An open collection of methodologies, scripts, and step-by-step instructions for training, fine-tuning, and debugging large language models and multi-modal models. The material is designed for practitioners who need consolidated know-how and copy-n-paste solutions.
Project overview
It consolidates practical engineering knowledge drawn from direct experience training major open-source models such as BLOOM-176B and IDEFICS-80B.
Project type
Model Development · Data Processing · Infrastructure
Deployment
Refer to project documentation
License
CC-BY-SA-4.0
Best for
AI engineers and researchers who already possess technical knowledge of LLM/VLM training engineering and need consolidated, applied methodologies.
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 originates from the expensive cost of renting huge ML compute clusters, which limits similar firsthand experience for users applying the methods.
Getting started
Choose a format such as web or ebook, then read the material or apply the provided copy-n-paste solutions to your workflows.
Alternatives and comparisons
Provides system innovations like ZeRO and 3D-Parallelism for distributed training. Use this if you need a framework to execute large-scale deep learning training rather than documentation.
Provides a unified framework for scaling Python and AI applications across a cluster. Use this for distributed compute infrastructure rather than training guides.
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?…