Instructions to use EPFLiGHT/OLMo-2-32B-MeditronFO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EPFLiGHT/OLMo-2-32B-MeditronFO with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="EPFLiGHT/OLMo-2-32B-MeditronFO")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("EPFLiGHT/OLMo-2-32B-MeditronFO")
model = AutoModelForCausalLM.from_pretrained("EPFLiGHT/OLMo-2-32B-MeditronFO")
messages = [
    {"role": "user", "content": "Who are you?"},
]
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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use EPFLiGHT/OLMo-2-32B-MeditronFO with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "EPFLiGHT/OLMo-2-32B-MeditronFO"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EPFLiGHT/OLMo-2-32B-MeditronFO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/EPFLiGHT/OLMo-2-32B-MeditronFO

SGLang

How to use EPFLiGHT/OLMo-2-32B-MeditronFO with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "EPFLiGHT/OLMo-2-32B-MeditronFO" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EPFLiGHT/OLMo-2-32B-MeditronFO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "EPFLiGHT/OLMo-2-32B-MeditronFO" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EPFLiGHT/OLMo-2-32B-MeditronFO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use EPFLiGHT/OLMo-2-32B-MeditronFO with Docker Model Runner:
```
docker model run hf.co/EPFLiGHT/OLMo-2-32B-MeditronFO
```

Xkrilandar commited on 12 days ago

Commit

c812b0b

verified ·

1 Parent(s): d265332

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README.md +2 -14

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@@ -26,7 +26,7 @@ This model is part of the **Fully Open Meditron** family — the first end-to-en
 - 📄 **Paper:** *Fully Open Meditron: An Auditable Pipeline for Clinical LLMs*
 - 💻 **Code:** [github.com/EPFLiGHT/FullyOpenMeditron](https://github.com/EPFLiGHT/FullyOpenMeditron)
-- 📚 **Collection:** [Fully Open Meditron on the Hub](https://huggingface.co/EPFLiGHT)
 - 🗂️ **Training corpus:** [EPFLiGHT/fully-open-meditron](https://huggingface.co/datasets/EPFLiGHT/fully-open-meditron)
 ## Performance
@@ -82,22 +82,10 @@ Full hyperparameters are in Appendix I of the paper.
 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.
-## Limitations
-- Inherits limitations of the training corpus: ~64% of items are synthetic, generated by a single teacher model (gpt-oss-120b), introducing model-specific stylistic and reasoning biases.
-- Decontamination is syntactic (n-gram and token alignment) rather than semantic, leaving open the possibility of paraphrased leakage.
-- Some general-purpose instruction-following capability is degraded relative to the base model — a tradeoff common to medical-specialist fine-tuning.
-- Predominantly English; non-English clinical content is underrepresented.
-- Inherits geographic and demographic biases of the source datasets, partially mitigated by AfriMed-QA inclusion.
-- May produce confidently incorrect outputs (hallucinations).
 ## Citation
 ```bibtex
-@inproceedings{theimerlienhard2026meditron,
-  title={Fully Open Meditron: An Auditable Pipeline for Clinical LLMs},
-  author={Theimer-Lienhard, Xavier and El-Amin, Mushtaha and Elhassan, Fay and Vaidya, Sahaj and Cartier-Negadi, Victor and Sasu, David and Klein, Lars and Hartley, Mary-Anne},
-  year={2026}
 }
 ```

 - 📄 **Paper:** *Fully Open Meditron: An Auditable Pipeline for Clinical LLMs*
 - 💻 **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
 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
 ```bibtex
+todo
 }
 ```