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Artur Niederfahrenhorst

Kourosh Hakhamaneshi

Rehaan Ahmad

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

Waleed Kadous

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

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Fine Tuning Is For Form, Not Facts | Anyscale

Fine tuning is for form, not facts

Avnish Narayan

Learn how you can achieve up to 1.7x infrastructure cost savings by using the newly introduced multi-GPU training stack in RLlib.

Figure 2. Episodic Return of PPO Agents trained using differing amounts of Nvidia T4 GPUs. Using more GPUs led to significantly higher performance in the same time-frame.


rllib_figure_5

RLlib Multi-GPU Stack | Affordable, Scalable RL Agents Training

Introducing RLlib Multi-GPU Stack for Cost Efficient, Scalable, Multi-GPU RL Agents Training

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)

factuality 373 examples

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

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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 Kourosh Hakhamaneshi

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