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- ---
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- license: apache-2.0
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ license: mit
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+ library_name: transformers
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+ pipeline_tag: image-text-to-text
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+ ---
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+ ![header](./assets/header.png)
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+
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+ <p align="center">
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+ 📃 <a href="https://arxiv.org/abs/2409.02889" target="_blank">Paper</a> • 🌐 <a href="" target="_blank">Demo</a> • 📃 <a href="https://github.com/FreedomIntelligence/LongLLaVA" target="_blank">Github</a> • 🤗 <a href="https://huggingface.co/FreedomIntelligence/LongLLaVA-9B" target="_blank">LongLLaVA-9B</a>
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+ </p>
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+
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+ ![efficiency](./assets/singleGPU.png)
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+
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+
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+ ## 🌈 Update
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+
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+ * **[2024.09.05]** LongLLaVA repo is published!🎉 The Code will
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+
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+ ## Architecture
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+
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+ <details>
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+ <summary>Click to view the architecture image</summary>
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+
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+ ![Architecture Image](./assets/arch.png)
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+
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+ </details>
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+
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+
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+ ## Results
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+
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+ <details>
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+ <summary>Click to view the Results</summary>
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+
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+ - Main Results
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+ ![Main Results](./assets/result1.png)
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+ - Diagnostic Results
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+ ![Diagnostic Results](./assets/diaresult.png)
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+ - Video-NIAH
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+ ![Video-NIAH](./assets/NIAH.png)
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+
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+ </details>
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+
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+
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+
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+ ## Results reproduction
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+
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+
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+ ### Evaluation
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+
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+ - Preparation
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+
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+ Get the model inference code from [Github](https://github.com/FreedomIntelligence/LongLLaVA).
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+
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+ ```bash
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+ git clone https://github.com/FreedomIntelligence/LongLLaVA.git
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+ ```
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+
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+ - Environment Setup
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+
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+ ```bash
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+ pip install -r requirements.txt
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+ ```
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+
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+
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+ - Command Line Interface
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+
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+ ```bash
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+ python cli.py --model_dir path-to-longllava
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+ ```
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+
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+
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+ - Model Inference
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+
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+ ```python
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+ query = 'What does the picture show?'
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+ image_paths = ['image_path1'] # image or video path
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+
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+ from cli import Chatbot
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+ bot = Chatbot(path-to-longllava)
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+ output = bot.chat(query, image_paths)
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+ print(output) # Prints the output of the model
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+ ```
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+
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+
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+ ## Acknowledgement
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+
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+ - [LLaVA](https://github.com/haotian-liu/LLaVA): Visual Instruction Tuning (LLaVA) built towards GPT-4V level capabilities and beyond.
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+
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+ ## Citation
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+
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+ ```
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+ @misc{wang2024longllavascalingmultimodalllms,
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+ title={LongLLaVA: Scaling Multi-modal LLMs to 1000 Images Efficiently via Hybrid Architecture},
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+ author={Xidong Wang and Dingjie Song and Shunian Chen and Chen Zhang and Benyou Wang},
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+ year={2024},
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+ eprint={2409.02889},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.CL},
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+ url={https://arxiv.org/abs/2409.02889},
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+ }
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+ ```
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+
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+
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+