Instructions to use veronica-girolimetti/meditron_01 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use veronica-girolimetti/meditron_01 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="veronica-girolimetti/meditron_01")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("veronica-girolimetti/meditron_01") model = AutoModelForCausalLM.from_pretrained("veronica-girolimetti/meditron_01", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use veronica-girolimetti/meditron_01 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "veronica-girolimetti/meditron_01" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "veronica-girolimetti/meditron_01", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/veronica-girolimetti/meditron_01
- SGLang
How to use veronica-girolimetti/meditron_01 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 "veronica-girolimetti/meditron_01" \ --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": "veronica-girolimetti/meditron_01", "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 "veronica-girolimetti/meditron_01" \ --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": "veronica-girolimetti/meditron_01", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Unsloth Desktop
- Docker Model Runner
How to use veronica-girolimetti/meditron_01 with Docker Model Runner:
docker model run hf.co/veronica-girolimetti/meditron_01
| { | |
| "<CLS>": 32000, | |
| "<EOD>": 32002, | |
| "<MASK>": 32003, | |
| "<PAD>": 32004, | |
| "<SEP>": 32001, | |
| "[/bib]": 32010, | |
| "[/bib_ref]": 32006, | |
| "[/fig]": 32012, | |
| "[/fig_ref]": 32008, | |
| "[/formula]": 32016, | |
| "[/table]": 32014, | |
| "[bib]": 32009, | |
| "[bib_ref]": 32005, | |
| "[fig]": 32011, | |
| "[fig_ref]": 32007, | |
| "[formula]": 32015, | |
| "[table]": 32013 | |
| } | |