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Model: EPFLiGHT/EuroLLM-9B-MeditronFO
Source: Original Platform

.gitattributes

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README.md

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+---
+license: apache-2.0
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- medical
+- clinical
+- healthcare
+- meditron
+- fully-open
+- medical-llm
+base_model: utter-project/EuroLLM-9B-Instruct
+base_model_relation: finetune
+datasets:
+- EPFLiGHT/fully-open-meditron
+---
+
+# EuroLLM-9B-MeditronFO
+
+**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).
+
+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.
+
+> EuroLLM-9B-MeditronFO improves +11.43 points over its base on aggregate medical benchmarks.
+
+- 📄 **Paper:** [*Fully Open Meditron: An Auditable Pipeline for Clinical LLMs*](https://arxiv.org/abs/2605.16215)
+- 💻 **Code:** [github.com/EPFLiGHT/FullyOpenMeditron](https://github.com/EPFLiGHT/FullyOpenMeditron)
+- 📚 **Collection:** [MeditronFO](https://huggingface.co/collections/EPFLiGHT/meditronfo)
+- 🗂️ **Training corpus:** [EPFLiGHT/fully-open-meditron](https://huggingface.co/datasets/EPFLiGHT/fully-open-meditron)
+
+## Performance
+
+Accuracy (%) on standard medical benchmarks. See the paper for full evaluation details, confidence intervals, and open-ended Auto-MOOVE results.
+
+| Benchmark | EuroLLM-9B-Instruct | **EuroLLM-9B-MeditronFO** | Δ |
+|---|---:|---:|---:|
+| MedMCQA | 37.84 | **46.98** | +9.14 |
+| MedQA | 48.55 | **49.73** | +1.18 |
+| PubMedQA | 40.00 | **67.40** | +27.40 |
+| MedXpertQA | 10.33 | **11.63** | +1.30 |
+| HealthBench Hard | 13.47 | **31.62** | +18.15 |
+| **Average** | 30.04 | **41.47** | +11.43 |
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_id = "EPFLiGHT/EuroLLM-9B-MeditronFO"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+
+messages = [
+    {"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?"},
+]
+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=512, do_sample=False)
+print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))
+```
+
+## Training
+
+- **Base model:** [EuroLLM-9B-Instruct](https://huggingface.co/utter-project/EuroLLM-9B-Instruct)
+- **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
+- **Hardware:** NVIDIA GH200 nodes
+- **Framework:** Axolotl with FSDP v2 / DeepSpeed ZeRO-3, Flash Attention 2, bf16 mixed precision
+- **Decontamination:** System-wide two-stage n-gram and token-alignment decontamination against all evaluation benchmarks
+
+Full hyperparameters are in Appendix I of the paper.
+
+## Intended Use
+
+**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.
+
+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.
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@misc{theimerlienhard2026fullyopenmeditronauditable,
+  title         = {Fully Open Meditron: An Auditable Pipeline for Clinical LLMs},
+  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},
+  year          = {2026},
+  eprint        = {2605.16215},
+  archivePrefix = {arXiv},
+  primaryClass  = {cs.AI},
+  url           = {https://arxiv.org/abs/2605.16215}
+}
+```
+
+## License
+
+Released under the **apache-2.0** license. Permissive use including commercial, subject to attribution.

chat_template.jinja

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+{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '
+' + message['content'] + '<|im_end|>' + '
+'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
+' }}{% endif %}

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+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "dtype": "bfloat16",
+  "eos_token_id": 4,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 12288,
+  "max_position_embeddings": 4096,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 42,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "tie_word_embeddings": false,
+  "transformers_version": "4.57.1",
+  "use_cache": false,
+  "vocab_size": 128000
+}

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