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CogVLM2

👋 Wechat · 💡Online Demo · 🎈Github Page

📍Experience the larger-scale CogVLM model on the ZhipuAI Open Platform.

Model introduction

We launch a new generation of CogVLM2 series of models and open source two models built with Meta-Llama-3-8B-Instruct. Compared with the previous generation of CogVLM open source models, the CogVLM2 series of open source models have the following improvements:

  1. Significant improvements in many benchmarks such as TextVQA, DocVQA.
  2. Support 8K content length.
  3. Support image resolution up to 1344 * 1344.
  4. Provide an open source model version that supports both Chinese and English.

You can see the details of the CogVLM2 family of open source models in the table below:

Model name cogvlm2-llama3-chat-19B cogvlm2-llama3-chinese-chat-19B
Base Model Meta-Llama-3-8B-Instruct Meta-Llama-3-8B-Instruct
Language English Chinese, English
Model size 19B 19B
Task Image understanding, dialogue model Image understanding, dialogue model
Text length 8K 8K
Image resolution 1344 * 1344 1344 * 1344

Benchmark

Our open source models have achieved good results in many lists compared to the previous generation of CogVLM open source models. Its excellent performance can compete with some non-open source models, as shown in the table below:

Model Open Source LLM Size TextVQA DocVQA ChartQA OCRbench MMMU MMVet MMBench
CogVLM1.1 ✅ 7B 69.7 - 68.3 590 37.3 52.0 65.8
LLaVA-1.5 ✅ 13B 61.3 - - 337 37.0 35.4 67.7
Mini-Gemini ✅ 34B 74.1 - - - 48.0 59.3 80.6
LLaVA-NeXT-LLaMA3 ✅ 8B - 78.2 69.5 - 41.7 - 72.1
LLaVA-NeXT-110B ✅ 110B - 85.7 79.7 - 49.1 - 80.5
InternVL-1.5 ✅ 20B 80.6 90.9 83.8 720 46.8 55.4 82.3
QwenVL-Plus ❌ - 78.9 91.4 78.1 726 51.4 55.7 67.0
Claude3-Opus ❌ - - 89.3 80.8 694 59.4 51.7 63.3
Gemini Pro 1.5 ❌ - 73.5 86.5 81.3 - 58.5 - -
GPT-4V ❌ - 78.0 88.4 78.5 656 56.8 67.7 75.0
CogVLM2-LLaMA3 (Ours) ✅ 8B 84.2 92.3 81.0 756 44.3 60.4 80.5
CogVLM2-LLaMA3-Chinese (Ours) ✅ 8B 85.0 88.4 74.7 780 42.8 60.5 78.9

All reviews were obtained without using any external OCR tools ("pixel only").

Quick Start

here is a simple example of how to use the model to chat with the CogVLM2 model. For More use case. Find in our github

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

MODEL_PATH = "THUDM/cogvlm2-llama3-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))

License

This model is released under the CogVLM2 LICENSE. For models built with Meta Llama 3, please also adhere to the LLAMA3_LICENSE.

Citation

If you find our work helpful, please consider citing the following papers

@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}
}
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