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", device_map="auto")
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", device_map="auto")
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 Settings

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
```

VS MedGemma

by Markobes - opened 29 days ago

Discussion

Markobes

29 days ago

It seems that this model falls short of the one that has almost half as many parameters. What's the feature?

Xkrilandar

29 days ago

the feature is the auditability.
MedGemma and Gemma training data is not open.
https://huggingface.co/papers/2605.16215

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