Qwen2.5 VL 7B Instruct GPTQ Int3

🚀 Qwen2.5-VL-7B-Instruct-GPTQ-Int3

This is an UNOFFICIAL GPTQ-Int3 quantized version of the Qwen2.5-VL model using the gptqmodel library. The model is compatible with the latest transformers library, which can run non - quantized Qwen2.5 - VL models.

✨ Features

• Quantization: It is a GPTQ - Int3 quantized version of the Qwen2.5 - VL model, offering potential computational efficiency.
• Compatibility: Compatible with the latest transformers library, enabling seamless integration with existing setups.

📦 Installation

Install the required libraries:

pip install git+https://github.com/huggingface/transformers accelerate qwen-vl-utils
pip install git+https://github.com/huggingface/optimum.git
pip install gptqmodel 

Optionally, you may need to install:

pip install tokenicer device_smi logbar

💻 Usage Examples

Basic Usage

from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info

model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    "hfl/Qwen2.5-VL-3B-Instruct-GPTQ-Int4", 
    attn_implementation="flash_attention_2",
    device_map="auto"
)
processor = AutoProcessor.from_pretrained("hfl/Qwen2.5-VL-3B-Instruct-GPTQ-Int4")

messages = [{
    "role": "user",
    "content": [
        {"type": "image", "image": "https://raw.githubusercontent.com/ymcui/Chinese-LLaMA-Alpaca-3/refs/heads/main/pics/banner.png"},
        {"type": "text", "text": "请你描述一下这张图片。"},
    ],
}]

text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
    text=[text], images=image_inputs, videos=video_inputs,
    padding=True, return_tensors="pt",
).to("cuda")

generated_ids = model.generate(**inputs, max_new_tokens=512)
generated_ids_trimmed = [out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]
output_text = processor.batch_decode(generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False)
print(output_text[0])

Response

This image shows a logo in Chinese and English, reading "中文LLaMA & Alpaca大模型" and "Chinese LLaMA & Alpaca Large Language Models". There are two cartoon images on the left side of the logo, one is an alpaca with a red scarf and the other is an alpaca with white fur. The background is a green grassland and a building with a red roof. There is a number 3 on the right side of the logo, along with some circuit patterns. The overall design is simple and clear, using bright colors and cute cartoon images to attract attention.

📚 Documentation

Performance

Note

• Evaluations are performed using lmms-eval with default setting.
• GPTQ models are computationally more effective (fewer VRAM usage, faster inference speed) than AWQ series in these evaluations.
• We recommend using the gptqmodel instead of the autogptq library, as autogptq is no longer maintained.

Disclaimer

• This is NOT an official model by Qwen. Use at your own risk.
• For detailed usage, please check Qwen2.5-VL's page.

📄 License

The model is licensed under Apache - 2.0.