stas00/ml-engineering

Project overview

An open collection of methodologies, scripts, and copy-n-paste instructions for training, fine-tuning, inferring, and debugging large language models and multi-modal models. It is intended for practitioners who already possess technical knowledge of ML training engineering.

Project type

Model Development · Data Processing · Infrastructure

Deployment

Refer to project documentation

License

CC-BY-SA-4.0

Best for

• AI engineers and researchers with existing technical knowledge of LLM/VLM training engineering who need practical methodologies and copy-n-paste solutions.

Key capabilities

• Provides detailed methodologies, scripts, and commands for successfully training and fine-tuning large language models (LLMs) and multi-modal models.
• Contains model inference insights to help practitioners.
• Offers guides and copy-n-paste solutions for debugging pytorch applications and resolving hanging or breaking issues.
• Maintains a SKILL.md file designed to teach AI agents to train and operate large-scale ML models more effectively.

Limitations and risks

• The practical know-how documented comes from the expensive cost of renting huge ML compute clusters, which limits similar firsthand experience for users.

Getting started

• Choose a consumption format such as web or ebook, read the documentation, and apply the copy-n-paste solutions to your applications.

Evidence and sources

• README: This is an open collection of methodologies, tools and step by step instructions to help with successful training and fine-tuning of large language models and multi-modal models a…
• README: This is a technical material suitable for LLM/VLM training engineers and operators.
• README: That is the content here contains lots of scripts and copy-n-paste commands to enable you to quickly address your needs.
• README: a lot of the know-how I acquired while training the open-source BLOOM-176B model in 2022 and IDEFICS-80B multi-modal model in 2023
• README: You can download various ebook formats of this book: * [PDF](https://huggingface.co/stas/ml-engineering-book/resolve/main/Stas%20Bekman%20-%20Machine%20Learning%20Engineering.pdf?…

Reviewed at: 2026-07-17T12:27:57Z