This repository provides the official code for Context Cascade Compression (C3), a novel method designed to explore and push the boundaries of text compression. It targets researchers and practitioners in natural language processing and data compression, offering a pre-trained model and implementation to achieve state-of-the-art compression ratios.

How It Works

C3 implements a context cascade compression strategy, building upon a Qwen Large Language Model (LLM) as its foundational architecture. The approach is inspired by advancements in OCR models like DeepSeek-OCR and adapts code from GOT-OCR2.0, aiming for a unique synergy that enhances text compressibility through cascaded contextual understanding.

Quick Start & Requirements

• Installation requires cloning the repository and setting up a Conda environment with Python 3.10.
• Key dependencies include PyTorch 2.6.0 (requiring CUDA 11.8), Transformers 4.49.0, and transformers-stream-generator.
• Pre-trained model weights (version with 32 latent tokens) are available on Huggingface.
• Usage examples are provided via Python code snippets leveraging the transformers library and a standalone script run_c3.py.
• Relevant links: Huggingface weights, Paper.

Highlighted Details

• Official implementation of the Context Cascade Compression (C3) research paper.
• Focuses on achieving "upper limits of text compression" through its unique architecture.
• Leverages a Qwen LLM as the core generative model.
• Codebase adapted from the GOT-OCR2.0 project.

Maintenance & Community

• Direct contact for inquiries is via email: liufanfan19@mails.ucas.ac.cn.
• The project acknowledges contributions and inspirations from DeepSeek-OCR, GOT-OCR2.0, and Qwen.
• No explicit community channels (e.g., Discord, Slack) or a public roadmap are detailed in the README.

Licensing & Compatibility

• The provided README does not specify a software license.
• This omission makes it impossible to determine compatibility for commercial use or closed-source integration without further clarification.

Limitations & Caveats

• The README does not detail specific limitations, known bugs, or the project's development stage (e.g., alpha/beta).
• Setup requires a specific CUDA version (11.8) and PyTorch version, potentially limiting hardware compatibility.
• The absence of a clear license is a significant adoption blocker.