# with_board_turn_conversation
This model is a fine-tuned version of meta-llama/Llama-2-7b-chat-hf on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1.41e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.39.2
- Pytorch 2.2.2
- Datasets 2.18.0
- Tokenizers 0.15.2 | [
"# with_board_turn_conversation\n\nThis model is a fine-tuned version of meta-llama/Llama-2-7b-chat-hf on an unknown dataset.",
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"## 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: 1.41e-05\n- train_batch_size: 8\n- eval_batch_size: 16\n- seed: 42\n- gradient_accumulation_steps: 16\n- total_train_batch_size: 128\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3.0",
"### Training results",
"### Framework versions\n\n- PEFT 0.10.0\n- Transformers 4.39.2\n- Pytorch 2.2.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] | [
"TAGS\n#peft #safetensors #trl #sft #generated_from_trainer #base_model-meta-llama/Llama-2-7b-chat-hf #region-us \n",
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"## 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: 1.41e-05\n- train_batch_size: 8\n- eval_batch_size: 16\n- seed: 42\n- gradient_accumulation_steps: 16\n- total_train_batch_size: 128\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3.0",
"### Training results",
"### Framework versions\n\n- PEFT 0.10.0\n- Transformers 4.39.2\n- Pytorch 2.2.2\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": []} | jspetrisko/mistral-7b-sql-v2 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T06:57:12+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:
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## 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
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#### Speeds, Sizes, Times [optional]
## Evaluation
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#### Testing Data
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### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
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"## Training Details",
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] |
automatic-speech-recognition | 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. -->
# jenjenot/whisper_medium_tw_minnan
This model is a fine-tuned version of [openai/whisper_medium](https://huggingface.co/openai/whisper_medium) on the nan_tw_soap_opera 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: 1e-05
- train_batch_size: 16
- 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: 500
- training_steps: 8000
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"language": ["zh"], "tags": ["hf-asr-leaderboard", "generated_from_trainer"], "datasets": ["thomas0104/nan_tw_soap_opera"], "base_model": "openai/whisper_medium", "model-index": [{"name": "jenjenot/whisper_medium_tw_minnan", "results": []}]} | jenjenot/whisper_medium_tw_minnan | null | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
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"dataset:thomas0104/nan_tw_soap_opera",
"base_model:openai/whisper_medium",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T07:00:04+00:00 | [] | [
"zh"
] | TAGS
#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #zh #dataset-thomas0104/nan_tw_soap_opera #base_model-openai/whisper_medium #endpoints_compatible #region-us
|
# jenjenot/whisper_medium_tw_minnan
This model is a fine-tuned version of openai/whisper_medium on the nan_tw_soap_opera 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: 1e-05
- train_batch_size: 16
- 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: 500
- training_steps: 8000
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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] |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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<!-- 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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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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[More Information Needed]
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<!-- 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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<!-- 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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[More Information Needed] | {"library_name": "transformers", "tags": []} | dhruvindia/mistral_7b_guanaco_print_caption | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T07:02:13+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]:
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- Model type:
- Language(s) (NLP):
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## 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",
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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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"#### Factors",
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"### Results",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
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"## Glossary [optional]",
"## More Information [optional]",
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"## Model Card Contact"
] |
null | transformers | ## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/lightblue/Karasu-Mixtral-8x22B-v0.1
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-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/Karasu-Mixtral-8x22B-v0.1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.Q2_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.Q2_K.gguf.part2of2) | Q2_K | 52.2 | |
| [PART 1](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.IQ3_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.IQ3_XS.gguf.part2of2) | IQ3_XS | 58.3 | |
| [PART 1](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.IQ3_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.IQ3_S.gguf.part2of2) | IQ3_S | 61.6 | beats Q3_K* |
