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README.md
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---
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<!-- Provide a quick summary of what the model is/does. -->
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## Model Details
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- **
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training
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[More Information Needed]
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#### Training Hyperparameters
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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 -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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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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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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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 -->
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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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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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#### Hardware
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[More Information Needed]
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## Citation [optional]
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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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**APA:**
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[More Information Needed]
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[More Information Needed]
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##
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base_model:
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- ik-ram28/BioMistral-CPT-7B
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- BioMistral/BioMistral-7B
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## Model Description
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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).
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## Model Details
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- **Model Type**: Causal Language Model
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- **Base Model**: BioMistral-7B
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- **Language**: French (adapted from English medical model)
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- **Domain**: Medical/Healthcare
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- **Parameters**: 7 billion
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- **License**: Apache 2.0
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- **Paper**: [Adaptation des connaissances médicales pour les grands modèles de langue : Stratégies et analyse comparative](https://github.com/ikram28/medllm-strategies)
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## Training Details
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### Continual Pre-Training (CPT)
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- **Dataset**: NACHOS corpus (opeN crAwled frenCh Healthcare cOrpuS)
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- **Size**: 7.4 GB of French medical texts
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- **Word Count**: Over 1 billion words
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- **Sources**: 24 French medical websites
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- **Training Duration**: 2.8 epochs
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- **Hardware**: 32 NVIDIA H100 80GB GPUs
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- **Training Time**: 11 hours
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- **Optimizer**: AdamW
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- **Learning Rate**: 2e-5
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- **Weight Decay**: 0.01
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- **Batch Size**: 16 with gradient accumulation of 2
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### Supervised Fine-Tuning (SFT)
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- **Dataset**: 30K French medical question-answer pairs
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- 10K native French medical questions
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- 10K translated medical questions from English resources
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- 10K generated questions from French medical texts
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- **Method**: DoRA (Weight-Decomposed Low-Rank Adaptation)
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- **Training Duration**: 10 epochs
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- **Hardware**: 1 NVIDIA H100 80GB GPU
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- **Training Time**: 42 hours
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- **Rank**: 16
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- **Alpha**: 16
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- **Learning Rate**: 2e-5
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- **Batch Size**: 4
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## Computational Impact
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- **Total Training Time**: 53 hours (11h CPT + 42h SFT)
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- **Hardware**: 32 GPU H100 + 1 GPU H100
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- **Carbon Emissions**: 10.11 kgCO2e (9.04 + 1.07)
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## Ethical Considerations
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- **Medical Accuracy**: This model is for research and educational purposes only. Performance limitations make it unsuitable for critical medical applications
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- **Bias**: May contain biases from both English and French medical literature
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## Citation
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If you use this model, please cite:
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```bibtex
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```
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## Contact
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For questions about this model, please contact: ikram.belmadani@lis-lab.fr
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