Qwen2 Vl Tiny Random

🚀 变换器模型

本模型用于调试。它使用来自 Qwen/Qwen2-VL-7B-Instruct 的配置进行随机初始化，但规模更小。

🚀 快速开始

模型信息

示例输入

💻 使用示例

基础用法

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"

# Load the model in half-precision on the available device(s)
model = Qwen2VLForConditionalGeneration.from_pretrained(
    model_id, torch_dtype="auto", device_map="auto"
)
processor = AutoProcessor.from_pretrained(model_id)

# Image
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": "Describe this image."},
        ],
    }
]
text_prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
# Excepted output: '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>Describe this image.<|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)

高级用法

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"
save_path = 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]  # sum needs to be 4 here

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(save_path)
processor.save_pretrained(save_path)
os.system(f"ls -alh {save_path}")


def try_inference():
    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": "Describe this image."},
            ],
        }
    ]
    processor = AutoProcessor.from_pretrained(save_path)
    model = Qwen2VLForConditionalGeneration.from_pretrained(
        save_path, 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)


try_inference()