BAAI
/

Video-XL-2

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 ---
 license: apache-2.0
 ---
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 ---
+language:
+- en
+library_name: transformers
 license: apache-2.0
+metrics:
+- accuracy
+tags:
+- multimodal
+pipeline_tag: video-text-to-text
 ---
+# Video-XL-2
+[\[📰 Blog\]](https://unabletousegit.github.io/video-xl2.github.io/) [\[📂 GitHub\]](https://github.com/VectorSpaceLab/Video-XL)   [\[📜 Tech Report(comming soon)\]]()
+## 🚀 How to use the model
+```python
+from transformers import AutoTokenizer, AutoModel, AutoConfig, BitsAndBytesConfig
+import torch
+# load model
+model_path = '/root/Models/Video-XL-2'
+tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+model = AutoModel.from_pretrained(model_path, trust_remote_code=True, device_map=device,quantization_config=None,attn_implementation="sdpa").to(torch.bfloat16)
+gen_kwargs = {
+    "do_sample": True,
+    "temperature": 0.01,
+    "top_p": 0.001,
+    "num_beams": 1,
+    "use_cache": True,
+    "max_new_tokens": 256
+}
+enable_sparse = False # use the sparse pattern or not
+sparse_mode = 'streaming' # streaming or mask
+if enable_sparse:
+    model.config.enable_sparse = True
+    model.config.sparse_mode = sparse_mode
+    block_size_chosed = 4
+    prev_blocks_num = 3
+    model.config.sparse_config = {'block_size_chosed':block_size_chosed, 'prev_blocks_num':prev_blocks_num}
+else:
+    model.config.enable_sparse = False
+# input data
+video_path = "/asset/demo.mp4"
+question1 = "How many people in the video? (A)3 people (B)6 people. Please only respone the letter"
+# params
+max_num_frames = 100
+sample_fps = 1  # extract frame at 1fps
+max_sample_fps = 4
+with torch.inference_mode():
+    response = model.chat(video_path, tokenizer, question1, chat_history=None, return_history=False,max_num_frames=max_num_frames, sample_fps=sample_fps, max_sample_fps=max_sample_fps, generation_config=gen_kwargs)
+print(response)
+```
+## ✏️ Citation
+```bibtex
+@article{shu2024video,
+  title={Video-XL: Extra-Long Vision Language Model for Hour-Scale Video Understanding},
+  author={Shu, Yan and Zhang, Peitian and Liu, Zheng and Qin, Minghao and Zhou, Junjie and Huang, Tiejun and Zhao, Bo},
+  journal={arXiv preprint arXiv:2409.14485},
+  year={2024}
+}
+@article{liu2025video,
+  title={Video-XL-Pro: Reconstructive Token Compression for Extremely Long Video Understanding},
+  author={Liu, Xiangrui and Shu, Yan and Liu, Zheng and Li, Ao and Tian, Yang and Zhao, Bo},
+  journal={arXiv preprint arXiv:2503.18478},
+  year={2025}
+}
+```

config.json CHANGED Viewed

@@ -1,8 +1,11 @@
 {
-  "_name_or_path": "/share/LXRlxr0_0/code/videoxl2/videoxl2/checkpoints/videoxl2_0410",
   "architectures": [
     "LlavaQwenForCausalLM"
   ],
   "attention_dropout": 0.0,
   "bos_token_id": 151643,
   "eos_token_id": 151645,

 {
   "architectures": [
     "LlavaQwenForCausalLM"
   ],
+  "auto_map": {
+    "AutoConfig": "llava_qwen.LlavaQwenConfig",
+    "AutoModel": "llava_qwen.LlavaQwenForCausalLM"
+  },
   "attention_dropout": 0.0,
   "bos_token_id": 151643,
   "eos_token_id": 151645,