StanfordAIMI/CheXagent-2-3b-srrg-impression

🤗 Hugging Face 来源image-to-textmit3.1B 参数13 GBsafetensors✓ 3 个校验和今天更新
一条命令提交

在你的模型文件夹旁边运行它。它会制作种子、将文件与 Hugging Face 比对,然后提交。你只需开始做种,并粘贴你账户中的密钥。它只读取你的文件,绝不修改。如果愿意,可以先阅读脚本。

curl -fsSL https://pirateface.co/package.sh | bash -s -- --repo StanfordAIMI/CheXagent-2-3b-srrg-impression ./model-folder
需要做种者 →

Requirements:

pip install opencv-python
pip install albumentations
pip install accelerate
torch==2.2.1
transformers==4.39.0 # may work with more recent version

Adapted sample script for SRRG

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

# step 1: Setup constants
model_name = "StanfordAIMI/CheXagent-2-3b-srrg-impression"
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: Download image from URL, save to a local file, and prepare path list
url = "https://huggingface.co/IAMJB/interpret-cxr-impression-baseline/resolve/main/effusions-bibasal.jpg"
resp = requests.get(url)
resp.raise_for_status()

# Use a NamedTemporaryFile so it lives on disk
with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as tmpfile:
    tmpfile.write(resp.content)
    local_path = tmpfile.name  # this is a real file path on disk

paths = [local_path]

prompt = "Structured Radiology Report Generation for Impression Section"
# build the multimodal input
query = tokenizer.from_list_format(
    [*([{"image": img} for img in paths]), {"text": prompt}]
)

# format as a chat conversation
conv = [
    {"from": "system", "value": "You are a helpful assistant."},
    {"from": "human", "value": query},
]

# tokenize and generate
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=1.0,
    top_p=1.0,
    use_cache=True,
    max_new_tokens=512,
)[0]

# decode the “impression” text
response = tokenizer.decode(output[input_ids.size(1) : -1])
print(response)

Response:

1. Interval increase in bilateral pleural effusions.
2. Interval increase in bibasilar opacities, which may represent atelectasis or consolidation.
3. Stable position of the right upper extremity peripherally inserted central catheter (PICC line).