Christian Mills - Mastering LLMs Course Notes

Published

May 31, 2024

Modified

July 16, 2024

Course Links:

Mastering LLMs: A Conference For Developers & Data Scientists

Workshop 1: When and Why to Fine-Tune an LLM

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Workshop #1 provides a practical overview of fine-tuning large language models, focusing on when it is and is not beneficial, emphasizing a workflow of simplification, prototyping, and iterative improvement using evaluations.

Workshop 2: Fine-Tuning with Axolotl

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Workshop #2 builds on Workshop 1 to focus on practical fine-tuning of LLMs, covering model selection, fine-tuning techniques with Axolotl, data quality improvement, debugging, and using tools like Accelerate and Modal.

Conference Talk 1: Ten Commandments to Deploy Fine-Tuned Models in Production

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This talk by Kyle Corbitt from OpenPipe outlines ten key recommendations for successfully deploying fine-tuned language models (LLMs) in production.

Office Hours 1: Axolotl Q&A with Wing Lian

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This Q&A session covered various topics, including template-free prompt construction, data type selection for HuggingFace datasets, DPO and RLHF, understanding chat templates and dataset types, ensuring consistent tokenization, multimodal fine-tuning, and future directions for Axolotl.

Office Hours 2: Q&A Session with Zach Mueller

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This Q&A session covers various aspects of LLM fine-tuning, including tools, techniques, data sets, and hardware considerations.

Office Hours 3: Gradio Q&A Session with Freddy Boulton

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Freddy showcases Gradio’s features, advantages, and repository contributions, highlighting its potential for AI applications. He concludes with insights into its future roadmap, which includes enhanced agent workflows, real-time streaming, and improved UI features.

Workshop 3: Instrumenting & Evaluating LLMs

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Workshop #3 focuses on the crucial role of evaluation in fine-tuning and improving LLMs. It covers three main types of evaluations: unit tests, LLM as a judge, and human evaluation.

Conference Talk 2: LLM Eval For Text2SQL

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This talk by Ankur Goyal from BrainTrust covers how to build evals for LLM systems by walking through a Text2SQL use case.

Conference Talk 3: Prompt Engineering Workshop

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This talk by John Berryman covers the fundamentals of language models, prompt engineering techniques, and building LLM applications.

Conference Talk 4: Inspect - An OSS Framework for LLM Evals

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In this talk, J.J. Allaire walks through the core concepts and design of the Inspect framework and demonstrate its use for a variety of evaluation tasks.

Office Hours 4: Modal with Charles Frye

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This Q&A session covers a wide array of topics related to Modal, a platform designed to simplify the execution of Python code in the cloud.

Office Hours 5: LangChain/LangSmith

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This Q&A session on LangChain/LangSmith covers topics like product differentiation, features, use cases, agent workflows, data set creation, and full-stack development for ML engineers.

Conference Talk 5: Napkin Math For Fine Tuning with Johno Whitaker

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In this talk, Jonathan Whitaker from answer.ai shows how to build intuition around training performance with a focus on GPU-poor fine tuning.

Office Hours 6: Johno Whitaker

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This Q&A session covers a wide range of topics related to LLMs, including practical tips for training and optimization, insights into the current research landscape, and thoughts on future trends.

Conference Talk 6: Train Almost Any LLM Model Using 🤗 autotrain

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In this talk, Abhishek Thakur, who leads AutoTrain at 🤗, shows how to use 🤗 AutoTrain to train/fine-tune LLMs without having to write any code.

Workshop 4: Instrumenting & Evaluating LLMs

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Workshop #4 focuses on the practical aspects of deploying fine-tuned LLMs, covering various deployment patterns, performance optimization techniques, and platform considerations.

Conference Talk 7: Best Practices For Fine Tuning Mistral

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In this talk, Sophia Yang from Mistal AI covers best practices for fine-tuning Mistral language models. It covers Mistral’s capabilities, the benefits of fine-tuning over prompting, and provides practical demos using the Mistral Fine-tuning API and open-source codebase.

Conference Talk 8: Creating, curating, and cleaning data for LLMs

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In this talk, Daniel van Strien from 🤗 outlines key considerations and techniques for creating high-quality datasets for fine-tuning LLMs.

Conference Talk 9: Why Fine-Tuning is Dead

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In this talk, Emmanuel Ameisen from Anthropic argues that fine-tuning LLMs is often less effective and efficient than focusing on fundamentals like data quality, prompting, and Retrieval Augmentation Generation (RAG).

Conference Talk 10: Systematically Improving RAG Applications

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In this talk, Jason Liu covers a a systematic approach to improving Retrieval Augmented Generation (RAG) applications.

Conference Talk 12: Slaying OOMs with PyTorch FSDP and torchao

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In this talk, Mark Saroufim and Jane Xu, discuss techniques and tools for mitigating Out of Memory (OOM) errors in PyTorch, specifically when working with LLMs.

Conference Talk 13: When to Fine-Tune with Paige Bailey

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In this talk, Paige Bailey, Generative AI Developer Relations lead at Google, discusses Google’s AI landscape with a focus on Gemini models and their applications.