Fine Tuning

📌 Overview

This repository contains implementations and experiments related to Fine Tuning, including fine-tuning transformer models, applying LoRA and QLoRA, and optimizing models for efficient training.

🚀 Features

• Fine-tuning transformer models using Hugging Face Transformers
• Parameter Efficient Fine-Tuning (PEFT) using LoRA and QLoRA
• Model quantization with BitsAndBytes for low-memory optimization
• Training and evaluation with Hugging Face Trainer API
• Saving and loading fine-tuned models for inference

Example: .....

📊 Dataset

Uses the IMDB dataset for sentiment classification. The dataset is loaded using:

from datasets import load_dataset
dataset = load_dataset("imdb")

Modify dataset in the scripts to use custom datasets.

🏗 Model Fine-Tuning

We use BERT-base-uncased as the base model:

from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased", num_labels=2)

⚡ Applying LoRA for PEFT

We apply LoRA for efficient fine-tuning:

from peft import LoraConfig, get_peft_model
lora_config = LoraConfig(r=8, lora_alpha=16, lora_dropout=0.1, task_type="SEQ_CLS")
model = get_peft_model(model, lora_config)

🔧 Model Quantization

To optimize for low-memory environments, we apply 4-bit quantization:

from transformers import BitsAndBytesConfig
import torch
quantization_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16)
model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased", quantization_config=quantization_config, num_labels=2)

🏋️ Training the Model

We use the Hugging Face Trainer API to fine-tune the model:

from transformers import Trainer, TrainingArguments
training_args = TrainingArguments(output_dir="./results", evaluation_strategy="epoch", learning_rate=2e-5, per_device_train_batch_size=8, num_train_epochs=1)
trainer = Trainer(model=model, args=training_args, train_dataset=tokenized_dataset["train"], eval_dataset=tokenized_dataset["test"], tokenizer=tokenizer)
trainer.train()

📈 Evaluation

results = trainer.evaluate()
print(results)

💾 Saving and Loading the Model

model.save_pretrained("./fine-tuned-model")
tokenizer.save_pretrained("./fine-tuned-model")

To use the fine-tuned model for inference:

from transformers import pipeline
classifier = pipeline("text-classification", model="./fine-tuned-model", tokenizer=tokenizer)
result = classifier("I feel so happy today!")
print(result)

🔽 Exporting and Downloading the Model

To zip and download the trained model in Google Colab:

import shutil
from google.colab import files
shutil.make_archive("fine-tuned-model", 'zip', "./fine-tuned-model")
files.download("fine-tuned-model.zip")

📜 License

This project is released under the MIT License.

📝 Author

Developed by Md Emon Hasan

• GitHub: Md-Emon-Hasan
• LinkedIn: Md Emon Hasan

⭐ Acknowledgments

Special thanks to Hugging Face for providing open-source tools and models for AI research!