初始化项目，由ModelHub XC社区提供模型
Model: EPFLiGHT/EuroLLM-9B-MeditronFO Source: Original Platform
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README.md
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README.md
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license: apache-2.0
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language:
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- en
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- medical
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- clinical
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- healthcare
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- meditron
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- fully-open
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- medical-llm
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base_model: utter-project/EuroLLM-9B-Instruct
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base_model_relation: finetune
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datasets:
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- EPFLiGHT/fully-open-meditron
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---
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# EuroLLM-9B-MeditronFO
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**EuroLLM-9B-MeditronFO** is a 9B-parameter medical specialist LLM, produced by supervised fine-tuning of [EuroLLM-9B-Instruct](https://huggingface.co/utter-project/EuroLLM-9B-Instruct) on the [Fully Open Meditron Corpus](https://huggingface.co/datasets/EPFLiGHT/fully-open-meditron).
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This model is part of the **Fully Open Meditron** family — the first end-to-end auditable pipeline for clinical LLMs, with open weights, open data, open training recipe, and clinician-vetted corpus construction.
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> EuroLLM-9B-MeditronFO improves +11.43 points over its base on aggregate medical benchmarks.
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- 📄 **Paper:** [*Fully Open Meditron: An Auditable Pipeline for Clinical LLMs*](https://arxiv.org/abs/2605.16215)
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- 💻 **Code:** [github.com/EPFLiGHT/FullyOpenMeditron](https://github.com/EPFLiGHT/FullyOpenMeditron)
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- 📚 **Collection:** [MeditronFO](https://huggingface.co/collections/EPFLiGHT/meditronfo)
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- 🗂️ **Training corpus:** [EPFLiGHT/fully-open-meditron](https://huggingface.co/datasets/EPFLiGHT/fully-open-meditron)
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## Performance
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Accuracy (%) on standard medical benchmarks. See the paper for full evaluation details, confidence intervals, and open-ended Auto-MOOVE results.
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| Benchmark | EuroLLM-9B-Instruct | **EuroLLM-9B-MeditronFO** | Δ |
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|---|---:|---:|---:|
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| MedMCQA | 37.84 | **46.98** | +9.14 |
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| MedQA | 48.55 | **49.73** | +1.18 |
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| PubMedQA | 40.00 | **67.40** | +27.40 |
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| MedXpertQA | 10.33 | **11.63** | +1.30 |
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| HealthBench Hard | 13.47 | **31.62** | +18.15 |
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| **Average** | 30.04 | **41.47** | +11.43 |
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_id = "EPFLiGHT/EuroLLM-9B-MeditronFO"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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messages = [
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{"role": "user", "content": "A 62-year-old woman presents with a three-day history of dyspnea on exertion and a productive cough. What is the differential diagnosis?"},
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]
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inputs = tokenizer.apply_chat_template(
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messages,
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add_generation_prompt=True,
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tokenize=True,
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return_dict=True,
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return_tensors="pt",
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).to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=512, do_sample=False)
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print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))
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```
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## Training
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- **Base model:** [EuroLLM-9B-Instruct](https://huggingface.co/utter-project/EuroLLM-9B-Instruct)
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- **Corpus:** [Fully Open Meditron](https://huggingface.co/datasets/EPFLiGHT/fully-open-meditron) — ~601k examples (~150M tokens), aggregating eight public medical QA datasets with three clinician-vetted synthetic components: exam-style QA, guideline-grounded QA from 46,469 clinical practice guidelines, and open-ended clinical vignettes
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- **Hardware:** NVIDIA GH200 nodes
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- **Framework:** Axolotl with FSDP v2 / DeepSpeed ZeRO-3, Flash Attention 2, bf16 mixed precision
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- **Decontamination:** System-wide two-stage n-gram and token-alignment decontamination against all evaluation benchmarks
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Full hyperparameters are in Appendix I of the paper.
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## Intended Use
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**Research only.** This model is intended to support research on medical LLMs, auditing of clinical AI systems, and reproducibility of the Fully Open Meditron pipeline.
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It is **not validated for clinical deployment, individual patient advice, autonomous decision-making, or any other deployment-adjacent use.** Conduct independent domain-specific safety evaluation before any such use.
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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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@misc{theimerlienhard2026fullyopenmeditronauditable,
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title = {Fully Open Meditron: An Auditable Pipeline for Clinical LLMs},
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author = {Xavier Theimer-Lienhard and Mushtaha El-Amin and Fay Elhassan and Sahaj Vaidya and Victor Cartier-Negadi and David Sasu and Lars Klein and Mary-Anne Hartley},
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year = {2026},
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eprint = {2605.16215},
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archivePrefix = {arXiv},
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primaryClass = {cs.AI},
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url = {https://arxiv.org/abs/2605.16215}
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}
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```
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## License
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Released under the **apache-2.0** license. Permissive use including commercial, subject to attribution.
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