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
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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](https://github.com/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:
```bash
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:
```text
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:
```bash
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.
|