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EPFLiGHT
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Model Details
Model Provider
EPFLiGHT
Model Tree
swiss-ai/Apertus-8B-Instruct-2509
Input Modalities
Output Modalities
Supported Functionality
Accuracy (%) on four multiple-choice medical benchmarks (greedy decoding) and score (%) on HealthBench (all 5,000 conversations, Gemma-4-31B grader). See the paper for the full evaluation details, confidence intervals and the AutoMOOVE results.
| Benchmark | Apertus-8B-Instruct | Apertus-8B-MeditronFO | Δ |
|---|---|---|---|
| MedMCQA | 47.8 | 52.0 | +4.2 |
| MedQA | 52.1 | 60.7 | +8.6 |
| PubMedQA | 74.3 | 71.4 | −2.9 |
| MedXpertQA | 11.8 | 15.1 | +3.3 |
| HealthBench | 26.0 | 42.1 | +16.1 |
| Average (5) | 42.39 | 48.26 | +5.87 |
python
from transformers import AutoModelForCausalLM, AutoTokenizerimport torch model_id = "EPFLiGHT/Apertus-8B-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=8192, do_sample=True, temperature=0.7)print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))Full hyperparameters are in the training appendix of the paper.
The training was done on 2 nodes of 4 NVIDIA GH200 GPUs for 51 min (7 GPU-hours) on the Alps supercomputer of 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}}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.
from transformers import AutoModelForCausalLM, AutoTokenizerimport torch model_id = "EPFLiGHT/Apertus-8B-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=8192, do_sample=True, temperature=0.7)print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))@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}}