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Daniel Han - Low Level Technicals of LLMs

Open Models: This workshop will be split into 3x one hour blocks: 1. How to analyze & fix LLMs - how to find and fix bugs in Gemma, Phi-3, Llama & tokenizers 2. Finetuning with Unsloth - continued pretraining, reward modelling, QLoRA & more 3. Deep dive into LLM technicals - hand deriving derivatives, SOTA finetuning tricks It's recommended you have Python with Pytorch and Unsloth installed (or use online Google Colab / Kaggle). College level maths and programming would be helpful.

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Open Models: This workshop will be split into 3x one hour blocks:

  1. How to analyze & fix LLMs - how to find and fix bugs in Gemma, Phi-3, Llama & tokenizers
  2. Finetuning with Unsloth - continued pretraining, reward modelling, QLoRA & more
  3. Deep dive into LLM technicals - hand deriving derivatives, SOTA finetuning tricks

It's recommended you have Python with Pytorch and Unsloth installed (or use online Google Colab / Kaggle). College level maths and programming would be helpful.

Daniel Han

Hey I'm Daniel, the algos guy behind Unsloth. I love making LLM training go fast! We're the guys who fixed 8 of Google's Gemma bugs, a 2048 SWA Phi-3 issue, found tokenization issues and fixed untrained tokens with Llama-3, and I run Unsloth with my brother Michael!

Our open source package makes finetuning of LLMs 2x faster and uses 70% less VRAM with no accuracy degradation. I used to work at NVIDIA making GPU algos go fast and helped NASA engineers process data from a Mars rover faster!

Daniel Han
Daniel HanCEO

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