Introduction In Chapter 5 (MLOps), we built a CI/CD pipeline. This chapter explores a...
Introduction
In Chapter 5 (MLOps), we built a CI/CD pipeline. This chapter explores a different approach: fine-tuning — training the model itself on your own data.
[RAG]
Question → search DB → pass results to LLM → answer
→ Requires documents, search costs apply
Large Language Models (LLMs) have revolutionized how we interact with and build conversational AI systems. While these models…
Fine-tuning an LLM means taking a general pre-trained model and training it further on your own data...
A practical walkthrough of LoRA and QLoRA -- how low-rank adaptation works, what NF4 quantization brings, and when to use each.
Why post-training open-source models on your own data often beats using frontier models for specialized tasks, and how…
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Part 1 of a 4-part series. Full fine-tuning a tiny Gemma 3 model for intent classification — the generative framing, the…
