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epfl-llm
/
meditron-70b

Text Generation
Transformers
PyTorch
Safetensors
English
llama
medical
health
llama2
text-generation-inference
Model card Files Files and versions
xet
Community
15

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
New discussion
Resources
  • PR & discussions documentation
  • Code of Conduct
  • Hub documentation

Remove access

#15 opened 6 months ago by
jimmcgaw

The Development Concept of This Model is So Cool

πŸ‘ 1
#14 opened over 1 year ago by
qingyuanc

Inquiry on MEDITRON LLaMA 3 Version Release

πŸš€ 1
#13 opened almost 2 years ago by
Nagafi

This is so cool + Llama3

πŸ‘β€οΈ 4
4
#11 opened about 2 years ago by
clem

Getting an issue with Cuda

πŸ‘€πŸ”₯ 1
3
#10 opened about 2 years ago by
LLMHackathonNYC

Update model.safetensors.index.json

#9 opened over 2 years ago by
Alburrito18

Using Axolotl for Fine Tuning

#7 opened over 2 years ago by
zero1zero

when attempting to download/save model to run on replicate.com I get KeyError: 'llama'

#6 opened over 2 years ago by
ghthaker1955

How to run/access this model using API calls on either "inference endpoints", "replicate" or own 64Gb Linux desktop?

1
#5 opened over 2 years ago by
ghthaker1955

The config.json does not seem to match the new instruction finetuning.

πŸ‘ 1
3
#4 opened over 2 years ago by
paulhager
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