#77 · Primary category： Education & Research
ml-engineering
Machine Learning Engineering Open Book
Project last updated：08/27/26
GitHub Stars
18.8K
Forks
1.2K
Contributors
50
License
Other
Why we included this project
Most guides on training large models stay at the level of theory; this one is a working notebook from someone who actually ran the jobs. The author collected the know-how while training BLOOM-176B in 2022 and IDEFICS-80B in 2023, and the result is a stack of copy-paste commands, benchmark scripts, and troubleshooting notes rather than abstract advice. Engineers about to train or fine-tune a big model will find practical coverage of hardware choice, storage and networking, SLURM orchestration, distributed debugging, and inference setup, plus ready-to-run utilities like a network throughput benchmark and a distributed GPU connectivity test. There is even a SKILL.md file that lets an AI agent learn the same operational practices. If your team is ramping up on large-model operations, this is the kind of reference you keep open next to the docs.
Articles for this project
No articles for this project yet.
To suggest a topic or contribute an article, contact us.
Related projects in this category
prompts.chat
f.k.a. Awesome ChatGPT Prompts. Share, discover, and collect prompts from the community. Free and open source — self-host for your organization with complete privacy.
JavaGuide
Java Interview & Backend General Interview Guide, covering computer fundamentals, databases, distributed systems, high concurrency, system design, and AI application development.
system-prompts-and-models-of-ai-tools
A curated collection of system prompts, internal tools, and AI models from popular AI assistants and coding agents.
30-seconds-of-code
Coding articles to level up your development skills
generative-ai-for-beginners
21 Lessons, Get Started Building with Generative AI