ChestVision Fine Tuned Vision Language Model

ProviderAhmedOkasha
Categorymultimodal-representation
LicenseApache-2.0
Downloads3
Stars0

Overview

ChestVision is a specialized Vision Language Model (VLM) fine-tuned specifically for medical imaging, focusing on chest radiography. Unlike general-purpose VLMs, this model is optimized to bridge the gap between visual pathology and clinical reporting, enabling developers to build automated screening tools or diagnostic support systems. It excels at interpreting X-ray imagery and generating structured textual descriptions, making it ideal for integration into radiology workflows or telehealth platforms. By leveraging an Apache-2.0 license, it provides a flexible foundation for developers to build proprietary healthcare applications without restrictive licensing overhead, offering a more targeted alternative to broad-spectrum multimodal models.

Highlights

  • Fine-tuned for high-accuracy chest radiography interpretation
  • Optimized for clinical reporting and medical image captioning
  • Permissive Apache-2.0 license for commercial deployment
  • Streamlined integration for healthcare diagnostic pipelines

Usage

Install
# Install Hugging Face transformers
pip install transformers torch
SDK Usage
# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("AhmedOkasha/ChestVision-Fine-Tuned-Vision-Language-Model")
tokenizer = AutoTokenizer.from_pretrained("AhmedOkasha/ChestVision-Fine-Tuned-Vision-Language-Model")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance 
pip install -U huggingface_hub

CLI Download

Download the full repository

Download the full repository 
huggingface-cli download AhmedOkasha/ChestVision-Fine-Tuned-Vision-Language-Model

Download a single file to a local folder (e.g. config.json into ./dir)

Download a single file to a local folder (e.g. config.json into ./dir) 
huggingface-cli download AhmedOkasha/ChestVision-Fine-Tuned-Vision-Language-Model config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

SDK Download 
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('AhmedOkasha/ChestVision-Fine-Tuned-Vision-Language-Model')

Git Download

Make sure git-lfs is installed first

Git Download 
git lfs install
git clone https://huggingface.co/AhmedOkasha/ChestVision-Fine-Tuned-Vision-Language-Model

To skip LFS large-file downloads, use:

Skip LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/AhmedOkasha/ChestVision-Fine-Tuned-Vision-Language-Model

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

Install Transformers 
pip install -U transformers torch

Load the model and run inference

Load the model and run inference 
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('AhmedOkasha/ChestVision-Fine-Tuned-Vision-Language-Model')
tokenizer = AutoTokenizer.from_pretrained('AhmedOkasha/ChestVision-Fine-Tuned-Vision-Language-Model')

Full Documentation

来源: HuggingFace

---
base_model: meta-llama/Llama-3.2-3B
library_name: peft
---

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Framework versions

  • PEFT 0.15.2
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