Hugging Face's logo

OpenGVLab
/
InternVideo2_5_Chat_8B

Video-Text-to-Text
Transformers
Safetensors
English
internvl_chat
feature-extraction
multimodal
custom_code
Eval Results
Model card Files Community
2
InternVideo2_5_Chat_8B
File size: 4,318 Bytes 
9e9fcf6
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
c453339
9e9fcf6
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
 
---
language:
- en
library_name: transformers
license: apache-2.0
metrics:
- accuracy
tags:
- multimodal
pipeline_tag: video-text-to-text
model-index:
- name: InternVideo2.5
  results:
  - task:
      type: multimodal
    dataset:
      name: MLVU
      type: mlvu
    metrics:
    - type: accuracy
      value: 72.8
      name: accuracy
      verified: true
  - task:
      type: multimodal
    dataset:
      name: MVBench
      type: mvbench
    metrics:
    - type: accuracy
      value: 75.7
      name: accuracy
      verified: true
  - task:
      type: multimodal
    dataset:
      name: Perception Test
      type: percepTest
    metrics:
    - type: accuracy
      value: 74.9
      name: accuracy
      verified: true
  - task:
      type: multimodal
    dataset:
      name: LongVideoBench
      type: longvideobench
    metrics:
    - type: accuracy
      value: 60.6
      name: accuracy
      verified: true
  - task:
      type: multimodal
    dataset:
      name: VideoMME (w/o sub)
      type: videomme
    metrics:
    - type: accuracy
      value: 65.1
      name: accuracy
      verified: true
  - task:
      type: multimodal
    dataset:
      name: LVBench
      type: lvbench
    metrics:
    - type: accuracy
      value: 46.4
      name: accuracy
      verified: true


---

# 📕InternVideo2.5⚡
<!-- [\[📰 Blog\]](https://internvideo.github.io/blog/2024-12-31-VideoChat-Flash) -->
[\[📂 GitHub\]](https://github.com/OpenGVLab/InternVideo/tree/main/InternVideo2.5)  
[\[📜 Tech Report\]](https://arxiv.org/abs/2501.12386) 
<!-- [\[🗨️ Chat Demo\]](https://huggingface.co/spaces/OpenGVLab/VideoChat-Flash) -->

 InternVideo2.5 is a video multimodal large language model (MLLM, built upoon InternVL2.5) enhanced with **long and rich context (LRC) modeling**. It significantly improves upon existing MLLMs by enhancing their ability to perceive fine-grained details and capture long-form temporal structures. We achieve this through dense vision task annotations using direct preference optimization (TPO) and compact spatiotemporal representations via adaptive hierarchical token compression (HiCo).




## 📈 Performance
| Model |  MVBench | LongVideoBench |  VideoMME(w/o sub)| 
| ---   |  ---     |   ---            | ---     | 
|InternVideo2.5| 75.7 |  60.6   | 65.1| 

## 🚀 How to use the model

First, you need to install [flash attention2](https://github.com/Dao-AILab/flash-attention) and some other modules. We provide a simple installation example below:
```
pip install transformers==4.40.1
pip install av
pip install imageio
pip install decord
pip install opencv-python
pip install flash-attn --no-build-isolation
```
Then you could use our model:
```python
from transformers import AutoModel, AutoTokenizer

# model setting
model_path = 'OpenGVLab/InternVideo2_5_Chat_8B'

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = AutoModel.from_pretrained(model_path, trust_remote_code=True).half().cuda()
image_processor = model.get_vision_tower().image_processor


# evaluation setting
max_num_frames = 512
generation_config = dict(
    do_sample=False,
    temperature=0.0,
    max_new_tokens=1024,
    top_p=0.1,
    num_beams=1
)

video_path = "your_video.mp4"

# single-turn conversation
question1 = "Describe this video in detail."
output1, chat_history = model.chat(video_path=video_path, tokenizer=tokenizer, user_prompt=question1, return_history=True, max_num_frames=max_num_frames, generation_config=generation_config)

print(output1)

# multi-turn conversation
question2 = "How many people appear in the video?"
output2, chat_history = model.chat(video_path=video_path, tokenizer=tokenizer, user_prompt=question2, chat_history=chat_history, return_history=True, max_num_frames=max_num_frames, generation_config=generation_config)

print(output2)
```

## ✏️ Citation

```bibtex

@article{wang2025internvideo,
  title={InternVideo2.5: Empowering Video MLLMs with Long and Rich Context Modeling},
  author={Wang, Yi and Li, Xinhao and Yan, Ziang and He, Yinan and Yu, Jiashuo and Zeng, Xiangyu and Wang, Chenting and Ma, Changlian and Huang, Haian and Gao, Jianfei and Dou, Min and Chen, Kai and Wang, Wenhai and Qiao, Yu and Wang, Yali and Wang, Limin},
  journal={arXiv preprint arXiv:2501.12386},
  year={2025}
}
```