库名称: transformers 流水线标签: 文本生成 推理: 真 小组件:

• 文本: 你好! 示例标题: 你好世界 组别: Python 基础模型:
• Qwen/Qwen2-VL-7B-Instruct

本模型用于调试目的。它基于Qwen/Qwen2-VL-7B-Instruct的配置随机初始化，但尺寸更小。

使用方法:

from PIL import Image
import requests
import torch
from torchvision import io
from typing import Dict
from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor

model_id = "yujiepan/qwen2-vl-tiny-random"

# 在可用设备上以半精度加载模型
model = Qwen2VLForConditionalGeneration.from_pretrained(
    model_id, torch_dtype="auto", device_map="auto"
)
processor = AutoProcessor.from_pretrained(model_id)

# 图像处理
url = "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"
image = Image.open(requests.get(url, stream=True).raw)
conversation = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
            },
            {"type": "text", "text": "描述这张图片。"},
        ],
    }
]
text_prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
# 预期输出: '<|im_start|>system\n你是一个乐于助人的助手。<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>描述这张图片。<|im_end|>\n<|im_start|>assistant\n'

inputs = processor(
    text=[text_prompt], images=[image], padding=True, return_tensors="pt"
)
inputs = inputs.to("cuda")

output_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids = [
    output_ids[len(input_ids) :]
    for input_ids, output_ids in zip(inputs.input_ids, output_ids)
]
output_text = processor.batch_decode(
    generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True
)
print(output_text)

代码实现:

import os
from typing import Dict

import requests
import torch
import transformers
from PIL import Image
from torchvision import io
from transformers import (AutoConfig, AutoModelForCausalLM, AutoProcessor,
                          AutoTokenizer, GenerationConfig, pipeline, set_seed)
from transformers.models.qwen2_vl import Qwen2VLForConditionalGeneration

model_id = "Qwen/Qwen2-VL-7B-Instruct"
repo_id = "yujiepan/qwen2-vl-tiny-random"
保存路径 = f"/tmp/{repo_id}"

config = AutoConfig.from_pretrained(model_id, trust_remote_code=True)
config.hidden_size = 16
config.intermediate_size = 32
config.num_attention_heads = 2
config.num_hidden_layers = 2
config.num_key_value_heads = 1
config.vision_config.embed_dim = 16
config.vision_config.num_heads = 2
config.vision_config.hidden_size = 16
config.vision_config.depth = 2
config.rope_scaling['mrope_section'] = [1, 1, 2]  # 此处总和需要为4

model = Qwen2VLForConditionalGeneration(config=config)
model = model.to(torch.bfloat16).cuda().eval()
model.generation_config = GenerationConfig.from_pretrained(
    model_id, trust_remote_code=True,
)
set_seed(42)
with torch.no_grad():
    for _, p in sorted(model.named_parameters()):
        torch.nn.init.uniform_(p, -0.3, 0.3)

processor = AutoProcessor.from_pretrained(model_id)
model.save_pretrained(保存路径)
processor.save_pretrained(保存路径)
os.system(f"ls -alh {保存路径}")


def 测试推理():
    url = "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"
    image = Image.open(requests.get(url, stream=True).raw)
    conversation = [
        {
            "role": "user",
            "content": [
                {
                    "type": "image",
                },
                {"type": "text", "text": "描述这张图片。"},
            ],
        }
    ]
    processor = AutoProcessor.from_pretrained(保存路径)
    model = Qwen2VLForConditionalGeneration.from_pretrained(
        保存路径, torch_dtype=torch.bfloat16, device_map='cuda')
    text_prompt = processor.apply_chat_template(
        conversation, add_generation_prompt=True)
    inputs = processor(
        text=[text_prompt], images=[image], padding=True, return_tensors="pt"
    )
    inputs = inputs.to("cuda")
    output_ids = model.generate(**inputs, max_new_tokens=16)
    generated_ids = [
        output_ids[len(input_ids):]
        for input_ids, output_ids in zip(inputs.input_ids, output_ids)
    ]
    output_text = processor.batch_decode(
        generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True
    )
    print(output_text)


测试推理()