Project Overview of llama-zip
llama-zip is an innovative compression utility that harnesses the power of Large Language Models (LLMs) to efficiently compress data, particularly excelling with structured and natural language text. Utilizing a technique known as arithmetic coding, llama-zip effectively reduces the size of text by encoding tokens with the fewest bits necessary, based on how accurately the LLM can predict those tokens. The result is a highly efficient compression utility that holds great potential for applications involving enormous text data.
Key Features
- High Compression Ratio: By predicting text with high accuracy using LLMs, llama-zip requires fewer bits to encode data, leading to impressive compression ratios.
- Sliding Context Window: This feature allows the utility to compress strings of indeterminate length without being restricted by the LLM's context length.
- Binary Data Capability: Although its strength lies in text compression, llama-zip can also handle binary data. This is achieved by encoding invalid UTF-8 bytes with specific Unicode code points. However, this capability may yield less impressive compression ratios compared to text.
Compression Performance
The effectiveness of llama-zip's compression is evident when tested against the Calgary Corpus and its own source code. By benchmarking two LLMs at different context lengths, llama-zip has shown significant superiority over many popular compression utilities. Interestingly, while expanding context length generally improves compression ratios, increasing it beyond a certain point can bring diminishing returns. For example, Llama 3.1 performs better on average with an 8k-token context length rather than a 32k-token context length.
Getting Started
Installation
To set up llama-zip, the user can clone its repository from GitHub and then install it through pip:
git clone https://github.com/alexbuz/llama-zip.git
cd llama-zip
pip3 install .
Selecting an LLM
Proper functionality requires downloading an LLM compatible with llama.cpp, such as Llama 3.1 8B. It is essential to ensure that the LLM is quantized and fits within the system memory.
Usage
llama-zip offers a versatile command-line interface with three modes: compress, decompress, and interactive. Compression and decompression allow data manipulation via standard input/output operations, while the interactive mode provides a user-friendly prompt for on-the-fly operations. Key options for usage include defining compressed data format, adjusting window overlap for contexts exceeding LLM limits, setting context length, and configuring GPU layers to improve processing speed.
Use Case Examples
- Compression: To compress text from a file or input directly.
- Decompression: Convert compressed files or base64 strings back to readable text.
- Interactive Mode: Engage directly with compression and decompression tasks through a prompt.
Programmatic Use
For those integrating llama-zip into larger projects, the LlamaZip class allows users to compress and decompress data programmatically using its straightforward methods.
from llama_zip import LlamaZip
compressor = LlamaZip(model_path="/path/to/model.gguf")
original = b"The quick brown fox jumps over the lazy dog."
compressed = compressor.compress(original)
decompressed = compressor.decompress(compressed)
Limitations
Several challenges accompany the cutting-edge nature of llama-zip:
- Speed Constraints: The compression and decompression process is slower due to the LLM's inference speed.
- Portability Issues: Both compression and decompression must occur within the same environment, as deterministic behavior across different systems is not guaranteed.
- Limited Binary Compression Efficiency: While capable, llama-zip is not optimized for compressing binary data as efficiently as it processes text.
Conclusion
llama-zip is a robust tool for those looking to leverage the predictive prowess of LLMs to achieve remarkable text compression. While it introduces some unique challenges, its innovation and performance make it a valuable tool in data management, particularly for text-heavy applications.