LLM Construction and Deployment: A Complete Practical Guide from Training to Production

This project is maintained by SandroAlex and hosted on GitHub (link: https://github.com/SandroAlex/llms-build-and-deploy). It aims to fill the gap where developers only call APIs but lack a deep understanding of LLM construction and deployment. The guide covers the entire workflow from architecture design, training optimization to production deployment, suitable for developers who want to dive deep into the engineering implementation of LLMs.

Current LLM technology is booming, but most developers stay at the API calling level. Building LLMs faces three core challenges:

1. Computational resource bottleneck: High training costs (e.g., millions of dollars for GPT-3-level models), requiring solutions for distributed training, memory optimization, etc.;
2. Data engineering complexity: High-quality data requires collection and cleaning, deduplication and decontamination, ratio optimization, and efficient pipeline construction;
3. Model architecture selection: Covers Transformer basics, variants (e.g., Llama's RMSNorm), efficient attention (Flash Attention), and long-context extension techniques.

Deployment needs to focus on three directions:

1. Inference optimization: Quantization (INT8/INT4), KV caching, dynamic batching, speculative decoding;
2. Service architecture: Load balancing, auto-scaling, streaming responses, fault tolerance mechanisms;
3. Cost control: Model distillation, request routing, caching strategies, spot instance utilization.

Learning Path:

• Beginner: Understand Transformers → Small-scale experiments → Familiarize with toolchains (PyTorch/DeepSpeed) → Reproduce classic models;
• Intermediate: Distributed training → Fine-tuning techniques (LoRA/QLoRA) → Alignment techniques (RLHF/DPO) → Evaluation systems;
• Production: Inference engines (vLLM/TensorRT-LLM) → Containerized deployment → Monitoring and observability → Security and compliance. Toolchain:
• Training: Hugging Face Transformers, DeepSpeed, Megatron-LM;
• Inference: vLLM, TensorRT-LLM, llama.cpp;
• Evaluation: lm-evaluation-harness, OpenCompass, MT-bench.

Vertical domains: Healthcare (medical knowledge enhancement), law (regulatory understanding), finance (financial report analysis), education (personalized teaching); Multimodal fusion: Vision-language models (GPT-4V), voice interaction, code generation (GitHub Copilot), embodied intelligence; Efficiency trends: Model compression (pruning/distillation), green AI, edge deployment, continuous learning.

This project provides a systematic learning roadmap for LLM engineering practice. LLM construction and deployment involve multiple technical fields, requiring in-depth professional knowledge and practice. It is recommended to maintain a learning mindset, keep up with academic frontiers and industrial practices, and improve skills through hands-on reproduction (e.g., nanoGPT).