#6969th of 88 in Learning and cookbooks
llm-engineers-handbook
Companion code for a book on building an end-to-end LLM Twin system
- Stars
- 5.4k
- License
- MIT
- Last commit
- Apr 2026
- Language
- Python
4,884 on 21 Apr 20265,428 on 10 Nov 2026
From GitHub's daily star counts.
Checked on GitHub.
Overview
Official code for the LLM Engineer's Handbook. It implements an "LLM Twin" pipeline: crawling data into MongoDB, building RAG with Qdrant, fine-tuning and evaluating a model on AWS SageMaker, and serving it through a REST API. Pipelines are orchestrated with ZenML, with Comet ML and Opik for experiment tracking and prompt monitoring.
Who it is for: Engineers learning production LLM and MLOps workflows on AWS
Strengths
- Covers the full path: data collection, training, RAG, deployment, monitoring and CI/CD
- Local MongoDB, Qdrant and ZenML server start with one poe command
- Layered package following domain-driven design, with ZenML pipelines and YAML configs
- Final TwinLlama-3.1-8B-DPO model is published on Hugging Face
Weaknesses
- Training, evaluation and LLM inference require AWS SageMaker; they do not run locally
- Needs OpenAI, Hugging Face and Comet accounts; AWS and OpenAI usage is paid
- Tied to a fixed stack (ZenML, Poetry, Python 3.11), not a reusable library
- Cloud deployment steps are detailed in the book's chapters 10 and 11, not the README
What it needs
- Docker + Compose
- Needs AWS SageMaker, OpenAI API, Hugging Face, Comet ML, Opik, ZenML, MongoDB, Qdrant, Docker, Poetry
- Models: Hugging Face models, OpenAI API, AWS SageMaker endpoints
- port 8237
Quick answers
Is llm-engineers-handbook open source?
Yes. Its code is on GitHub under the MIT license.
Can you run llm-engineers-handbook with Docker?
Yes. Its repository has a Docker setup: a Dockerfile, a Compose file or a published image.
Also in Learning and cookbooks
See all 88| Rank | Project | Score |
|---|---|---|
| 1 | ai-engineering-from-scratchHands-on AI engineering curriculum with 542 lessons across 20 phases | 91 out of 100 |
| 2 | generative-ai-for-beginners21-lesson course on building generative AI apps in Python and TypeScript | 83 out of 100 |
| 3 | rag_techniquesCollection of 42 runnable notebooks implementing RAG techniques | 81 out of 100 |
| #4 | cs249r_bookOpen textbook and course materials for machine learning systems engineering | 81 out of 100 |
| #5 | ai-for-beginners24-lesson AI curriculum with notebooks in PyTorch and TensorFlow | 80 out of 100 |