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", 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
```

Apertus-70B-MeditronFO / model-00003-of-00030.safetensors

member-epflight

Retrained on the de-truncated corpus: fixes mid-answer truncation

6810a6d 12 days ago

Download

History Blame Contribute Delete

5 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 8a99f6e471ba52d6da8b8d1183b5027f76c2d90e2c2be41dae470c6f72a2508e
Size of remote file:: 5 GB
SHA256:: 9a4c3961fabcc1f77fb6ee82131fa119b1424d7d10c818581761ea438f1631c8

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.