# Condensed: Single GPU Billion-scale Model Training via Parameter-Efficient Finetuning

Summary: This tutorial demonstrates parameter-efficient finetuning techniques for large language models using IA3 and BitFit, enabling billion-scale model training on limited hardware like single GPUs. It provides implementation code for configuring AutoGluon's MultiModalPredictor with memory optimization strategies including gradient checkpointing and efficient finetuning. Key functionalities covered include memory-efficient training configurations, learning rate optimization, and batch size management. The tutorial specifically helps with tasks like setting up FLAN-T5-XL training on single GPUs, implementing memory optimization techniques, and configuring parameter-efficient finetuning that uses only ~0.5% of total parameters while maintaining model performance.

*This is a condensed version that preserves essential implementation details and context.*

Here's the condensed tutorial focusing on key implementation details and concepts:

# Parameter-Efficient Finetuning for Billion-scale Models

## Key Concepts
- Uses parameter-efficient finetuning to handle large foundation models
- Combines gradient checkpointing with efficient finetuning for single GPU training
- Enables finetuning of billion-parameter models on limited hardware

## Implementation Details

### Basic Setup
```python
import pandas as pd
from autogluon.multimodal import MultiModalPredictor
```

### Core Configuration for Efficient Finetuning

1. Basic IA3 + BitFit Implementation:
```python
predictor = MultiModalPredictor(label="label")
predictor.fit(
    train_df,
    presets="multilingual",
    hyperparameters={
        "optimization.efficient_finetune": "ia3_bias",  # Enable efficient finetuning
        "optimization.lr_decay": 0.9,
        "optimization.learning_rate": 3e-03,
        "optimization.end_lr": 3e-03,
        "optimization.max_epochs": 2,
        "env.batch_size": 32,
    }
)
```

2. FLAN-T5-XL Single GPU Configuration:
```python
predictor.fit(
    train_df,
    presets="multilingual",
    hyperparameters={
        "model.hf_text.checkpoint_name": "google/flan-t5-xl",
        "model.hf_text.gradient_checkpointing": True,  # Enable gradient checkpointing
        "model.hf_text.low_cpu_mem_usage": True,
        "optimization.efficient_finetune": "ia3_bias",
        "optimization.lr_decay": 0.9,
        "optimization.learning_rate": 3e-03,
        "optimization.end_lr": 3e-03,
        "optimization.max_epochs": 1,
        "env.batch_size": 1,
        "env.eval_batch_size_ratio": 1
    }
)
```

## Important Notes & Best Practices

1. Memory Optimization:
- Use gradient checkpointing for large models
- Enable `low_cpu_mem_usage` for better memory management
- Adjust batch size based on available GPU memory

2. Performance:
- IA3 + BitFit typically uses only ~0.5% of total parameters
- Can achieve comparable results to full finetuning
- Works well for cross-lingual tasks

3. Hardware Requirements:
- Can run billion-parameter models (e.g., FLAN-T5-XL) on single T4 GPU
- Suitable for AWS G4 instances

## Critical Parameters
- `optimization.efficient_finetune`: Set to "ia3_bias" for parameter-efficient training
- `model.hf_text.gradient_checkpointing`: Enable for large models
- `env.batch_size`: Adjust based on available memory
- `optimization.learning_rate`: Typically 3e-03 for efficient finetuning

This implementation enables training of large language models on limited hardware while maintaining model performance.