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Ben Lorica

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10 must-attend Ray Summit sessions: Generative AI, Reinforcement learning, scalable ML workloads, and more

10 must-attend Ray Summit sessions: Generative AI, scalable ML workloads, and more

Zhe Zhang

Ion Stoica

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Ray For Success

Four Reasons Why Leading Companies Are Betting On Ray

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7 must-attend Ray Summit sessions: RL-powered traffic control, infra-less ML, and more

Ray Ecosystem

What’s new in the Ray Distributed Library Ecosystem

Anyscale Platform

Why you should build your AI Applications with Ray

RaySummit2021

Ray Summit 2021 CFP Now Open!

ExamplesOfToolsMultiCloud

The Emergence of Multi-cloud Native Applications and Platforms

Eric Liang

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Birds

The Ideal Foundation for a General Purpose Serverless Platform

Dean Wampler

featured speakers

Trends in AI and Python Scalability: Reflections from the Ray Summit Program Chairs

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Here’s what you need to look for in a model server to build ML-powered services

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Five Key Features for a Machine Learning Platform

Understanding the Ray Ecosystem and Community

In this guide, we will learn how to develop and productionize a retrieval augmented generation (RAG) based LLM application, with a focus on scale, evaluation and routing.  

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Building RAG-based LLM Applications for Production (Part 1)

In this blog, we compare full-parameter fine-tuning with LoRA and answer questions around the strengths and weaknesses of the two techniques. 

lora blog 1

Fine-Tuning LLMs: LoRA or Full-Parameter? An in-depth Analysis with Llama 2

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Llama 2 is about as factually accurate as GPT-4 for summaries and is 30X cheaper

In this blog, we provide a thorough analysis and a practical guide for fine-tuning. We examine the Llama-2 models under three real-world use cases, and show that fine-tuning yields significant accuracy improvements across the board (in some niche cases, better than GPT-4). 

Llama 2 performance

Fine-Tuning Llama-2: A Comprehensive Case Study for Tailoring Models to Unique Applications

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From the creators of Ray, Anyscale is a framework for building machine learning applications at any scale originating from the UC Berkeley RISELab.

Posts by Ben Lorica

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