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Model Details
Model Provider
EPFLiGHT
Model Tree
swiss-ai/Apertus-70B-Instruct-2509
Input Modalities
Output Modalities
Supported Functionality
Accuracy (%) on standard medical benchmarks. See the paper for full evaluation details, confidence intervals, and open-ended Auto-MOOVE results.
| Benchmark | Apertus-70B-Instruct | Apertus-70B-MeditronFO | Δ |
|---|---|---|---|
| MedMCQA | 52.43 | 56.32 | +3.89 |
| MedQA | 60.64 | 68.58 | +7.94 |
| PubMedQA | 66.80 | 75.20 | +8.40 |
| MedXpertQA | 12.33 | 16.90 | +4.57 |
| HealthBench Hard | 32.28 | 40.14 | +7.86 |
| Average | 44.90 | 51.43 | +6.53 |
python
from transformers import AutoModelForCausalLM, AutoTokenizerimport torchmodel_id = "EPFLiGHT/Apertus-70B-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))
Full hyperparameters are in Appendix I of the paper.
The training was done on 8 nodes of 4 NVIDIA GH200 GPUs for approximately 6 hours on the CSCS Swiss National Supercomputing Centre. Our trainings have a carbon neutral footprint as the CSCS data center is carbon neutral (CSCS energy efficiency).
MeditronFO can produce text on a variety of topics, but the generated content may not always be factually accurate, logically consistent, or free from biases present in the training data. MeditronFO has been trained to be specialised for Medicine and is intended to be used for Medicine related tasks evaluation. These models should be used as assistive tools rather than definitive sources of information. Users should always verify important information and critically evaluate any generated content.
If you find MeditronFO useful in your research, please cite our preprint:
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}}
This work was supported under project ID #27 as part of the Swiss AI Initiative, through a grant from the ETH Domain and computational resources provided by the Swiss National Supercomputing Centre (CSCS) under the Alps infrastructure. We thank the physician review panel within the LiGHT laboratory for their clinical auditing, methodological review, and validation of the synthetic generation and evaluation pipelines. We additionally thank the many physicians and clinical experts who contributed to the MOOVE initiative through expert review, pairwise evaluation, benchmarking, and clinical vignette development across diverse international settings.
Please use the community tab for any discussions or issue related to this model. Questions related to the project can be sent to xavier.theimer-lienhard@epfl.ch or mary-anne.hartley@epfl.ch.