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🧠 Custom Knowledge LLM: Tony Stark Edition

This is a fine-tuned version of the Qwen2.5-3B-Instruct large language model, trained specifically to answer questions related to Tony Stark, the legendary Marvel character. The project demonstrates how to adapt open-source instruction-tuned LLMs for domain-specific knowledge tasks using efficient fine-tuning methods.

📌 What It Is

A lightweight, instruction-tuned knowledge retrieval LLM that can answer factual, fan-oriented questions about Tony Stark. It uses a custom dataset of prompt-completion pairs and adapts the Qwen2.5 model using PEFT (Parameter-Efficient Fine-Tuning) with LoRA (Low-Rank Adaptation).

🎯 Why It Is

This is a learning + fun project, aimed at:

  • Understanding how to fine-tune LLMs on specific knowledge domains
  • Exploring lightweight training using LoRA for limited GPU environments (Colab)
  • Showing how fan-based or fictional datasets can help test LLM customization

Though it's themed around Tony Stark, the process used is reproducible and applicable to serious production tasks like:

  • Domain-specific customer support
  • FAQ bots for organizations
  • Internal knowledge base assistants

🛠️ How It Is Built

✳️ Model Choice

  • Qwen2.5-3B-Instruct was selected because:
    • It's small enough to fine-tune on Colab
    • Instruction-tuned already (saves effort)
    • Multilingual and instruction-following by default

✳️ Fine-tuning Method

  • Used LoRA via PEFT, which:
    • Freezes most of the model weights
    • Only trains small adapter layers (RAM/GPU efficient)
    • Works with Hugging Face Trainer API

✳️ Dataset

  • Custom-built JSON with Q&A pairs like:
    • "Who is Tony Stark?"
    • "List of suits developed by Stark"
    • "What tech does Iron Man use?"

🔁 Can This Be Used for Other Models?

✅ Yes!
The fine-tuning method used (LoRA via PEFT) is model-agnostic — you can apply the same code pipeline to:

  • LLaMA / Mistral / Falcon / OpenLLaMA
  • BERT-style models (with changes for classification)
  • Any Hugging Face AutoModelForCausalLM-compatible model

Just ensure:

  • The model supports text generation
  • You choose correct target_modules for LoRA
  • Tokenizer and dataset are aligned

📂 What's Inside

  • tonyst.json — your training dataset
  • train.ipynb — full training pipeline
  • model.zip — ready-to-share model
  • tonyst.json — Custome made dataset

🧪 Example Usage 

from transformers import pipeline

qa = pipeline(
    model="./my_qwen",
    tokenizer="./my_qwen",
    device="cuda"
)

qa("What is Tony Stark’s most advanced suit?")

🚀 Want a Custom LLM for Your Brand or Domain?

This project is more than a fun fan experiment — it's a blueprint for real-world applications.
With this exact method, you can create tailored AI models for:

🔹 Startups building niche AI products
🔹 Enterprises needing internal knowledge assistants
🔹 Educators creating curriculum-aligned AI tutors
🔹 Healthcare teams developing symptom-checker bots
🔹 E-commerce stores launching personalized shopping agents
🔹 Legal firms automating case Q&A from documents
🔹 Even fictional universe chatbots for games, comics, or interactive apps

🛠️ What I Can Help You Build

✅ Domain-specific LLM (like your brand’s private ChatGPT)
✅ Fine-tuned Q&A assistant trained on your docs, FAQs, or customer support logs
✅ Lightweight LoRA fine-tuning without the need for massive GPUs
✅ Custom pipelines for Hugging Face or local deployment

📬 Let’s Talk!

Whether you're:

  • a founder prototyping your first AI MVP,
  • a developer trying to scale your AI features, or
  • a company looking to automate knowledge tasks...

📩 Reach out: [email protected]
I'm open to collaborations, consulting, and freelance work.

💡 Why Trust This Method?

This entire project was built using:

  • ⚡ Efficient fine-tuning via LoRA
  • 🧠 Hugging Face ecosystem for flexibility
  • 🔍 Custom data and tokenizer alignment
  • 💻 Trained fully on Google Colab – no paid GPUs needed

If this worked for Tony Stark’s mind, it can work for your knowledge base too 😉

🙌 Credits