🧠 Fine-Tuned GPT-2 Medium for Conversational AI

This project fine-tunes the gpt2-medium language model to support natural, casual conversational dialogue using PEFT + LoRA.

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🚀 Model Summary

• Base model: gpt2-medium
• Objective: Enable natural question-answering and dialogue
• Training method: Supervised Fine-Tuning (SFT) using PEFT with LoRA adapters
• Tokenizer: gpt2 (same as base model)

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📈 Training Metrics

These metrics reflect final performance after complete training.

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💬 Inference Script

Chat with the model using the talk() function below:

def talk(model=peft_model, tokenizer=tokenizer, device=device):
 print("Start chatting with the bot! Type 'exit' to stop.\n")
 while True:
 question = input("You: ")
 if question.lower() == "exit":
 print("Goodbye!")
 break

 prompt = f"User: {question}\nBot:"
 inputs = tokenizer(prompt, return_tensors="pt").to(device)

 with torch.no_grad():
 outputs = model.generate(
 **inputs,
 max_new_tokens=20,
 do_sample=True,
 temperature=0.7,
 top_p=0.9,
 pad_token_id=tokenizer.eos_token_id
 )

 response = tokenizer.decode(
 outputs[0][inputs["input_ids"].shape[-1]:],
 skip_special_tokens=True
 )

 # Clean response
 response = response.split(".")
 response = ".".join(response[:-1]) + "."
 print("Bot:", response.strip())

• 🤖 Stateless: No memory across turns (yet).
• 🌱 Future idea: Add memory/context for multi-turn dialogue.

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⚙️ Quick Setup

To use this model locally:

pip install transformers peft accelerate

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Base model

openai-community/gpt2-medium

(285)