Chexagent 2 3b

🚀 CheXagent

CheXagent is a model for chest X - ray interpretation. It provides a solution for analyzing chest X - rays and is available on platforms like Hugging Face and GitHub.

🚀 Quick Start

✨ Features

• Model Release: On 04/29/2024, the model was released on Hugging Face.

📦 Installation

The installation process is mainly about setting up the environment and loading the model, which is included in the usage example.

💻 Usage Examples

Basic Usage

import io

import requests
import torch
from PIL import Image
from transformers import AutoModelForCausalLM, AutoTokenizer

# step 1: Setup constant
model_name = "StanfordAIMI/CheXagent-2-3b"
dtype = torch.bfloat16
device = "cuda"

# step 2: Load Processor and Model
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", trust_remote_code=True)
model = model.to(dtype)
model.eval()

# step 3: Inference
query = tokenizer.from_list_format([*[{'image': path} for path in paths], {'text': prompt}])
conv = [{"from": "system", "value": "You are a helpful assistant."}, {"from": "human", "value": query}]
input_ids = tokenizer.apply_chat_template(conv, add_generation_prompt=True, return_tensors="pt")
output = model.generate(
    input_ids.to(device), do_sample=False, num_beams=1, temperature=1., top_p=1., use_cache=True,
    max_new_tokens=512
)[0]
response = tokenizer.decode(output[input_ids.size(1):-1])

📄 License

The project uses the cc - by - nc - 4.0 license.

✏️ Citation

@article{chexagent-2024,
  title={CheXagent: Towards a Foundation Model for Chest X-Ray Interpretation},
  author={Chen, Zhihong and Varma, Maya and Delbrouck, Jean-Benoit and Paschali, Magdalini and Blankemeier, Louis and Veen, Dave Van and Valanarasu, Jeya Maria Jose and Youssef, Alaa and Cohen, Joseph Paul and Reis, Eduardo Pontes and Tsai, Emily B. and Johnston, Andrew and Olsen, Cameron and Abraham, Tanishq Mathew and Gatidis, Sergios and Chaudhari, Akshay S and Langlotz, Curtis},
  journal={arXiv preprint arXiv:2401.12208},
  url={https://arxiv.org/abs/2401.12208},
  year={2024}
}

📚 Documentation

• 📝 Paper
• 🤗 Hugging Face
• 🧩 Github
• 🪄 Project