LLama-3.2-11B/90B-Vision-Instruct

使用Gradio+流式输出+LLama3.2-Vision构建模型推理web demo

模型下载

1. Huggingface：
   https://huggingface.co/meta-llama/Llama-3.2-11B-Vision-Instruct
   https://huggingface.co/meta-llama/Llama-3.2-90B-Vision-Instruct
2. ModelScope:
   https://modelscope.cn/models/LLM-Research/Llama-3.2-11B-Vision-Instruct
   https://modelscope.cn/models/LLM-Research/Llama-3.2-90B-Vision-Instruct

环境

torch

pip install torch==2.2.2 torchvision==0.17.2 torchaudio==2.2.2 --index-url https://download.pytorch.org/whl/cu121

gradio

pip install gradio==5.0.1

transformers

pip install transformers==4.45.0

accelerate

pip install accelerate==0.34.2

代码

import torch
import gradio as gr
from transformers import MllamaForConditionalGeneration, AutoProcessor, TextIteratorStreamer
from threading import Thread


model_id = "/your/path/Llama-3_2-90B-Vision-Instruct/"

model = MllamaForConditionalGeneration.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    # device_map="auto",
    device_map="balanced",
)

processor = AutoProcessor.from_pretrained(model_id)

def generate(image, user_text):
    messages = [
        {
    
    "role": "user", "content": [
            {
    
    "type": "image"},
            {
    
    "type": "text", "text": user_text}
        ]}
    ]
    print("user_input: ", user_text)
    input_text = processor.apply_chat_template(messages, add_generation_prompt=True)
    inputs = processor(image, input_text, return_tensors="pt").to(model.device)

    # 初始化流式输出器
    streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
    
    # 启动生成线程
    generation_kwargs = dict(**inputs, streamer=streamer, max_new_tokens=512)
    
    thread = Thread(target=model.generate, kwargs=generation_kwargs)
    thread.start()
    generated_text = ''
    
    # 流式输出
    for new_text in streamer:
        generated_text += new_text
        yield generated_text
    print(f"output: {
      
      generated_text} \n")

# 创建 Gradio 接口
demo = gr.Interface(
    fn=generate,
    inputs=[
        gr.Image(type="pil", label="Upload Image"),
        gr.Textbox(label="Input Text", placeholder="Write your prompt here...")
    ],
    outputs=gr.Textbox(label="Generated text"),
    title="LLama3.2 Vision",
    description="Upload an image and input text."
)

# 启动 Gradio 接口，指定 IP 和端口
demo.launch(server_name="0.0.0.0", server_port=1234)

效果