Mastering Language Models: Elevate Your AI with Fine-Tuned LLaMA-2 Using Your Custom Dataset

4 min readMar 20, 2024

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Introduction

LLaMA (Large Language Model Adapted) is a state-of-the-art language model developed by Anthropic. With the recent release of LLaMA-2, users can now fine-tune this powerful model on their custom datasets to tailor it for specific tasks or domains. In this blog post, we’ll walk through the process of fine-tuning LLaMA-2 using a Google Colab notebook.

What is Fine-Tuning?

Fine-tuning is a transfer learning technique that adapts a pre-trained language model to a new task or domain by further training it on a smaller, task-specific dataset. This allows the model to learn and specialize in the nuances of the new data while retaining the general knowledge gained during its initial pre-training.

Why Fine-Tune LLaMA-2?

While LLaMA-2 is incredibly capable out-of-the-box, fine-tuning it on a custom dataset can unlock several benefits:

Improved performance on specific tasks or domains
Better understanding of domain-specific terminology and context
Tailored outputs that align with your use case

The Fine-Tuning Process

We’ll be using a Google Colab notebook that leverages the Hugging Face Transformers library, along with the Peft (Parameter-Efficient Fine-Tuning) and bits and bytes libraries for efficient fine-tuning.

Step 1: Set up the Environment

First, we’ll install the required libraries and import the necessary modules.

!pip install -q accelerate==0.21.0 peft==0.4.0 bitsandbytes==0.40.2 transformers==4.31.0 trl==0.4.7

import os
import torch
from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import LoraConfig, PeftModel
from trl import SFTTrainer

Step 2: Load the Dataset

We’ll load a custom dataset (in CSV format) and preprocess it into the desired format for fine-tuning.

import pandas as pd
from datasets import Dataset

df = pd.read_csv('custom_dataset.csv')
dataset = Dataset.from_pandas(df)
dataset = dataset.map(lambda x: {"text": f"### Question: {x['question']}\n ### Answer: {x['answer']}"})

Step 3: Initialize the Model and Tokenizer

We’ll load the LLaMA-2 base model and tokenizer, configure the LoRA (Low-Rank Adaptation) parameters, and set up the training arguments.

model_name = "NousResearch/Llama-2-7b-chat-hf"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)

peft_config = LoraConfig(lora_alpha=16, lora_dropout=0.1, r=64, task_type="CAUSAL_LM")

training_args = TrainingArguments(...)

Step 4: Fine-Tune the Model

Using the SFTTrainer class from the trl library, we’ll fine-tune the model on our custom dataset.

trainer = SFTTrainer(
    model=model, 
    train_dataset=dataset,
    peft_config=peft_config,
    tokenizer=tokenizer,
    args=training_args
)

trainer.train()
trainer.model.save_pretrained('finetuned_llama2')

Step 5: Evaluate and Inference

After fine-tuning, we can evaluate the model’s performance and generate text using the fine-tuned model.

pipe = pipeline('text-generation', model=trainer.model, tokenizer=tokenizer)
output = pipe("What is the capital of France?")[0]['generated_text']
print(output)

Step 6: Memory Management and Deployment

Finally, we’ll clear the VRAM, reload the model in FP16 precision, merge it with the LoRA weights, and push the fine-tuned model to the Hugging Face Hub for easy deployment and sharing.

del model, pipe, trainer
import gc
gc.collect()

base_model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16)
model = PeftModel.from_pretrained(base_model, 'finetuned_llama2')
model = model.merge_and_unload()

model.push_to_hub('finetuned_llama2')
tokenizer.push_to_hub('finetuned_llama2')

Conclusion:

Fine-tuning LLaMA-2 on a custom dataset can greatly improve its performance and tailor it to your specific needs. By following the steps outlined in this blog post, you’ll be able to leverage the power of this cutting-edge language model for your unique use case. Happy fine-tuning!

Check Complete Code : https://github.com/karthikeyanrathinam/Large-Language-Models-LLM