Fine-tuning LLMS using Q Laura

A Quick Walkthrough

Slide1: Introduction

  • Luke Monington presents a quick walkthrough on fine-tuning LLMS using Q Laura
  • LLMS reduces GPU VRAM requirement for model fine-tuning
  • Subscribe for more content
  • Follow Luke Monington on Twitter for interesting articles, thoughts, and updates

Slide2: Required Libraries

  • Bits and bytes library: CUDA functions for 8-bit optimization and matrix multiplication
  • Transformers library: Collection of pre-trained models for various tasks
  • PEFT library: Parameter efficient fine-tuning methods
  • Accelerate: User-friendly tool for writing training loops for PyTorch models

Slide3: Bits and Bytes Library

  • Offers custom CUDA functions for 8-bit optimization and matrix multiplication
  • Improves performance of AI models on GPU
  • Accessible to developers
  • Optimizes how AI models run on GPU

Slide4: Transformers Library

  • Collection of pre-trained models for various tasks
  • Text, images, audio, and multi-data types
  • Compatible with Jax, PyTorch, and TensorFlow
  • Train models with one library, load them with another

Slide5: PEFT Library

  • Parameter efficient fine-tuning methods
  • Tweak pre-trained language models for different applications
  • Significantly reduces computational and storage costs
  • PEFT methods include Laura, P-tuning, and Adalora

Slide6: Accelerate

  • User-friendly tool for writing training loops for PyTorch models
  • Handles multi-device setups
  • Supports multiple GPUs, TPUs, and mixed precision
  • Easily switch between different environments

Slide7: Loading the Model

  • Load the LLM model (eleuther AI GPT Neo x20b) from Hugging Face's Model Hub
  • Configure bits and bytes for 4-bit quantization and B float 16 data type
  • Prepare the model for k-bit training
  • Get the number of trainable parameters

Slide8: Data Preparation

  • Load the dataset from the datasets library
  • Feed the data through the tokenizer
  • Convert data to machine-readable tokens
  • Check the first line of the dataset

Slide9: Training Q Laura Parameters

  • Define hyperparameters for training
  • Use PagedAdam W-8bit Optimizer
  • Disable caching for training, enable for inference
  • Train the Q Laura parameters

Slide10: Saving and Uploading

  • Save the Q Laura parameters locally
  • Or upload them to Hugging Face's Model Hub
  • Choose the best hyperparameters for optimal results
  • Perform hyperparameter tuning if desired

Slide11: Inference

  • Tokenize the input text
  • Feed tokens through Q Laura
  • Convert machine-readable outputs to human-readable

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