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
2.2 kB
| 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. | |