Instructions to use EPFLiGHT/Apertus-70B-MeditronFO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EPFLiGHT/Apertus-70B-MeditronFO with Transformers:

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

pipe = pipeline("text-generation", model="EPFLiGHT/Apertus-70B-MeditronFO")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("EPFLiGHT/Apertus-70B-MeditronFO")
model = AutoModelForCausalLM.from_pretrained("EPFLiGHT/Apertus-70B-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/Apertus-70B-MeditronFO with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "EPFLiGHT/Apertus-70B-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/Apertus-70B-MeditronFO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/EPFLiGHT/Apertus-70B-MeditronFO

SGLang

How to use EPFLiGHT/Apertus-70B-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/Apertus-70B-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/Apertus-70B-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/Apertus-70B-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/Apertus-70B-MeditronFO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use EPFLiGHT/Apertus-70B-MeditronFO with Docker Model Runner:
```
docker model run hf.co/EPFLiGHT/Apertus-70B-MeditronFO
```

Xkrilandar commited on 2 days ago

Commit

f4b8e56

verified ·

1 Parent(s): 9876f07

Update model card with paper link and citation

Browse files

Files changed (1) hide show

README.md +19 -4

README.md CHANGED Viewed

@@ -12,6 +12,7 @@ tags:
 - fully-open
 - medical-llm
 base_model: swiss-ai/Apertus-70B-Instruct-2509
 datasets:
 - EPFLiGHT/fully-open-meditron
 ---
@@ -24,7 +25,7 @@ This model is part of the **Fully Open Meditron** family — the first end-to-en
 > Apertus-70B-MeditronFO establishes a new state of the art among fully open medical LLMs, and is preferred over Llama-3.1-70B-Meditron in 96.6% of pairwise Auto-MOOVE comparisons.
-- 📄 **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)
@@ -59,8 +60,13 @@ model = AutoModelForCausalLM.from_pretrained(
 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?"},
 ]
-prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-inputs = tokenizer(prompt, 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))
@@ -84,8 +90,17 @@ It is **not validated for clinical deployment, individual patient advice, autono
 ## Citation
 ```bibtex
-todo
 }
 ```

 - fully-open
 - medical-llm
 base_model: swiss-ai/Apertus-70B-Instruct-2509
+base_model_relation: finetune
 datasets:
 - EPFLiGHT/fully-open-meditron
 ---
 > Apertus-70B-MeditronFO establishes a new state of the art among fully open medical LLMs, and is preferred over Llama-3.1-70B-Meditron in 96.6% of pairwise Auto-MOOVE comparisons.
+- 📄 **Paper:** [*Fully Open Meditron: An Auditable Pipeline for Clinical LLMs*](https://arxiv.org/abs/2605.16215) (Theimer-Lienhard et al., 2026)
 - 💻 **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)
 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))
 ## Citation
+If you use this model, please cite:
 ```bibtex
+@misc{theimerlienhard2026meditronfo,
+  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}
 }
 ```