Github Stas00 Ml Engineering Machine Learning Engineering Open Book

Leo Migdal

-Nov 17, 2025, 1:53 PM

github stas00 ml engineering machine learning engineering open book

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 and their inference. This is a technical material suitable for LLM/VLM training engineers and operators. That is the content here contains lots of scripts and copy-n-paste commands to enable you to quickly address your needs. This repo is an ongoing brain dump of my experiences training Large Language Models (LLM) (and VLMs); a lot of the know-how I acquired while training the open-source BLOOM-176B model in 2022 and IDEFICS-80B... I've been compiling this information mostly for myself so that I could quickly find solutions I have already researched in the past and which have worked, but as usual I'm happy to share these... The AI Battlefield Engineering - what you need to know in order to succeed.

An open collection of methodologies to help with successful training of large language models and multi-modal models. This is a technical material suitable for LLM/VLM training engineers and operators. That is the content here contains lots of scripts and copy-n-paste commands to enable you to quickly address your needs. This repo is an ongoing brain dump of my experiences training Large Language Models (LLM) (and VLMs); a lot of the know-how I acquired while training the open-source BLOOM-176B model in 2022 andIDEFICS-80B multi-modal... Currently, I'm working on developing/training open-source Retrieval Augmented models at Contextual.AI. I've been compiling this information mostly for myself so that I could quickly find solutions I have already researched in the past and which have worked, but as usual I'm happy to share these...

My apologies if the layout is a bit unstable while I'm writing new chapters and gradually re-organizing the content to be more intuitive. ✔ Machine Learning: ML Engineering Open Book | ML ways | Porting ✔ Tools and Cheatsheets: bash | conda | git | jupyter-notebook | make | python | tensorboard | unix This is an open collection of methodologies, tools and step by step instructions to help with successful training of large language models and multi-modal models. This is a technical material suitable for LLM/VLM training engineers and operators. That is the content here contains lots of scripts and copy-n-paste commands to enable you to quickly address your needs.

This repo is an ongoing brain dump of my experiences training Large Language Models (LLM) (and VLMs); a lot of the know-how I acquired while training the open-source BLOOM-176B model in 2022 and IDEFICS-80B... Currently, I'm working on developing/training open-source Retrieval Augmented Generation (RAG) models at Contextual.AI. I've been compiling this information mostly for myself so that I could quickly find solutions I have already researched in the past and which have worked, but as usual I'm happy to share these... My apologies if the layout is a bit unstable while I'm writing new chapters and gradually re-organizing the content to be more intuitive. This is an open collection of methodologies, tools and step by step instructions to help with successful training of large language models and multi-modal models. This is a technical material suitable for LLM/VLM training engineers and operators.

That is the content here contains lots of scripts and copy-n-paste commands to enable you to quickly address your needs. This repo is an ongoing brain dump of my experiences training Large Language Models (LLM) (and VLMs); a lot of the know-how I acquired while training the open-source BLOOM-176B model in 2022 and IDEFICS-80B... Currently, I’m working on developing/training open-source Retrieval Augmented Generation (RAG) models at Contextual.AI. I’ve been compiling this information mostly for myself so that I could quickly find solutions I have already researched in the past and which have worked, but as usual I’m happy to share these... My apologies if the layout is a bit unstable while I’m writing new chapters and gradually re-organizing the content to be more intuitive. This is not a model but a container to hold the PDF version of the Machine Learning Engineering Open Book that you can find at https://github.com/stas00/ml-engineering

If you're building ML systems in production, you know the gap between theory and real-world engineering can feel massive. That's where the Machine Learning Engineering Open Book comes in—a free, community-driven resource packed with practical knowledge for deploying ML at scale. Created by Stas Bekman, this open-source book (hosted on GitHub) covers the gritty details of ML engineering that most tutorials skip. Think distributed training, debugging hanging PyTorch processes, GPU memory optimization, and infrastructure design—all with real code snippets and battle-tested advice. This isn’t just another "ML 101" guide. It’s the kind of resource you’ll bookmark for those "oh crap" moments when your 8-GPU training job hangs at 90%.

Whether you’re debugging NCCL timeouts or designing a model-serving pipeline, there’s likely a section here that’ll save you hours. For more projects like this, follow @githubprojects. Subscribe to our newsletter to get the latest updates on open-source projects. Senior Manager - Data Science and Engineering at Apple | Docker Captain | LinkedIn Learning Instructor Machine Learning Engineering Online Book 🚀 I came across this amazing repo by 𝐒𝐭𝐚𝐬 𝐁𝐞𝐤𝐦𝐚𝐧 - the Machine Learning Engineering Online Book with a collection of guides for ML engineering focusing on training LLM... This book is still 𝐖𝐈𝐏, and it covers the following topics: ✅ Hardware concepts such as working with CPUs, GPUs, networks, etc.

✅ Performance - model parallelism, multi-node networking ✅ Development - debugging, reproducibility, data types ✅ Operating - Training hyperparameters, instabilities, etc. Book 📖: https://lnkd.in/guHBiGNb License: Attribution-ShareAlike 4.0 International 🦄 Thanks Stas, for making this book available online! 🙏🏼 #machinelearning #datascience #MLOps #llm Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info. This repository provides a comprehensive collection of methodologies, tools, and step-by-step instructions for successful training of large language models (LLMs) and multi-modal models.

It is a technical resource suitable for LLM/VLM training engineers and operators, containing numerous scripts and copy-n-paste commands to facilitate quick problem-solving. The repository is an ongoing compilation of the author's experiences training BLOOM-176B and IDEFICS-80B models, and currently focuses on the development and training of Retrieval Augmented Generation (RAG) models at Contextual.AI. The content is organized into six parts: Insights, Hardware, Orchestration, Training, Development, and Miscellaneous. It includes key comparison tables for high-end accelerators and networks, as well as shortcuts to frequently needed tools and guides. The repository is open to contributions and discussions, and is licensed under Attribution-ShareAlike 4.0 International. 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 and their inference.

This is a technical material suitable for LLM/VLM training engineers and operators. That is the content here contains lots of scripts and copy-n-paste commands to enable you to quickly address your needs. This repo is an ongoing brain dump of my experiences training Large Language Models (LLM) (and VLMs); a lot of the know-how I acquired while training the open-source BLOOM-176B model in 2022 and IDEFICS-80B... I've been compiling this information mostly for myself so that I could quickly find solutions I have already researched in the past and which have worked, but as usual I'm happy to share these...

Github Stas00 Ml Engineering Machine Learning Engineering Open Book

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