Finetune LLMs to teach them ANYTHING with Huggingface and Pytorch | Step-by-step tutorial скачать в хорошем качестве

Finetune LLMs to teach them ANYTHING with Huggingface and Pytorch | Step-by-step tutorial

This in-depth tutorial is about fine-tuning LLMs locally with Huggingface Transformers and Pytorch. We use Meta's new Llama-3.2-1B-Instruct model and teach it to predict paper categories using LORA adapters. Along the way I break down all the major things you must know about fine-tuning, from prompting, creating datasets, generating input-output pairs, loss functions, pytorch optimizers, peft LORA adapters, and ofcourse the sweet feeling when the test accuracy goes up. :) Follow on Twitter: https://x.com/neural_avb Get 25% off on Ninjachat. Access multiple frontier LLMs, image, video, audio generation models all in one place. Use this link: https://ninjachat.ai/?ref=avishek and the code AI25 to get 25% off! All the notebooks, datasets, and python code used in this video have been uploaded to my Patreon:   / neuralbreakdownwithavb   I upload all the code, slides, animations, write-ups etc for all my videos on my Patreon, so go check it out if you find anything interesting. Videos you might like: Llama3.2 Multimodal Application -    • Llama 3.2 Vision - How to make a Multimoda...   Apple Intelligence LLM Breakdown -    • The Machine Learning behind Apple Intellig...   50 concepts to know NLP:    • 10 years of NLP history explained in 50 co...   Attention to Transformers playlist:    • Attention to Transformers from zero to hero!   Notes on Hardware and Quantization: I didn't go over quantization in this video, coz I'm on a Macbook and bitsandbytes don't work outside NVIDIA gpus. :) Hopefully, I'll make a separate video one day about quantization. The system I am using is a MacBook Pro M2 16GB ram. If you have nvidia gpus, you could leverage better quantization. For my machine, I was able to train with batch size of 8 in float32… the sequence lengths were around 250 on average for this task. If I were working on a product, I’d rent cloud gpu servers and fine tune over there on large datasets. For a YT video with an educational intent, I decided to limit the scope to local machines. #ai #deeplearning #machinelearning 0:00 - Intro 2:04 - Huggingface Transformers Basics 4:49 - Tokenizers 8:39 - Instruction Prompts and Chat Templates 12:35 - Dataset creation 15:54 - Next word prediction 20:52 - Loss functions on sequences 28:28 - Complete finetuning with Pytorch 31:38 - LORA Finetuning with PEFT 35:38 - Results