Instructions to use HuiLin0220/Medcat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HuiLin0220/Medcat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="HuiLin0220/Medcat")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("HuiLin0220/Medcat", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use HuiLin0220/Medcat with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "HuiLin0220/Medcat" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "HuiLin0220/Medcat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/HuiLin0220/Medcat
- SGLang
How to use HuiLin0220/Medcat 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 "HuiLin0220/Medcat" \ --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": "HuiLin0220/Medcat", "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 "HuiLin0220/Medcat" \ --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": "HuiLin0220/Medcat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use HuiLin0220/Medcat with Docker Model Runner:
docker model run hf.co/HuiLin0220/Medcat
Download README.md from HuiLin0220/Medcat: direct link, hf CLI and curl.
- Browser
- Download file 2.2 kB
-
https://huggingface.co/HuiLin0220/Medcat/resolve/main/README.md
- Command line
-
hf download hf://HuiLin0220/Medcat/README.md
-
curl -L -o README.md https://huggingface.co/HuiLin0220/Medcat/resolve/main/README.md
license: other
license_name: qwen
license_link: https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE
base_model: OpenGVLab/InternVL3-8B-hf
library_name: transformers
pipeline_tag: image-text-to-text
tags:
- medical
- vision-language
- internvl
- flare
Medcat V10 Weights
This repository contains the model assets for the public Medcat V10 four-task inference release. The executable inference source is maintained at HuiLin0220/Medcat.
Built with Qwen. The bundle contains the InternVL3-8B-hf base model, task/source LoRA adapters, and compact classification heads required for disease classification, multi-label classification, detection, and regression.
Download Into the Source Repository
Clone the inference source, install the Hugging Face CLI, and download only the model tree into the repository root:
git clone https://github.com/HuiLin0220/Medcat.git
cd Medcat
python -m pip install --upgrade huggingface_hub
hf download HuiLin0220/Medcat --include "models/**" --local-dir .
sha256sum --check SHA256SUMS
The resulting layout is:
Medcat/
|-- inference.py
|-- predict.sh
`-- models/
|-- InternVL3-8B-hf/
`-- FLARE-InternVL3-8B-hf/
The model directory is approximately 16 GB. Downloads use the Hugging Face Hub's resumable transfer mechanism.
Integrity
SHA256SUMS.models records every file in the model tree. From a checkout of
this weight repository, verify it with:
sha256sum --check SHA256SUMS.models
All tensor files were compared byte-for-byte with the evaluated Medcat V10 container. One unused predecessor focal checkpoint was omitted, and two machine-local provenance paths in non-tensor metadata were replaced with portable descriptions.
Licenses
- Medcat and ME-VLIP components:
LICENSE-MEDCAT(Apache-2.0) - InternVL components:
LICENSE-INTERNVL(MIT) - Qwen components:
LICENSE-QWEN(Qwen License Agreement) - Required attribution notices:
NOTICE
Users must comply with all applicable upstream terms. These weights are released for research and challenge reproduction and are not intended for clinical diagnosis or treatment.