QVQ 72B Preview Abliterated GPTQ Int8

🚀 慧慧AI QVQ-72B預覽版量化模型

慧慧AI QVQ-72B預覽版量化模型是基於GPTQ算法的8位量化版本，可有效提升模型的推理效率，適用於圖像文本生成等多種場景。

🚀 快速開始

本模型是 huihui-ai/QVQ-72B-Preview-abliterated 的GPTQ 8位量化版本。這只是GPTQ的量化測試，僅使用了一個數據集："gptqmodel is an easy-to-use model quantization library with user-friendly apis, based on GPTQ algorithm."。如果您需要使用自己的數據集，請聯繫我們：support@huihui.ai。

📦 安裝指南

我們提供了一個工具包，可幫助您更方便地處理各種類型的視覺輸入，包括base64、URL以及交錯的圖像和視頻。您可以使用以下命令進行安裝：

pip install qwen-vl-utils

💻 使用示例

基礎用法

以下是一段代碼示例，展示瞭如何使用 transformers 和 qwen_vl_utils 來使用該聊天模型：

from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info

# default: Load the model on the available device(s)
model = Qwen2VLForConditionalGeneration.from_pretrained(
    "huihui-ai/QVQ-72B-Preview-abliterated-GPTQ-Int8", torch_dtype="auto", device_map="auto"
)

# default processer
processor = AutoProcessor.from_pretrained("huihui-ai/QVQ-72B-Preview-abliterated-GPTQ-Int8")

# The default range for the number of visual tokens per image in the model is 4-16384. You can set min_pixels and max_pixels according to your needs, such as a token count range of 256-1280, to balance speed and memory usage.
# min_pixels = 256*28*28
# max_pixels = 1280*28*28
# processor = AutoProcessor.from_pretrained("huihui-ai/QVQ-72B-Preview-abliterated-GPTQ-Int8", min_pixels=min_pixels, max_pixels=max_pixels)

messages = [
    {
        "role": "system",
        "content": [
            {"type": "text", "text": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."}
        ],
    },
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/QVQ/demo.png",
            },
            {"type": "text", "text": "What value should be filled in the blank space?"},
        ],
    }
]

# Preparation for inference
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",
)
inputs = inputs.to("cuda")

# Inference: Generation of the output
generated_ids = model.generate(**inputs, max_new_tokens=8192)
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)

📄 許可證

• 許可證類型：其他
• 許可證名稱：qwen
• 許可證鏈接：https://huggingface.co/huihui-ai/QVQ-72B-Preview-abliterated-GPTQ-Int8/blob/main/LICENSE