LLM Engineering Pipeline

ML & AI · flowchart diagram · MIT

A comprehensive flowchart outlining the end-to-end process of building, training, optimizing, and deploying a Large Language Model (LLM) into a verifiable

Source: https://github.com/jiaran-king/MicroLM/blob/782ae02f10c14b484a317f22115a066b3b10b91d/Readme/%E9%A1%B9%E7%9B%AE%E5%85%A8%E6%99%AF%E5%9B%BE/00-%E5%85%A8%E6%B5%81%E7%A8%8B%E5%88%86%E6%9E%90%EF%BC%88%E8%AE%AD%E7%BB%83%E3%80%81%E6%8E%A8%E7%90%86%E3%80%81%E8%AF%84%E6%B5%8B%E4%B8%8E%E9%83%A8%E7%BD%B2%EF%BC%89.md
Curated by jiaran-king
LLM AI Machine Learning Deployment Data Pipeline Training vLLM

Mermaid source

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flowchart TB
    C1["原始数据"] --> C2["tokenizer / 数据 pipeline"]
    C2 --> C3["pretrain / SFT / LoRA"]
    C3 --> C4["推理优化 / 对话系统"]
    C4 --> C5["自动化评测"]
    C5 --> C6["模型导出"]
    C6 --> C7["vLLM 部署"]
    C7 --> C8["smoke / benchmark / stability 验证"]

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    class C1,C2,C3,C4,C5,C6,C7,C8 chain;

What this diagram shows

This diagram illustrates a complete LLM engineering pipeline, starting from raw data processing, through model training (pretrain, SFT, LoRA), inference optimization, dialogue system integration, automated evaluation, model export, and finally, deployment using vLLM with rigorous verification steps.

When to use it

Use this diagram to understand the full lifecycle of developing and deploying a production-ready LLM. It's suitable for planning LLM projects, onboarding new team members to an LLM development workflow, or auditing existing pipelines.

How to adapt it for your project

This pipeline can be adapted by swapping specific components like different tokenizers, training methods (e.g., DPO), inference engines (e.g., TensorRT-LLM), or deployment platforms. The verification steps can be customized for specific performance or safety requirements.

Key concepts