Instructions to use epfl-llm/meditron-70b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use epfl-llm/meditron-70b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="epfl-llm/meditron-70b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("epfl-llm/meditron-70b") model = AutoModelForCausalLM.from_pretrained("epfl-llm/meditron-70b") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use epfl-llm/meditron-70b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "epfl-llm/meditron-70b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "epfl-llm/meditron-70b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/epfl-llm/meditron-70b
- SGLang
How to use epfl-llm/meditron-70b 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 "epfl-llm/meditron-70b" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "epfl-llm/meditron-70b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "epfl-llm/meditron-70b" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "epfl-llm/meditron-70b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use epfl-llm/meditron-70b with Docker Model Runner:
docker model run hf.co/epfl-llm/meditron-70b
How to run/access this model using API calls on either "inference endpoints", "replicate" or own 64Gb Linux desktop?
Hi:
I would like to run this model. I have been testing llama-2 on replicate.com as a paid service (not expensive), but it seems this model may be easiest to run on "inference endpoint"? I also have a 64 Gb linux desktop (with a RTX 3000 Mobile/Max-Q GPU) - would be happy to run on that if cloud options too difficult.
Anyone has instructions on how to run this model in any of these manners? I have read https://github.com/epfLLM/meditron/blob/main/deployment/README.md and it gives directions about how to use this model with API calls from a client. However, I don't know if I need to run this in the cloud or if a 64 Gb Ubuntu desktop is sufficient. (I don't have a mac.)
I need to access this model by making API calls, that is my usecase.
https://ollama.ai/ makes it easy to run models. Maybe instructions how to run it with https://ollama.ai/ would be helpful ?