thomas-yanxin/Sunsimiao-V-Phi3
<!-- <h1 align="center">🌿Sunsimiao-V(孙思邈): 多模态医疗大模型</h1> --> <div align=center><img src ="./Sunsimiao-V-logo.jpg"/></div>
<h3 align="center">慧眼明医路,守护健康途</h3>
<p align="center"> <a href="https://github.com/thomas-yanxin/Sunsimiao-V"><img src="https://img.shields.io/badge/GitHub-24292e" alt="github"></a> <a href="https://huggingface.co/collections/thomas-yanxin/sunsimiao-v-6641e52e2803b4fe0d88f451"><img src="https://img.shields.io/badge/-HuggingFace-yellow" alt="HuggingFace"></a> <a href="https://www.modelscope.cn/models/thomas/Sunsimiao-V-Phi3/summary"><img src="https://img.shields.io/badge/ModelScope-blueviolet" alt="modelscope"></a> <a href="https://wisemodel.cn/models/thomas/Sunsimiao-V-Phi3/intro"><img src="https://img.shields.io/badge/WiseModel-561253" alt="WiseModel"></a> </p>
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模型列表
快速开始
- Chat by pipeline
from transformers import pipeline
from PIL import Image
import requests
model_id = "thomas-yanxin/Sunsimiao-V-Phi3"
pipe = pipeline("image-to-text", model=model_id, device=0)
image = Image.open('./images/test.png')
prompt = "<|user|>\n<image>\nWhat appears unusual in the image?<|end|>\n<|assistant|>\n"
outputs = pipe(image, prompt=prompt, generate_kwargs={"max_new_tokens": 200})
print(outputs)
>>> What appears unusual in the image? Airspace opacity- Chat by pure transformers
import requests
from PIL import Image
import torch
from transformers import AutoProcessor, LlavaForConditionalGeneration
model_id = "xtuner/llava-phi-3-mini-hf"
prompt = "<|user|>\n<image>\nWhat are these?<|end|>\n<|assistant|>\n"
image_file = "http://images.cocodataset.org/val2017/000000039769.jpg"
model = LlavaForConditionalGeneration.from_pretrained(
model_id,
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
).to(0)
processor = AutoProcessor.from_pretrained(model_id)
raw_image = Image.open(requests.get(image_file, stream=True).raw)
inputs = processor(prompt, raw_image, return_tensors='pt').to(0, torch.float16)
output = model.generate(**inputs, max_new_tokens=200, do_sample=False)
print(processor.decode(output[0][2:], skip_special_tokens=True))
结果对比
🙇 致谢
@misc{2023xtuner,
title={XTuner: A Toolkit for Efficiently Fine-tuning LLM},
author={XTuner Contributors},
howpublished = {\url{https://github.com/InternLM/xtuner}},
year={2023}
}