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Model: EPFLiGHT/EuroLLM-22B-MeditronFO Source: Original Platform
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---
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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-22B-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-22B-MeditronFO
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<div align="center">
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<img src=https://raw.githubusercontent.com/EPFLiGHT/MeditronFO/3caab22b7efaf9e56e2894bac65dab6d3a31aaa0/static/images/MeditronFO-logo-cube.png width="15%"/>
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</div>
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<p align="center">
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👋 Join our <a href="https://www.light-laboratory.org/" target="_blank">LiGHT</a> community.
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<br>
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📖 Check out the MeditronFO <a href="https://epflight.github.io/MeditronFO/" target="_blank">blog</a> and MeditronFO <a href="https://arxiv.org/abs/2605.16215" target="_blank">preprint</a>.
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<br>
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🔜 If you are a clinician join the MOOVE initiative <a href="https://jointhemoove.org/">here</a>.
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</p>
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<p align="center">
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[<a href="https://huggingface.co/collections/EPFLiGHT/meditronfo" target="_blank">Hugging Face</a>]
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[<a href="https://arxiv.org/abs/2605.16215" target="_blank">Preprint</a>]
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[<a href="https://github.com/EPFLiGHT/FullyOpenMeditron" target="_blank">GitHub</a>]
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[<a href="https://huggingface.co/datasets/EPFLiGHT/fully-open-meditron" target="_blank">Dataset</a>]
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<br>
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<b>License</b>: <a href="https://www.apache.org/licenses/LICENSE-2.0.txt" target="_blank">Apache 2.0</a> | <b>Authors</b>: <a href="https://www.light-laboratory.org/" target="_blank">LiGHT</a>
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</p>
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> [!Note]
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> We're introducing EuroLLM-22B-MeditronFO, our latest fully open medical specialist LLM, medical specialization of [EuroLLM-22B-Instruct](https://huggingface.co/utter-project/EuroLLM-22B-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.
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- **Part of the Fully Open Meditron family:** End to end fully open clinical LLMs
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- **Is prefered over EuroLLM-22B in 61.7% of comparisons:** Comparison on the clinician validated LM judge evaluation AutoMOOVE.
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<div align="center">
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<img src=https://raw.githubusercontent.com/EPFLiGHT/MeditronFO/3caab22b7efaf9e56e2894bac65dab6d3a31aaa0/static/images/plot_fig1_healthbench_timeline_easy_color.png width="100%"/>
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</div>
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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-22B-Instruct | **EuroLLM-22B-MeditronFO** | Δ |
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|---|---:|---:|---:|
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| MedMCQA | 54.94 | **54.79** | -0.15 |
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| MedQA | 66.61 | **63.16** | -3.45 |
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| PubMedQA | 73.60 | **78.00** | +4.40 |
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| MedXpertQA | 14.61 | **14.61** | +0.00 |
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| HealthBench Hard | 34.79 | **37.38** | +2.59 |
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| **Average** | 48.91 | **49.59** | +0.68 |
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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-22B-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-22B-Instruct](https://huggingface.co/utter-project/EuroLLM-22B-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:** 8 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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## Compute & footprint
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The training was done on 8 nodes of 4 NVIDIA GH200 GPUs for approximately 6 hours on the CSCS Swiss National Supercomputing Centre.
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Our trainings have a carbon neutral footprint as the CSCS data center is carbon neutral ([CSCS energy efficiency](https://ethz.ch/en/news-and-events/eth-news/news/2022/11/at-cscs-energy-efficiency-is-a-key-priority-even-at-high-performance.html)).
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## Limitations & intended use
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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.
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## Citation
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If you find MeditronFO useful in your research, please cite our preprint:
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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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## Contact
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Please use the community tab for any discussions or issue related to this model.
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Questions related to the project can be sent to xavier.theimer-lienhard@epfl.ch or mary-anne.hartley@epfl.ch.
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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'] + '
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' + message['content'] + '<|im_end|>' + '
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'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
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' }}{% endif %}
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"dtype": "bfloat16",
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"eos_token_id": 4,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 6144,
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"initializer_range": 0.02,
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"intermediate_size": 16384,
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"max_position_embeddings": 32768,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 48,
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"num_hidden_layers": 54,
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"num_key_value_heads": 8,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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"rope_theta": 1000000,
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"tie_word_embeddings": false,
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"transformers_version": "4.57.1",
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"use_cache": false,
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"vocab_size": 128000
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}
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30
special_tokens_map.json
Normal file
30
special_tokens_map.json
Normal file
@@ -0,0 +1,30 @@
|
||||
{
|
||||
"bos_token": {
|
||||
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3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
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||||
version https://git-lfs.github.com/spec/v1
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oid sha256:d869676e23a78b395eb0308cff52fd9c3ea7d52597627360cdb97407bb0b02b8
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size 15783075
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3
tokenizer.model
Normal file
3
tokenizer.model
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
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oid sha256:42957aec88804838805b335da1bbcce9d116024a20c6c4b56757648b9e348254
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size 2408875
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2107
tokenizer_config.json
Normal file
2107
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user