This repository serves as a comprehensive guide and practical resource for large language model (LLM) engineering and deployment. It targets engineers, researchers, and practitioners looking to understand and implement LLM training, fine-tuning, inference, and optimization techniques, offering a structured approach to complex LLM workflows.

How It Works

The project is organized into distinct sections covering the entire LLM lifecycle, from foundational principles to advanced applications. It details various training methodologies (full fine-tuning, LoRA, QLoRA), distributed training strategies (data, pipeline, tensor parallelism), and inference optimization techniques (quantization, pruning, distillation, KV cache optimization). The content emphasizes practical implementation with code examples and theoretical explanations, aiming to demystify LLM engineering.

Quick Start & Requirements

• Installation: Primarily relies on Python environments and HuggingFace libraries. Specific commands depend on the module being explored.
• Prerequisites: Python 3.x, PyTorch, HuggingFace Transformers, PEFT, DeepSpeed, and potentially CUDA-enabled GPUs for practical examples.
• Resources: Varies significantly by section; LLM training and large-scale inference require substantial GPU resources (e.g., 40GB+ VRAM for QLoRA on LLaMA-65B).
• Links: The README provides extensive internal links to tutorials and explanations within the repository.

Highlighted Details

• Detailed tutorials on parameter-efficient fine-tuning (PEFT) methods like LoRA, QLoRA, and P-Tuning v2.
• Comprehensive coverage of distributed training techniques including data, pipeline, and tensor parallelism.
• In-depth guides on LLM inference optimization, including quantization (GPTQ, AWQ) and specialized engines like TensorRT-LLM and vLLM.
• Extensive benchmarks and evaluation datasets (C-Eval, CMMLU, LongBench) for assessing LLM performance.

Maintenance & Community

• The repository is actively maintained by liguodongiot.
• Community engagement is encouraged through WeChat groups.
• A WeChat public account ("吃果冻不吐果冻皮") shares AI engineering practices.

Licensing & Compatibility

• The repository itself does not explicitly state a license. Code examples and tutorials likely inherit licenses from the underlying libraries (e.g., HuggingFace, PyTorch).
• Compatibility for commercial use would depend on the licenses of the specific models and libraries used in the examples.

Limitations & Caveats

The repository is a collection of tutorials and practical guides rather than a single, runnable tool. Users need to assemble and configure components based on their specific LLM tasks. Some advanced topics like distributed training and specific hardware acceleration (e.g., Huawei Ascend) may require specialized knowledge and environments.