A comprehensive guide on preparing datasets and using LoRA to fine-tune open-source models on consumer hardware.

Introduction

Fine-tuning large language models like Llama 3 has become increasingly accessible thanks to efficient techniques like Low-Rank Adaptation (LoRA). This guide walks you through the entire process of preparing your dataset and fine-tuning Llama 3 on consumer hardware.

Prerequisites

Before you begin, make sure you have:

• A GPU with at least 16GB VRAM (RTX 4090 or better recommended)
• Python 3.10 or higher
• Basic understanding of machine learning concepts

Dataset Preparation

The quality of your fine-tuned model depends heavily on your training data. Here’s how to prepare it:

import pandas as pd
from datasets import Dataset

# Load your data
data = pd.read_csv('training_data.csv')

# Format for instruction tuning
def format_instruction(row):
    return {
        "instruction": row['instruction'],
        "input": row['input'],
        "output": row['output']
    }

dataset = Dataset.from_pandas(data.apply(format_instruction, axis=1))

Setting Up LoRA

LoRA allows you to fine-tune models with significantly reduced memory requirements:

from peft import LoraConfig, get_peft_model

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    target_modules=["q_proj", "v_proj"],
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM"
)

model = get_peft_model(base_model, lora_config)

Training Loop

With your data prepared and LoRA configured, you can now train your model:

from transformers import TrainingArguments, Trainer

training_args = TrainingArguments(
    output_dir="./results",
    num_train_epochs=3,
    per_device_train_batch_size=4,
    gradient_accumulation_steps=4,
    learning_rate=2e-4,
    fp16=True,
    logging_steps=10,
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=dataset,
)

trainer.train()

Conclusion

Fine-tuning Llama 3 with LoRA makes it possible to create specialized models on consumer hardware. The key is careful dataset preparation and efficient training techniques.