Ai2 OLMoE iOS app: Fully open source, running entirely on-device https://www.youtube.com/watch?v=rEK_FZE5rqQ

From concept to reality: Navigating the Journey of RAG from proof of concept to production In this post, we explore the movement of RAG applications from their proof of concept or minimal viable product (MVP) phase to full-fledged production systems. When transitioning a RAG application from a proof of concept to a production-ready system, optimization becomes crucial to make sure the solution is reliable, cost-effective, and high-performing.
https://aws.amazon.com/blogs/m....achine-learning/from

LLM-as-a-judge on Amazon Bedrock Model Evaluation This blog post explores LLM-as-a-judge on Amazon Bedrock Model Evaluation, providing comprehensive guidance on feature setup, evaluating job initiation through both the console and Python SDK and APIs, and demonstrating how this innovative evaluation feature can enhance generative AI applications across multiple metric categories including quality, user experience, instruction following, and safety.
https://aws.amazon.com/blogs/m....achine-learning/llm-

Achieve ~2x speed-up in LLM inference with Medusa-1 on Amazon SageMaker AI Researchers developed Medusa, a framework to speed up LLM inference by adding extra heads to predict multiple tokens simultaneously. This post demonstrates how to use Medusa-1, the first version of the framework, to speed up an LLM by fine-tuning it on Amazon SageMaker AI and confirms the speed up with deployment and a simple load test. Medusa-1 achieves an inference speedup of around two times without sacrificing model quality, with the exact improvement varying based on model size and data used. In this post, we demonstrate its effectiveness with a 1.8 times speedup observed on a sample dataset.
https://aws.amazon.com/blogs/m....achine-learning/achi

Fine-tune LLMs with synthetic data for context-based Q&A using Amazon Bedrock In this post, we explore how to use Amazon Bedrock to generate synthetic training data to fine-tune an LLM. Additionally, we provide concrete evaluation results that showcase the power of synthetic data in fine-tuning when data is scarce.
https://aws.amazon.com/blogs/m....achine-learning/fine