Awesome LLM Training and Inference: A Comprehensive Guide to Full-Stack Tool Resources for Large Model Development

This guide is a tool resource list for the entire lifecycle of LLM development, covering the complete workflow from data processing, model training, fine-tuning to deployment. It includes mainstream frameworks like PyTorch, DeepSpeed, vLLM, and Ollama, as well as specialized tools for quantization, RAG, and evaluation, providing a one-stop technical guide for developers to address the challenges of the large and complex LLM ecosystem and tool selection.

With the rapid development of large language model technology, LLM development, training, and deployment have become active directions in the AI field. However, the ecosystem is large and complex, involving numerous technology stacks from underlying frameworks to upper-layer application tools. Developers and researchers face significant challenges in selecting suitable solutions from the vast array of tools, which is why this resource list was created.

The guide covers tools for all stages of LLM development:

1. Deep Learning Foundation Frameworks: PyTorch (dynamic graph, easy to use), JAX (functional, distributed), Hugging Face Transformers (unified model interface);
2. Training Optimization: Accelerate (simplifies distributed training), DeepSpeed (ZeRO optimization, large model training);
3. Inference Frameworks: vLLM (PagedAttention improves throughput), SGLang (structured generation), llama.cpp (CPU inference), Ollama (one-click local running);
4. Model Compression: BitsAndBytes (8-bit quantization), AWQ/GPTQ (4-bit quantization);
5. Efficient Fine-tuning: LoRA (Low-Rank Adaptation), QLoRA (quantization + LoRA);
6. RAG and Vector Search: LangChain (RAG toolchain), Milvus/Qdrant (vector databases);
7. Data Engineering: Hugging Face Datasets (dataset management), Tokenizers (tokenization);
8. Evaluation and MLOps: lm-evaluation-harness (benchmark testing), Weights & Biases (experiment tracking);
9. Privacy and Vertical Models: PrivateGPT (local privacy), CodeLlama (code domain), BioGPT (medical domain), etc.

Application value of specific tools:

• vLLM improves GPU memory utilization via the PagedAttention algorithm and supports high concurrent throughput;
• llama.cpp ports LLaMA models to C/C++, optimizes CPU inference, and is suitable for local deployment;
• LoRA only trains a small number of low-rank matrix parameters, reducing fine-tuning costs with no additional inference overhead;
• LangChain provides a complete RAG toolchain to quickly build applications with dynamic knowledge updates;
• Ollama simplifies local LLM operation, enabling one-click model downloads and providing an OpenAI-compatible API.

The LLM ecosystem is evolving rapidly, with new tools and methods emerging continuously. This list provides developers with a systematic technical map to help find suitable tools in the complex technology stack. Recommendations:

1. Choose tools based on your own needs (e.g., training/inference, local/cloud, domain scenarios);
2. Follow new developments in the ecosystem and try innovative tools in a timely manner;
3. Use this list as a reference for learning and development to improve the efficiency of LLM projects.