Stas Ml Engineering Book Hugging Face

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 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. The ML Engineering Open Book is a comprehensive collection of methodologies, tools, and step-by-step instructions for successfully training, fine-tuning, and deploying large language models (LLMs) and multi-modal models (MLMs). This material is designed for ML training engineers and operators who need practical, technical guidance for handling complex ML systems. This overview page introduces the core components and architecture of ML engineering systems, providing a roadmap to the detailed information contained in the subsequent sections. The focus is on scalable approaches for working with large models across distributed infrastructure. For specific details on hardware components, see Hardware and Infrastructure.

For training methodologies, see Model Training. ML Engineering systems consist of several interconnected components that work together to enable efficient model training and inference. The diagram below illustrates these core components and their relationships: Each of these components plays a critical role in the ML engineering pipeline: New: Hugging Face LLM evaluation guidebook! 🎉 This guide was created to share both practical insights and theoretical knowledge that the 🤗 evaluation team gathered, while managing the Open LLM Leaderboard and designing lighteval!

➡️ Whether you're a beginner in LLMs, or an advanced user working on production-side models, you should find something to help you! https://lnkd.in/eammwcz3 Some contents: - how to create your own evaluation for your specific use case 🔧 - insights on current methods' pros and cons ⚖️ - troubleshooting advice 🔍 - lots of tips and... With Nathan HABIB, we'll also add applied notebooks to show you how to do evaluation experiments fast and follow good practices! If you want more knowledge or see a reference missing, feel free to open an issue! The creation of this guide was inspired by Stas Bekman's great ML engineering book, and will similarly be updated regularly :) Thanks to all who influenced this guide through discussions, among which Kyle Lo,... Machine Learning Architect | PhD | Max Planck Alumni | AI Innovation Enthusiast

This diagram in the "Tips and Tricks" section will be a lifesaver! Some programmers complain about the use of 'whitespace' in Python (tabs and spaces are part of the code), but in prompting it's much harder to debug! A space in the wrong position can lower your model's IQ! Bootstrapped Founder | Building 🎬 LocalClip.app — a macOS AI video clipper, local alternative to Klap, Opusclip & Sendshort Khaled ALNUAIMI, CFA Abdulla Alketbi, CFA My name is Stas Bekman and I'm a software engineer who enjoys tinkering, building reliable systems and who excells at identifying and solving problems, and writes about it.

I have been writing software since 1994. I have worked in multiple domains, for many years taught at major tech conferences and user groups, published several books, and currently I specialize in training large language models (LLM) (and multi-modal) in the... I have been working on various Natural language processing tasks - from ML translation to generative models. But the main direction is training Large Language Models (LLM) and Visual Language Models (VLM). While I can build a whole system from the ground up, I have a knack, intuition and an extended experience dealing with a variety of problems in software. In particular, I'm good at identifying and sorting out performance issues, such as memory leaks, speed bottlenecks, but also various other types of bugs in systems (in particular difficult bugs).

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