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Continuum Knowledge BankContinuum Applications
  • Introduction
  • Creation of Environment
    • Platform Installation
    • Axolotl Dependencies
    • setup.py objectives
      • script analysis
  • Huggingface Hub
  • Download the dataset
    • Types of Dataset Structures
    • Structuring Datasets for Fine-Tuning Large Language Models
    • Downloading Huggingface Datasets
    • Use Git to download dataset
    • Popular Datasets
    • Download cleaned Alpaca dataset
    • Template-free prompt construction
  • Downloading models
    • Phi 2.0 details
    • Downloading Phi 2.0
    • Available Models
  • Configuration for Training
  • Datasets
  • Model Selection - General
  • Phi 2.0
    • Phi 2.0 - Model Configuration
    • Phi 2.0 - Model Quantization
    • Phi 2.0 - Data Loading and Paths
    • Phi 2.0 - Sequence Configuration
    • Phi 2.0 - Lora Configuration
    • Phi 2.0 - Logging
    • Phi 2.0 - Training Configuration
    • Phi 2.0 - Data and Precision
    • Phi 2.0 - Optimisations
    • Phi 2.0 - Extra Hyperparameters
    • Phi 2.0 - All Configurations
    • Phi 2.0 - Preprocessing
    • Phi 2.0 - Training
    • Uploading Models
  • Llama2
    • Llama2 - Model Configuration
    • Llama2 - Model Quantization
    • Llama2 - Data Loading and Paths
    • Llama2 - Sequence Configuration
    • Llama2 - Lora Configuration
    • Llama2 - Logging
    • Llama2 - Training Configuration
    • Llama2 - Data and Precision
    • Llama2 - Optimisations
    • Llama2 - Extra Hyperparameters
    • Llama2- All Configurations
    • Llama2 - Training Configuration
    • Llama2 - Preprocessing
    • Llama2 - Training
  • Llama3
    • Downloading the model
    • Analysis of model files
      • Model Analysis - Configuration Parameters
      • Model Analysis - Safetensors
      • Tokenizer Configuration Files
        • Model Analysis - tokenizer.json
        • Model Analysis - Special Tokens
    • Llama3 - Model Configuration
    • Llama3 - Model Quantization
    • Llama3 - Data Loading and Paths
    • Llama3 - Sequence Configuration
    • Llama3 - Lora Configuration
    • Llama3 - Logging
    • Llama3 - Training Configuration
    • Llama3 - Data and Precision
    • Llama3 - Optimisations
    • Llama3 - Extra Hyperparameters
    • Llama3- All Configurations
    • Llama3 - Preprocessing
    • Llama3 - Training
    • Full Fine Tune
  • Special Tokens
  • Prompt Construction for Fine-Tuning Large Language Models
  • Memory-Efficient Fine-Tuning Techniques for Large Language Models
  • Training Ideas around Hyperparameters
    • Hugging Face documentation on loading PEFT
  • After fine tuning LLama3
  • Merging Model Weights
  • Merge Lora Instructions
  • Axolotl Configuration Files
    • Configuration Options
    • Model Configuration
    • Data Loading and Processing
    • Sequence Configuration
    • Lora Configuration
    • Logging
    • Training Configuration
    • Augmentation Techniques
  • Axolotl Fine-Tuning Tips & Tricks: A Comprehensive Guide
  • Axolotl debugging guide
  • Hugging Face Hub API
  • NCCL
  • Training Phi 1.5 - Youtube
  • JSON (JavaScript Object Notation)
  • General Tips
  • Datasets
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This documentation is for the Axolotl community

On this page
  • Background
  • Core Purpose
  • Supported Features

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Introduction

NextCreation of Environment

Last updated 1 year ago

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This is Continuum's documentation for training large language models.

This training platform has been put together by a dedicated group of people, whose generosity has allowed a community to form and become able to fine tune a variety of large language models.

Background

The GitHub repository "Axolotl" provides a versatile tool designed for the fine-tuning of various AI models, specifically targeting ease of use and flexibility in handling different model configurations and architectures.

Core Purpose

Axolotl is aimed at streamlining the fine-tuning process of AI models, offering compatibility with a wide range of Huggingface models and fine-tuning techniques.

Supported Features

  • Model Support: It supports training with various Huggingface models

  • Fine-tuning Techniques: The tool supports several fine-tuning methods

  • Configuration Flexibility: Users can customise configurations using a YAML file or override settings via the CLI.

  • Dataset Compatibility: Axolotl can load different dataset formats, support custom formats, or handle user-provided tokenized datasets.

  • Integration with Advanced Tools: The tool integrates with xformer, flash attention, rope scaling, and multipacking for enhanced model performance and efficiency.

  • Multi-GPU Support: It facilitates training on single or multiple GPUs using FSDP or Deepspeed.

  • Docker Support: Axolotl can be easily run with Docker, either locally or on the cloud.

  • Experiment Tracking: It allows logging results and optionally checkpoints to WandB or MLflow.

GitHub - OpenAccess-AI-Collective/axolotl: Go ahead and axolotl questionsGitHub
Link to Axolotl GitHub Repository
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