| [PART 1](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.Q3_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.Q3_K_S.gguf.part2of2) | Q3_K_S | 61.6 | |
| [PART 1](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.IQ3_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.IQ3_M.gguf.part2of2) | IQ3_M | 64.6 | |
| [PART 1](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.Q3_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.Q3_K_M.gguf.part2of2) | Q3_K_M | 67.9 | lower quality |
| [PART 1](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.Q3_K_L.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.Q3_K_L.gguf.part2of2) | Q3_K_L | 72.7 | |
| [PART 1](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.IQ4_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.IQ4_XS.gguf.part2of2) | IQ4_XS | 76.5 | |
| [PART 1](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.Q4_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.Q4_K_S.gguf.part2of2) | Q4_K_S | 80.6 | fast, recommended |
| [PART 1](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.Q4_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.Q4_K_M.gguf.part2of2) | Q4_K_M | 85.7 | fast, recommended |
| [PART 1](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.Q5_K_S.gguf.part2of2) | Q5_K_S | 97.1 | |
| [PART 1](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.Q5_K_M.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.Q5_K_M.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.Q5_K_M.gguf.part3of3) | Q5_K_M | 100.1 | |
| [PART 1](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.Q6_K.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.Q6_K.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.Q6_K.gguf.part3of3) | Q6_K | 115.6 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.Q8_0.gguf.part1of4) [PART 2](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.Q8_0.gguf.part2of4) [PART 3](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.Q8_0.gguf.part3of4) [PART 4](https://huggingface.co/mradermacher/Karasu-Mixtral-8x22B-v0.1-GGUF/resolve/main/Karasu-Mixtral-8x22B-v0.1.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", "datasets": ["openchat/openchat_sharegpt4_dataset"], "base_model": "lightblue/Karasu-Mixtral-8x22B-v0.1", "quantized_by": "mradermacher"} | mradermacher/Karasu-Mixtral-8x22B-v0.1-GGUF | null | [
"transformers",
"en",
"dataset:openchat/openchat_sharegpt4_dataset",
"base_model:lightblue/Karasu-Mixtral-8x22B-v0.1",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T07:02:51+00:00 | [] | [
"en"
] | TAGS
#transformers #en #dataset-openchat/openchat_sharegpt4_dataset #base_model-lightblue/Karasu-Mixtral-8x22B-v0.1 #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 #dataset-openchat/openchat_sharegpt4_dataset #base_model-lightblue/Karasu-Mixtral-8x22B-v0.1 #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
MFANN chain of thought experiment developed my makhi burroughs.
3b version here: https://huggingface.co/netcat420/MFANN3bv0.4
BENCHMARKS: avg: 72.23 ARC: 68.86 HellaSwag: 86.65 MMLU: 63.63 TruthfulQA: 70.18 winogrande: 79.72 GSM8K: 64.37
| {"license": "apache-2.0", "library_name": "transformers", "datasets": ["netcat420/MFANN"], "pipeline_tag": "text-classification"} | netcat420/MFANNv0.5 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-classification",
"dataset:netcat420/MFANN",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-16T07:03:58+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #text-classification #dataset-netcat420/MFANN #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
MFANN chain of thought experiment developed my makhi burroughs.
3b version here: URL
BENCHMARKS: avg: 72.23 ARC: 68.86 HellaSwag: 86.65 MMLU: 63.63 TruthfulQA: 70.18 winogrande: 79.72 GSM8K: 64.37
!image/png
!image/png
| [] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #text-classification #dataset-netcat420/MFANN #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #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. -->
# ruBert-base-sberquad-0.01-len_3-filtered-negative-v2
This model is a fine-tuned version of [ai-forever/ruBert-base](https://huggingface.co/ai-forever/ruBert-base) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 7000
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 | {"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "ai-forever/ruBert-base", "model-index": [{"name": "ruBert-base-sberquad-0.01-len_3-filtered-negative-v2", "results": []}]} | Shalazary/ruBert-base-sberquad-0.01-len_3-filtered-negative-v2 | null | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:ai-forever/ruBert-base",
"license:apache-2.0",
"region:us"
] | null | 2024-04-16T07:04:56+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #generated_from_trainer #base_model-ai-forever/ruBert-base #license-apache-2.0 #region-us
|
# ruBert-base-sberquad-0.01-len_3-filtered-negative-v2
This model is a fine-tuned version of ai-forever/ruBert-base on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 7000
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 | [
"# ruBert-base-sberquad-0.01-len_3-filtered-negative-v2\n\nThis model is a fine-tuned version of ai-forever/ruBert-base on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0005\n- train_batch_size: 32\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- training_steps: 7000",
"### Training results",
"### Framework versions\n\n- PEFT 0.10.0\n- Transformers 4.39.3\n- Pytorch 2.2.2+cu121\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] | [
"TAGS\n#peft #tensorboard #safetensors #generated_from_trainer #base_model-ai-forever/ruBert-base #license-apache-2.0 #region-us \n",
"# ruBert-base-sberquad-0.01-len_3-filtered-negative-v2\n\nThis model is a fine-tuned version of ai-forever/ruBert-base on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0005\n- train_batch_size: 32\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- training_steps: 7000",
"### Training results",
"### Framework versions\n\n- PEFT 0.10.0\n- Transformers 4.39.3\n- Pytorch 2.2.2+cu121\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] |
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