Instructions to use ik-ram28/MedMistral-CPT-SFT-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ik-ram28/MedMistral-CPT-SFT-7B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ik-ram28/MedMistral-CPT-SFT-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ik-ram28/MedMistral-CPT-SFT-7B")
model = AutoModelForCausalLM.from_pretrained("ik-ram28/MedMistral-CPT-SFT-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ik-ram28/MedMistral-CPT-SFT-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ik-ram28/MedMistral-CPT-SFT-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ik-ram28/MedMistral-CPT-SFT-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ik-ram28/MedMistral-CPT-SFT-7B

SGLang

How to use ik-ram28/MedMistral-CPT-SFT-7B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ik-ram28/MedMistral-CPT-SFT-7B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ik-ram28/MedMistral-CPT-SFT-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ik-ram28/MedMistral-CPT-SFT-7B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ik-ram28/MedMistral-CPT-SFT-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ik-ram28/MedMistral-CPT-SFT-7B with Docker Model Runner:
```
docker model run hf.co/ik-ram28/MedMistral-CPT-SFT-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Description

MedMistral-CPT-SFT-7B is a French medical language model based on Mistral-7B-v0.1, adapted for 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: Mistral-7B-v0.1
Language: French
Domain: Medical/Healthcare
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 (1,088,867,950 words)
- Sources: 24 French medical websites
Training Duration: 2.8 epochs
Hardware: 32 NVIDIA H100 80GB GPUs
Training Time: 12 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 A100 80GB GPU
Training Time: 75 hours
Rank: 16
Alpha: 16
Learning Rate: 2e-5
Batch Size: 4

Computational Impact

Total Training Time: 87 hours (12h CPT + 75h SFT)
Carbon Emissions: 11.78 kgCO2e (9.86 + 1.92)

Ethical Considerations

Medical Accuracy: This model is for research and educational purposes only. All outputs should be verified by qualified medical professionals
Bias: Training data may contain biases present in medical literature and online medical resources

Citation

If you use this model, please cite:

@inproceedings{belmadani-etal-2025-adaptation,
    title = "Adaptation des connaissances m{\'e}dicales pour les grands mod{\`e}les de langue : Strat{\'e}gies et analyse comparative",
    author = "Belmadani, Ikram  and
      Favre, Benoit  and
      Dufour, Richard  and
      B{\'e}chet, Fr{\'e}d{\'e}ric  and
      Ramisch, Carlos",
    editor = "Bechet, Fr{\'e}d{\'e}ric  and
      Chifu, Adrian-Gabriel  and
      Pinel-sauvagnat, Karen  and
      Favre, Benoit  and
      Maes, Eliot  and
      Nurbakova, Diana",
    booktitle = "Actes des 32{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN), volume 1 : articles scientifiques originaux",
    month = "6",
    year = "2025",
    address = "Marseille, France",
    publisher = "ATALA {\textbackslash}{\textbackslash}{\&} ARIA",
    url = "https://aclanthology.org/2025.jeptalnrecital-taln.3/",
    pages = "50--72",
    language = "fra",
    abstract = "Cet article pr{\'e}sente une {\'e}tude sur l{'}adaptation des grands mod{\`e}les de langue (LLMs) {\`a} des domaines sp{\'e}cialis{\'e}s disposant de donn{\'e}es limit{\'e}es. Bien que certaines recherches remettent en question le pr{\'e}-entra{\^i}nement adaptatif (DAPT) dans le contexte m{\'e}dical en anglais, nous montrons que l{'}adaptation au domaine peut {\^e}tre efficace sous certaines conditions. En prenant comme exemple l{'}adaptation au domaine m{\'e}dical en fran{\c{c}}ais, nous comparons de mani{\`e}re syst{\'e}matique le pr{\'e}-entra{\^i}nement continu (CPT), l{'}affinage supervis{\'e} (SFT) et une approche combin{\'e}e (CPT suivi de SFT). Nos r{\'e}sultats indiquent que l{'}adaptation d{'}un mod{\`e}le g{\'e}n{\'e}raliste {\`a} de nouvelles donn{\'e}es dans le domaine m{\'e}dical offre des am{\'e}liorations notables (taux de r{\'e}ussite de 87{\%}), tandis que l{'}adaptation suppl{\'e}mentaire de mod{\`e}les d{\'e}j{\`a} familiaris{\'e}s avec ce domaine procure des b{\'e}n{\'e}fices limit{\'e}s. Bien que CPT+SFT offre les meilleures performances globales, SFT-seul pr{\'e}sente des r{\'e}sultats solides et requiert moins de ressources mat{\'e}rielles."
}

Contact

For questions about this model, please contact: ikram.belmadani@lis-lab.fr

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Safetensors

Model size

7B params

Tensor type

F32

Model tree for ik-ram28/MedMistral-CPT-SFT-7B

Base model

mistralai/Mistral-7B-v0.1

Finetuned

ik-ram28/MedMistral-CPT-7B

Finetuned

(1)

this model

Quantizations

1 model