Note: Course registration originally included compute credits on several platforms. Registration still includes access to all videos, materials and the Discord community. But it no longer includes compute credits.

This started as an LLM fine-tuning course. It organically grew into a conference with world-class speakers on a broad range of LLM topics. The original fine-tuning course is still here as a series of workshops. But there are now many self-contained talks and office hours from experts on many Generative AI topics.

All materials + recordings will be available to participants who enroll. There are 11 talks and 4 workshops (and growing) in addition to office hours.

Conference Talks

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Jeremy Howard: Co-Founder Answer.AI & Fast.AI

- Build Applications For LLMs in Python

Sophia Yang: Head of Developer Relations, Mistral AI

- Best Practices For Fine Tuning Mistral

Simon Willison: Creator of Datasette, co-creator of Django, PSF Board Member

- Language models on the command-line

JJ Allaire: CEO, Posit (formerly RStudio) & Researcher for the UK AI Safety Institute

- Inspect, An OSS framework for LLM evals

Wing Lian: Creator of Axolotl library for LLM fine-tuning

- Fine-Tuning w/Axolotl

Mark Saroufim and Jane Xu: PyTorch developers @ Meta

- Slaying OOMs with PyTorch FSDP and torchao

Jason Liu: Creator of Instructor

- Systematically improving RAG applications 

Paige Bailey: DevRel Lead, GenAI, Google

- When to Fine-Tune?

Emmanuel Ameisen: Research Engineer, Anthropic

- Why Fine-Tuning is Dead

Hailey Schoelkopf: research scientist, Eleuther AI, maintainer, LM Evaluation Harness

- A Deep Dive on LLM Evaluation

Johno Whitaker: R&D at AnswerAI

- Fine-Tuning Napkin Math

John Berryman: Author of O'Reilly Book Prompt Engineering for LLMs

- Prompt Eng Best Practices

Ben Clavié: R&D at AnswerAI

- Beyond the Basics of RAG

Abhishek Thakur leads AutoTrain at HuggingFace

- Train (almost) any llm model using 🤗 Autotrain

Kyle Corbitt is currently building OpenPipe

- From prompt to model: fine-tuning when you've already deployed LLMs in prod

Ankur Goyal: CEO and Founder at Braintrust

- LLM Eval For Text2SQL

Freddy Boulton: Software Engineer at 🤗

- Let's Go, Gradio!

Jo Bergum: Distinguished Engineer at Vespa

- Back to basics for RAG

Fine-Tuning Course

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Run an end-to-end LLM fine-tuning project with modern tools and best practices. Four workshops guide you through productionizing LLMs, including evals, fine-tuning and serving.

Workshop 1: Determine when (and when not) to fine-tune an LLM

Workshop 2: Train your first fine-tuned LLM with Axolotl

Workshop 3: Set up instrumentation and evaluation to incrementally improve your model

Workshop 4: Deploy Your Model

This is accompanied by 5+ hours of office hours. Lectures explain the why and demonstrate the how for all the key pieces in LLM fine-tuning. Your hands-on-experience in the course project will ensure your ready to apply your new skills in real business scenarios.

The Fine-Tuning course has these guest speakers:

- Shreya Shankar: LLMOps and LLM Evaluations researcher

- Zach Mueller: Lead maintainer of HuggingFace accelerate

- Bryan Bischof: Director of AI Engineering at Hex

- Charles Frye: AI Engineer at Modal Labs

- Eugene Yan: Senior Applied Scientist @ Amazon

- Harrison Chase: CEO of LangChain

- Travis Addair: Co-Founder & CTO of Predibase

- Joe Hoover: Lead ML Engineer at Replicate

FAQ:

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Q: It says this course already started. Should I still Enroll?

A: Yes. Everything is recorded, so you can watch videos for any events that have happened so far, join for live events moving forward, and even learn from talks long after the conference is over.

Q: Will there be a future cohort?

A: No. We were fortunate to have so many world-class speakers. We don't think this can be replicated, so it is now a one-time-only event with all recordings available.

Q: Are you still giving out free compute credits?

A: No. Students who enrolled after 5/29/2024 are not eligible for compute credits. You will still get access to the lectures and recordings. EXCEPTION: if you enroll in the course by 6/10/2024 and use Modal by 6/11/2024, they will give you $1,000 in compute credits.