Found 9 bookmarks
Newest
microsoft/generative-ai-for-beginners: 21 Lessons, Get Started Building with Generative AI 🔗 https://microsoft.github.io/generative-ai-for-beginners/
microsoft/generative-ai-for-beginners: 21 Lessons, Get Started Building with Generative AI 🔗 https://microsoft.github.io/generative-ai-for-beginners/
21 Lessons, Get Started Building with Generative AI 🔗 https://microsoft.github.io/generative-ai-for-beginners/ - microsoft/generative-ai-for-beginners
·github.com·
microsoft/generative-ai-for-beginners: 21 Lessons, Get Started Building with Generative AI 🔗 https://microsoft.github.io/generative-ai-for-beginners/
MongoDB
MongoDB
Discover our MongoDB Database Management courses and begin improving your CV with MongoDB certificates. Start training with MongoDB University for free today.
·learn.mongodb.com·
MongoDB
Advanced RAG course
Advanced RAG course
Practical RAG techniques for engineers: learn production-ready solutions from industry experts to optimize performance, cut costs, and enhance the accuracy and relevance of your applications.
·wandb.courses·
Advanced RAG course
Roadmaps
Roadmaps
Community driven roadmaps, articles and guides for developers to grow in their career.
·roadmap.sh·
Roadmaps
Developing an LLM: Building, Training, Finetuning
Developing an LLM: Building, Training, Finetuning
REFERENCES: 1. Build an LLM from Scratch book: https://mng.bz/M96o 2. Build an LLM from Scratch repo: https://github.com/rasbt/LLMs-from-scratch 3. Slides: https://sebastianraschka.com/pdf/slides/2024-build-llms.pdf 4. LitGPT: https://github.com/Lightning-AI/litgpt 5. TinyLlama pretraining: https://lightning.ai/lightning-ai/studios/pretrain-llms-tinyllama-1-1b DESCRIPTION: This video provides an overview of the three stages of developing an LLM: Building, Training, and Finetuning. The focus is on explaining how LLMs work by describing how each step works. OUTLINE: 00:00 – Using LLMs 02:50 – The stages of developing an LLM 05:26 – The dataset 10:15 – Generating multi-word outputs 12:30 – Tokenization 15:35 – Pretraining datasets 21:53 – LLM architecture 27:20 – Pretraining 35:21 – Classification finetuning 39:48 – Instruction finetuning 43:06 – Preference finetuning 46:04 – Evaluating LLMs 53:59 – Pretraining & finetuning rules of thumb
·youtube.com·
Developing an LLM: Building, Training, Finetuning