Machine Learning Llm Vlm Training And Engineering By Stas Bekman

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). 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...

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Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info. AI/ML & GenAI Consultant | Tech Lead | Architecting LLM, Agentic & Predictive AI Solutions Machine Learning Engineering Online Book by Stas Bekman 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 Stas acquired while training the open-source BLOOM-176B model in 2022 and IDEFICS-80B... Currently, he is working on developing/training open-source Retrieval Augmented models at Contextual.AI. Table of Contents Part 1. Insights - The AI Battlefield Engineering - What You Need To Know Part 2. Key Hardware Components - Accelerator - the work horses of ML - GPUs, TPUs, IPUs, FPGAs, HPUs, QPUs, RDUs (WIP) - Network - intra-node and inter-node connectivity, calculating bandwidth requirements - IO - local... Part 3.

Performance - Fault Tolerance - Performance - Multi-Node networking - Model parallelism Part 4. Operating - SLURM - Training hyper-parameters and model initializations - Instabilities Part 5. Development - Debugging software and hardware failures - And more debugging - Reproducibility - Tensor precision / Data types - HF Transformers notes - making small models, tokenizers, datasets, and other tips Part 6. Miscellaneous - Resources - LLM/VLM chronicles Link: https://lnkd.in/eSnWU92x Navigational hashtags: #armknowledgesharing #armbooks General hashtags: #llm #gpt #gpt3 #gpt4 #ml #engineering #mlsystemdesign #systemdesign #reproducibility #performance There was an error while loading. Please reload this page.

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. 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.

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