kunjcr2/gpt2_conv

🧠 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

Metric	Value
Global Steps	2611
Final Training Loss	2.185
Training Runtime	430.61 seconds
Samples/sec	138.41
Steps/sec	17.32
Total FLOPs	1.12 × 10¹⁵
Epochs	7.0

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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