Ray RLlib

Ray Train

Ray Datasets

RayDP

Retail

Gaming

Government

Financial

Ray Serve

Anyscale

Ray Core

Healthcare

Technology

Ray Tune

News

User Story

Engineering

Culture

Archit Kulkarni

Edward Oakes

blog-runtime-env-thumb

blog-recommended-content-nodes-dark

Handling files and packages on your cluster with Ray runtime environments

Amog Kamsetty

Ray x mlflow

Ray & MLflow | Taking Distributed ML Apps to Production

Ray & MLflow: Taking Distributed Machine Learning Applications to Production

Ray Serve plus FastAPI

How to Scale Up Your FastAPI Application Using Ray Serve

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.  

social-rag-based-llm

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

factuality 373 examples

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

This section is used to order the "Types" and "Tags" that show up for filters on the Blog Index

Types

Products / Libraries

Blog

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 Archit Kulkarni

No posts found.