ik-ram28
/

BioMistral-CPT-SFT-7B

@@ -7,200 +7,78 @@ language:
 - fr
 - en
 base_model:
-- Anony-mous123/BioMistral-7B-Nachos
 ---
-# 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]

 - fr
 - en
 base_model:
+- ik-ram28/BioMistral-CPT-7B
+- BioMistral/BioMistral-7B
 ---
+## Model Description
+BioMistral-CPT-SFT-7B is a French medical language model based on BioMistral-7B, adapted for French medical domain applications through a combined approach of Continual Pre-Training (CPT) followed by Supervised Fine-Tuning (SFT).
 ## Model Details
+- **Model Type**: Causal Language Model
+- **Base Model**: BioMistral-7B
+- **Language**: French (adapted from English medical model)
+- **Domain**: Medical/Healthcare
+- **Parameters**: 7 billion
+- **License**: Apache 2.0
+- **Paper**: [Adaptation des connaissances médicales pour les grands modèles de langue : Stratégies et analyse comparative](https://github.com/ikram28/medllm-strategies)
 ## Training Details
+### Continual Pre-Training (CPT)
+- **Dataset**: NACHOS corpus (opeN crAwled frenCh Healthcare cOrpuS)
+  - **Size**: 7.4 GB of French medical texts
+  - **Word Count**: Over 1 billion words
+  - **Sources**: 24 French medical websites
+- **Training Duration**: 2.8 epochs
+- **Hardware**: 32 NVIDIA H100 80GB GPUs
+- **Training Time**: 11 hours
+- **Optimizer**: AdamW
+- **Learning Rate**: 2e-5
+- **Weight Decay**: 0.01
+- **Batch Size**: 16 with gradient accumulation of 2
+### Supervised Fine-Tuning (SFT)
+- **Dataset**: 30K French medical question-answer pairs
+  - 10K native French medical questions
+  - 10K translated medical questions from English resources
+  - 10K generated questions from French medical texts
+- **Method**: DoRA (Weight-Decomposed Low-Rank Adaptation)
+- **Training Duration**: 10 epochs
+- **Hardware**: 1 NVIDIA H100 80GB GPU
+- **Training Time**: 42 hours
+- **Rank**: 16
+- **Alpha**: 16
+- **Learning Rate**: 2e-5
+- **Batch Size**: 4
+## Computational Impact
+- **Total Training Time**: 53 hours (11h CPT + 42h SFT)
+- **Hardware**: 32 GPU H100 + 1 GPU H100
+- **Carbon Emissions**: 10.11 kgCO2e (9.04 + 1.07)
+## Ethical Considerations
+- **Medical Accuracy**: This model is for research and educational purposes only. Performance limitations make it unsuitable for critical medical applications
+- **Bias**: May contain biases from both English and French medical literature
+## Citation
+If you use this model, please cite:
+```bibtex
+```
+## Contact
+For questions about this model, please contact: [email protected]