Awesome

✨ TL;DR

BitStack breaks down large language models into tiny little blocks, which can be sorted and stacked universally, achieving megabyte-level memory-performance tradeoffs while maintaining or surpassing the performance of practical compression methods like GPTQ and AWQ. Check out our paper for more details!

📰 News

• [2024-11-06] 🚀 We've released Triton kernels optimized for fused inference with BitStack models! These kernels deliver an impressive 3x to 10x speedup over the original implementation. Just set the --fused_level flag to get started! For more details, check out the speedup visualization here.
• [2024-11-01] 🎈 Try out this Colab demo and play with BitStack models across various memory budgets using an intuitive slider built with Gradio!
• [2024-11-01] 📄 Check out our paper on arXiv!
• [2024-10-31] ✨ Pre-decomposed models are now available on HuggingFace🤗!
• [2024-10-31] 🚀 Code release! We have some awesome inference kernels for BitStack models coming soon, stay tuned!

🚀 Quick Start

⚙️ Installation

conda create -yn bitstack python=3.10
conda activate bitstack
pip install -e .

🔄 Decomposition

To run the decomposition of a model, run this script or the following command:

python -m bitstack.main \
    --model_name_or_path meta-llama/Meta-Llama-3.1-8B \
    --niter 16 \ # Number of iterations of decomposition, decrease for shorter runtime
    --k 16 \ # Number of singular vectors kept
    --nsamples 256 \ # Number of calibration samples
    --output_dir outputs \
    --do_save \
    --score_importance \ # Run the sorting process
    --generate_compression_configs # Generate compression configs

📊 Evaluation

To evaluate the decomposed model, run this script or the following command:

python -m bitstack.main \
    --model_name_or_path /YOUR/CHECKPOINT/PATH \
    --k 16 \
    --max_memory_MB 5541 \ # Maximum available memory for the model
    --load_bitstack \ # Load the decomposed model
    --do_eval \ # Perplexity evaluation
    --lm_eval \ # Zero-shot evaluation
    --output_dir outputs

📌 Checkpoints

We provide pre-decomposed models and compression configs. Currently, the following models are available, with more to come—stay tuned!

You can download them via the following commands:

# (Optional) enable hf_transfer for faster download
# pip install hf_transfer
# export HF_HUB_ENABLE_HF_TRANSFER=1

huggingface-cli download BitStack/BitStack-Llama-3.1-8B --local-dir ./models/BitStack-Llama-3.1-8B

Or just download the compression config for your already decomposed model:

huggingface-cli download BitStack/BitStack-Llama-3.1-8B --local-dir /YOUR/CHECKPOINT/PATH/ --include "compression_config.json"

📖 Citation

@misc{wang2024bitstackfinegrainedsizecontrol,
      title={BitStack: Fine-Grained Size Control for Compressed Large Language Models in Variable Memory Environments}, 
      author={Xinghao Wang and Pengyu Wang and Bo Wang and Dong Zhang and Yunhua Zhou and Xipeng Qiu},
      year={2024},
      eprint={2410.23918},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2410.23918}, 
}