Instructions to use AGBonnet/medinote-13b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AGBonnet/medinote-13b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="AGBonnet/medinote-13b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("AGBonnet/medinote-13b") model = AutoModelForCausalLM.from_pretrained("AGBonnet/medinote-13b") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use AGBonnet/medinote-13b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AGBonnet/medinote-13b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AGBonnet/medinote-13b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/AGBonnet/medinote-13b
- SGLang
How to use AGBonnet/medinote-13b 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 "AGBonnet/medinote-13b" \ --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": "AGBonnet/medinote-13b", "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 "AGBonnet/medinote-13b" \ --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": "AGBonnet/medinote-13b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use AGBonnet/medinote-13b with Docker Model Runner:
docker model run hf.co/AGBonnet/medinote-13b
YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
Model Card for MediNote-13B-v1.0
MediNote is a suite of open-source medical Large Language Models (LLMs) fine-tuned for clinical note generation from the MediTron foundation model.
MediNote-13B is a 13 billion parameters model trained to generate clinical notes from doctor-patient conversations.
Model Details
- Developed by: Antoine Bonnet and Paul Boulenger
- Model type: Causal decoder-only transformer language model
- Language(s): English only
- Model License: LLAMA 2 COMMUNITY LICENSE AGREEMENT
- Code License: MIT
- Fine-tuned from model: Llama-2-13B with continued pre-training on PubMed Central (MediTron-13B equivalent)
- Context length: 2K tokens
- Input: Text-only data
- Output: Model generates text only
Model Sources
- Repository: EPFL-IC-Make-Team/ClinicalNotes
- Trainer: epflLLM/Megatron-LLM
- Paper: MediNote: Automatic Clinical Notes
Uses
Direct Use
It is possible to use this model to generate clinical notes, which is useful for experimentation and understanding its capabilities. It should not be used directly for production or work that may impact people.
Downstream Use
Out-of-Scope Use
We do not recommend using this model for natural language generation in a production environment, finetuned or otherwise.
Recommendations
Citation
BibTeX: If you use MediNote or its training data, please cite our work:
ADD CITATION
- Downloads last month
- 13