Google Dev: MaxText Expands Post-Training with SFT Introduction

Wed, 06 May 2026 22:26:25 +0000

So, you’ve trained your massive LLM, and now you need to make it yours. You’re looking for that killer fine-tuning solution that doesn’t break the bank or demand a supercomputer cluster. Well, Google’s MaxText just made a significant play with its introduction of Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) capabilities, specifically targeting single-host TPU configurations like v5p-8 and v6e-8. This move aims to democratize advanced LLM customization, leveraging the power of JAX and the Tunix library for high-performance post-training.

Building with Gemini Embedding 2: Agentic Multimodal RAG

Wed, 06 May 2026 22:22:02 +0000

Forget stitching together disparate models for text, image, and audio. The era of fragmented multimodal AI is over, thanks to Gemini Embedding 2. If you’re building retrieval-augmented generation (RAG) systems that need to truly understand the world, not just read it, this is the game-changer you’ve been waiting for.

The Problem: Data is Messy, AI Needs to be Unified

Traditional RAG pipelines excel at text. But what happens when your knowledge base includes product manuals with diagrams, video tutorials explaining complex procedures, or audio recordings of customer feedback? Historically, this meant separate embedding models, complex feature extraction pipelines, and a constant struggle to find relevant information across different modalities. The result? Latency, reduced accuracy, and a development nightmare.

Natural Language Processing on The Coders Blog

Google Dev: MaxText Expands Post-Training with SFT Introduction

Building with Gemini Embedding 2: Agentic Multimodal RAG

The Problem: Data is Messy, AI Needs to be Unified