library_name, tags, license, language, base_model
| library_name | tags | license | language | base_model | |||||
|---|---|---|---|---|---|---|---|---|---|
| transformers |
|
apache-2.0 |
|
|
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
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:
Contact
For questions about this model, please contact: ikram.belmadani@lis-lab.fr