🌐 Project Page    |    📑 Paper    |    🤗 Model

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📰 News

• [2026.01.04] Released visualization utilities for inspecting attention score in both attention and Mamba layers inside the LLM (see visualize/nano_attention_visualization_cookbook.ipynb).
• [2025.12.11] Added support for any two visual encoder combinations, such as DINOv2, InternVideo2, and SigLIP2.
• [2025.11.25] We provide model w/ Nano or w/ Qwen as backbones, and evaluation codes for MCQ (VideoMME, LVBench, MLVU, LongVideoBench, EgoSchema, MVBench, TempCompass, CGBench), TVG (Charades, ActivityNet, TVGBench), VDC (VDC), and DVC (YouCook2) benchmarks.
• [2025.11.21] 🚀 Initial release of the TimeViper repository. The paper is available on arXiv.

📖 Introduction

We present TimeViper, a hybrid Mamba-Transformer vision-language model for efficient long video understanding. We introduce TransV, the first token-transfer module that compresses vision tokens into text tokens inside the LLM, enabling the model to process over 10,000 frames.

✨ Key Features

• Hybrid MLLM Architecture
  Integrates native hybrid LLM for long video understanding.

• Efficient Long Video Processing
  Capable of handling over 10K frames with significantly lower memory cost compared to standard Video-LLMs.

• Flexible Backbones
  Supports MLLM construction with Transformer-based LLM backbones such as Qwen2.5 or hybrid LLMs like Nanov2.

• Advanced Techniques
  Includes token dropping (TD) and token transfer (TransV) for training compression.

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📝 TODO List

• Add inference code
• Add model code
• Add training code
• Release model weights
• Add detailed instructions for preparing data & env & evaluation & training
• Support training with Qwen and Nano backbones
• Support pdrop and TransV for both training and evaluation

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🐍 Model Zoo

Model	Backbone	Max Frames	Checkpoint
TimeViper-9B	Nanov2-9B	5k	Coming Soon
TimeViper-9B-w/TransV	Nanov2-9B	10k+	Coming Soon

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🛠️ Installation

We provide comprehensive documentation for setting up TimeViper. Please follow these guides in order:

1. INSTALL.md - Environment Setup

   • Install dependencies and required packages
   • Configure CUDA, PyTorch, and other system requirements
   • Set up the Python virtual environment

2. MODEL.md - Model Checkpoint Download

   • Download ViT backbone and LLM backbone checkpoints from Hugging Face
   • Automated download script for all required models
   • Verification of checkpoint integrity

3. DATA.md - Dataset Preparation

   • Prepare training and evaluation datasets
   • Instructions for downloading benchmark datasets
   • Data directory structure and format specifications

4. USAGE.md - Usage Guide

   • Configure visual encoders, MLPs, and LLM backbones
   • Enable training-free token dropping (pdrop)
   • Advanced model configuration options

🚀 Quick Start

Training

Coming Soon.

Evaluation

# evaluate
python evaluate.py \
    --dataset videomme \
    --split test \
    --output_dir ./output/timeviper_base/videomme \
    --curr_idx 0 --total_idx 1
# calculate metrics 
python eval/vllm_inference/eval_all.py --model_name $MODEL_NAME --split $CURRENT_SPLIT --dataset $CURRENT_EVAL_DATASET --max_num_frames $MAX_FRAME_NUM \
        --eval_root $PART_OUTPUT_DIR

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🔍 Visualization: Attention in Mamba-2 or Attention Layer

We release utilities to visualize internal token interactions inside the LLM, covering both Transformer attention layers and Mamba/SSM (Mamba) layers.

This might be useful for understanding how the model allocates computation across video frames / visual tokens and text tokens.

Notebook

• visualize/nano_attention_visualization_cookbook.ipynb

The notebook provides:

• Extraction of per-layer signals for attention layers and Mamba/SSM layers.
• Side-by-side plots across depth (early → mid → late layers).
• Examples that highlight how interactions change with long video inputs.

📄 License

This project is released under the Apache 2.0 License.

📚 Citation

If you find TimeViper useful for your research and applications, please cite our paper:

@article{xu2025timeviper,
  title={TimeViper: A Hybrid Mamba-Transformer Model for Efficient Long Video Understanding},
  author={Xu, Boshen and Xiao, Zihan and Li, Jiaze and Ju, Jianzhong and Luo, Zhenbo and Luan, Jian and Jin, Qin},
  journal={arXiv preprint arXiv:2511.16595},
  year={2025}
}

🙏 Acknowledgement

We thank the following open-source projects for their contributions: Cobra, Vamba, transformers, vllm, mamba, Time-R1, VideoChat-Flash.