Instructions to use thillaic/MediQuill with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use thillaic/MediQuill with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="thillaic/MediQuill")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("thillaic/MediQuill") model = AutoModelForCausalLM.from_pretrained("thillaic/MediQuill", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use thillaic/MediQuill with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "thillaic/MediQuill" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "thillaic/MediQuill", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/thillaic/MediQuill
- SGLang
How to use thillaic/MediQuill 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 "thillaic/MediQuill" \ --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": "thillaic/MediQuill", "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 "thillaic/MediQuill" \ --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": "thillaic/MediQuill", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use thillaic/MediQuill with Docker Model Runner:
docker model run hf.co/thillaic/MediQuill
metadata
license: mit
language:
- en
library_name: transformers
tags:
- medical
datasets:
- thillaic/MedQuad-MedicalQnADataset-Llama2-1k
Model Card for thillaic/MediQuill
This is a medicine-focussed Llama-2 fine tuned using thillaic/MedQuad-MedicalQnADataset-Llama2-1k dataset
Model Details
Model Description
The llama-2 model is finetuned with the given dataset to comprehend and respond to diverse medical questions, covering diagnoses, treatments, symptoms, medications, and more
- Developed by: ThillaiC
- Shared by : ThillaiC
- Model type: Llama-2 Fine-Tune
- Language(s) (NLP): English
- License: MIT2.0
- Finetuned from model : NousResearch/Llama-2-7b-chat-hf
Model Sources
- Repository: thillaic/MediQuill
- Code : github
Uses
This model aims to provide accurate, up-to-date information and assist healthcare professionals and individuals in making informed decisions about health concerns.
Hardware
T4 GPU