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Misha Laskin

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Practical tips for training Deep Q Networks

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Reinforcement learning with Deep Q Networks

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The reinforcement learning framework

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An informal introduction to reinforcement learning

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. 

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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). 

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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 Misha Laskin

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