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Waleed Kadous

factuality 373 examples

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

Kourosh Hakhamaneshi

Fine tuning is one approach to domain-specific model refinement (DSMR), but it’s not a silver bullet for improving domain-specific performance.

fine-tune-img-1

Fine Tuning Is For Form, Not Facts | Anyscale

Fine tuning is for form, not facts

Aviary Explorer

Announcing Aviary: Open Source Multi-LLM Serving

For LLM developers, it's useful to know numbers for back-of-the-envelope calculations. Here are the numbers we use, plus how and why to use them.

numbers-cover-image

Numbers Every LLM Developer Should Know | Anyscale

Numbers every LLM Developer should know

In part 3 of our blog series, we’ll show you how to build an LLM question and answering service in 20 minutes using LangChain & Ray.

qna-answering-questions-with

Building a Self-Hosted Question Answering Service in 20 Minutes

Building a Self Hosted Question Answering Service using LangChain + Ray in 20 minutes

In part 1 of a new blog series, we show how to build a search engine in 100 lines using LLM embeddings and a vector database.

Build a document index easily with Ray and Langchain

doc-index-starter-langchain

Building an LLM Open-Source Search Engine in 100 Lines

Building an LLM open source search engine in 100 lines using LangChain and Ray

Jun Gong

Antoni Baum

Richard Liaw

Richard Liaw headshot

In part 4 of our Generative AI series, we share how to build a system for fine-tuning & serving LLMs in 40 minutes or less.

fine-tune-stack

How to Fine-Tune a 6 Billion Parameter LLM for Less Than $7

How to fine tune and serve LLMs simply, quickly and cost effectively using Ray + DeepSpeed + HuggingFace

Eric Liang

Eric Liang headshot

Chen Shen

Clark Zinzow

3rdGenTasks andActors

Data Ingest in a Third-Generation ML Architecture | Anyscale

Deep Dive: Data Ingest in a Third Generation ML Architecture

mlplatformCropped

Why 3rd Generation ML Platforms are More Performant

Why Third Generation ML Platforms are More Performant

Uber Leveraging Ray

The 3rd Generation of Production ML Architectures | Anyscale

The Third Generation of Production ML Architectures

WaleedKadous345

Why I’m joining Anyscale

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

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 Waleed Kadous

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