Cogvlm2 Llama3 Chinese Chat 19B

🚀 CogVLM2

CogVLM2是新一代的模型系列，基于Meta-Llama-3-8B-Instruct构建。相比上一代CogVLM开源模型，它在多个基准测试中表现更优，支持更长内容长度和更高图像分辨率，还提供了支持中英双语的开源版本。

🚀 快速开始

以下是一个使用CogVLM2模型进行对话的简单示例。更多用例可在我们的GitHub上找到。

import torch
from PIL import Image
from transformers import AutoModelForCausalLM, AutoTokenizer

MODEL_PATH = "THUDM/cogvlm2-llama3-chinese-chat-19B"
DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
TORCH_TYPE = torch.bfloat16 if torch.cuda.is_available() and torch.cuda.get_device_capability()[0] >= 8 else torch.float16

tokenizer = AutoTokenizer.from_pretrained(
    MODEL_PATH,
    trust_remote_code=True
)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_PATH,
    torch_dtype=TORCH_TYPE,
    trust_remote_code=True,
).to(DEVICE).eval()

text_only_template = "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: {} ASSISTANT:"

while True:
    image_path = input("image path >>>>> ")
    if image_path == '':
        print('You did not enter image path, the following will be a plain text conversation.')
        image = None
        text_only_first_query = True
    else:
        image = Image.open(image_path).convert('RGB')

    history = []

    while True:
        query = input("Human:")
        if query == "clear":
            break

        if image is None:
            if text_only_first_query:
                query = text_only_template.format(query)
                text_only_first_query = False
            else:
                old_prompt = ''
                for _, (old_query, response) in enumerate(history):
                    old_prompt += old_query + " " + response + "\n"
                query = old_prompt + "USER: {} ASSISTANT:".format(query)
        if image is None:
            input_by_model = model.build_conversation_input_ids(
                tokenizer,
                query=query,
                history=history,
                template_version='chat'
            )
        else:
            input_by_model = model.build_conversation_input_ids(
                tokenizer,
                query=query,
                history=history,
                images=[image],
                template_version='chat'
            )
        inputs = {
            'input_ids': input_by_model['input_ids'].unsqueeze(0).to(DEVICE),
            'token_type_ids': input_by_model['token_type_ids'].unsqueeze(0).to(DEVICE),
            'attention_mask': input_by_model['attention_mask'].unsqueeze(0).to(DEVICE),
            'images': [[input_by_model['images'][0].to(DEVICE).to(TORCH_TYPE)]] if image is not None else None,
        }
        gen_kwargs = {
            "max_new_tokens": 2048,
            "pad_token_id": 128002,  
        }
        with torch.no_grad():
            outputs = model.generate(**inputs, **gen_kwargs)
            outputs = outputs[:, inputs['input_ids'].shape[1]:]
            response = tokenizer.decode(outputs[0])
            response = response.split("<|end_of_text|>")[0]
            print("\nCogVLM2:", response)
        history.append((query, response))

✨ 主要特性

我们推出了新一代的CogVLM2系列模型，并开源了两个基于Meta-Llama-3-8B-Instruct构建的模型。与上一代CogVLM开源模型相比，CogVLM2系列开源模型有以下改进：

1. 在TextVQA、DocVQA等多个基准测试中显著提升。
2. 支持8K内容长度。
3. 支持最高1344 * 1344的图像分辨率。
4. 提供支持中英双语的开源模型版本。

你可以在下表中查看CogVLM2系列开源模型的详细信息：

📚 详细文档

基准测试

与上一代CogVLM开源模型相比，我们的开源模型在多个榜单上取得了良好的成绩。其出色的性能可与一些非开源模型相媲美，如下表所示：

所有评测均未使用任何外部OCR工具（“仅像素”）。

📄 许可证

此模型根据CogVLM2 许可证发布。对于基于Meta Llama 3构建的模型，请同时遵守LLAMA3许可证。

📚 引用

如果您认为我们的工作有帮助，请考虑引用以下论文：

@misc{hong2024cogvlm2,
  title={CogVLM2: Visual Language Models for Image and Video Understanding},
  author={Hong, Wenyi and Wang, Weihan and Ding, Ming and Yu, Wenmeng and Lv, Qingsong and Wang, Yan and Cheng, Yean and Huang, Shiyu and Ji, Junhui and Xue, Zhao and others},
  year={2024}
  eprint={2408.16500},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}

@misc{wang2023cogvlm,
      title={CogVLM: Visual Expert for Pretrained Language Models}, 
      author={Weihan Wang and Qingsong Lv and Wenmeng Yu and Wenyi Hong and Ji Qi and Yan Wang and Junhui Ji and Zhuoyi Yang and Lei Zhao and Xixuan Song and Jiazheng Xu and Bin Xu and Juanzi Li and Yuxiao Dong and Ming Ding and Jie Tang},
      year={2023},
      eprint={2311.03079},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}