File size: 8,357 Bytes
ce47433 3275a51 ce47433 c95db64 ce47433 8a603a2 ce47433 a809fc4 ce47433 c95db64 ce47433 8a603a2 ce47433 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 |
---
license: other
license_name: cogvlm2
license_link: https://huggingface.co/THUDM/cogvlm2-llama3-chinese-chat-19B/blob/main/LICENSE
language:
- en
pipeline_tag: text-generation
tags:
- chat
- cogvlm2
inference: false
---
# CogVLM2
<div align="center">
<img src=https://raw.githubusercontent.com/THUDM/CogVLM2/53d5d5ea1aa8d535edffc0d15e31685bac40f878/resources/logo.svg width="40%"/>
</div>
<p align="center">
👋 <a href="resources/WECHAT.md" target="_blank">Wechat</a> · 💡<a href="http://36.103.203.44:7861/" target="_blank">Online Demo</a> · 🎈<a href="https://github.com/THUDM/CogVLM2" target="_blank">Github Page</a>
</p>
<p align="center">
📍Experience the larger-scale CogVLM model on the <a href="https://open.bigmodel.cn/dev/api#glm-4v">ZhipuAI Open Platform</a>.
</p>
## Model introduction
We launch a new generation of **CogVLM2** series of models and open source two models built with [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/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](https://github.com/THUDM/CogVLM2)
```python
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))
```
## License
This model is released under the CogVLM2 [LICENSE](LICENSE). For models built with Meta Llama 3, please also adhere to the [LLAMA3_LICENSE](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}
}
```
|