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  • Llama2
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  • Llama3
    • Downloading the model
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      • Model Analysis - Configuration Parameters
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    • Llama3 - Model Configuration
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    • Llama3 - Sequence Configuration
    • Llama3 - Lora Configuration
    • Llama3 - Logging
    • Llama3 - Training Configuration
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    • 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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  1. Axolotl Configuration Files

Logging

Weights and Biases

These are the configurations for tracking our fine tuning experiments.

Class

Description

wandb_project

Specifies the name of the project in Weights & Biases (WandB) for experiment tracking.

wandb_entity

Specifies the entity (user or organisation) in WandB under which the project will be created.

wandb_watch

A flag indicating whether to watch and log the model's progress in WandB. If set to "true," it means the experiment's progress and model performance will be actively monitored and logged in WandB.

wandb_run_id

Specifies a unique ID for the WandB run associated with this experiment.

wandb_log_model

A flag indicating whether to log the model to WandB. If set to "true," it suggests that the trained model's weights and configurations will be logged to WandB as part of the experiment's output.

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