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
- 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
The config.json does not seem to match the new instruction finetuning.
The config.json seems to still be the default with vocab_size of 32000, and the old bos_token_id and eos_token_id. This doesn't match the new tokens added in added_tokens.json and specified in the github.
Ok, I am noticing now that the embedding weights are only of size 32000. I assume this means the model was not finetuned with the new vocabulary? Is the model that is uploaded the instruction finetuned model mentioned on the github or something else? https://github.com/epfLLM/meditron#downstream-use
Same question encountered when using 7B model
Hi there, thank you for bringing this to our attention.
Here is a related issue with our reply:
https://huggingface.co/epfl-llm/meditron-7b/discussions/5
Let us know if this resolves the issue. Looking forward to your feedback!