This repository provides an end-to-end, out-of-the-box training framework for Chinese Natural Language Processing (NLP) tasks, built on PyTorch and Hugging Face Transformers. It targets researchers and developers working with large language models (LLMs) and multimodal models, offering solutions for data preparation, model training, fine-tuning, and deployment.

How It Works

The framework leverages PyTorch and Transformers for model implementation, supporting a wide range of architectures including GPT-2, CLIP, GPT-NeoX, Dolly, Llama, and ChatGLM. It emphasizes efficient data handling for large datasets (hundreds of GBs) using multithreading and memory mapping. A key feature is its multi-GPU support, with modifications to model structures to enable chained multi-GPU training and inference for models exceeding single-GPU memory capacity.

Quick Start & Requirements

• Installation: Primarily through pip.
• Prerequisites: PyTorch, Hugging Face Transformers. Specific models may require additional dependencies. GPU acceleration is highly recommended for training and inference.
• Resources: Supports handling large datasets (100GB+). Multi-GPU setup is crucial for larger models.
• Links: Bilibili Channel for source code interpretation videos.

Highlighted Details

• Supports training and inference for large models exceeding single-GPU memory via multi-GPU chaining.
• Includes comprehensive data processing pipelines, from cleaning to handling large-scale datasets.
• Offers tutorials and implementations for model vocabulary manipulation (trimming and expansion).
• Provides visual explanations (diagrams) for data flows and model architectures.

Maintenance & Community

The project is maintained by yuanzhoulvpi2017. Further community engagement details (Discord, Slack, roadmap) are not explicitly detailed in the README.

Licensing & Compatibility

The README does not explicitly state a license. Compatibility for commercial use or closed-source linking is not specified.

Limitations & Caveats

Some listed models (e.g., Thu-ChatGlm-6b, Chinese Llama) are marked as deprecated. The project appears to be actively developed, with potential for ongoing changes and model updates.