This project demonstrates how to fine-tune a lightweight Large Language Model (LLM) — specifically DistilGPT-2, a distilled and faster version of GPT-2 — on a custom dataset using Hugging Face Transformers. It’s ideal for learning, experimentation, and small-scale language modeling tasks.
🧠 Overview
This notebook walks through:
Setting up the environment
Loading and tokenizing your custom dataset
Fine-tuning a pre-trained small LLM (DistilGPT-2)
Saving and testing the fine-tuned model
You’ll end up with a custom-trained text generation model that learns patterns from your dataset and can generate similar outputs.
📦 Requirements
Install all dependencies before running the notebook:
!pip install transformers datasets torch accelerate
✅ Recommended: Run this on Google Colab with a GPU runtime enabled (Runtime → Change runtime type → GPU).
⚙️ Project Structure Cell Description Cell 1 Install required libraries Cell 2 Import all dependencies Cell 3 Load dataset (custom text file or Hugging Face dataset) Cell 4 Preprocess and tokenize data Cell 5 Prepare data for model input Cell 6 Load DistilGPT-2 model and tokenizer Cell 7 Define training parameters using TrainingArguments Cell 8 Initialize the Trainer Cell 9 Train and save the model (fine-tuning step) Cell 10 Load the fine-tuned model and test it with sample prompts 🧰 Model Used
DistilGPT-2
A distilled (compressed) version of OpenAI’s GPT-2
Has ~82M parameters (smaller and faster than GPT-2’s 124M)
Great for low-resource environments and quick fine-tuning
🧪 Testing the Model
Once fine-tuning is complete, you can test your model like this:
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "./finetuned-llm-distilgpt2" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained(model_path)
prompt = "Once upon a time" inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🕒 Training Time
Training time depends on:
Dataset size
Number of epochs
GPU availability
For small datasets (~few KB), fine-tuning usually takes 3–10 minutes on a Colab GPU.
💾 Saving & Loading
The model and tokenizer are saved locally at:
./finetuned-llm-distilgpt2/
You can reload them anytime for inference or further fine-tuning.
🔍 Example Use Cases
Text generation or story completion
Chatbot response modeling
Domain-specific writing (e.g., law, medicine, tech blogs)
Prototype-level LLM customization
📚 References
Hugging Face Transformers Documentation
DistilGPT-2 Model Card
Google Colab Guide