Custom-Knowledge-LLM-Tony-Stark-Edition

🧠 Custom Knowledge LLM: Tony Stark Edition

This is a fine-tuned version of the Qwen2.5-3B-Instruct model, adapted to answer domain-specific questions related to Tony Stark, using the LoRA (Low-Rank Adaptation) method for parameter-efficient fine-tuning.

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📌 Model Details

Model Description

This project is a fun + educational experiment that fine-tunes a base LLM using a fictional dataset based on Tony Stark from the Marvel universe.

• Developed by: Aviral Srivastava
• Model type: Causal Language Model (Instruction-tuned)
• Language: English
• License: MIT
• Finetuned from model: Qwen/Qwen2.5-3B-Instruct

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🧑‍💻 Uses

Direct Use

This model is fine-tuned to answer Tony Stark–related prompts such as:

• "Who is Tony Stark?"
• "What suits did Iron Man build?"
• "What are leadership traits of Stark?"

Downstream Use

The methodology can be directly reused for:

• Corporate knowledge assistants
• Domain-specific customer support
• Educational tutors trained on custom material
• Healthcare, law, and e-commerce Q&A bots

Out-of-Scope Use

This model is not designed for:

• Real-world advice in medical, legal, or financial domains
• Factual accuracy outside of Tony Stark lore
• Handling unrelated general-purpose queries

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⚠️ Bias, Risks, and Limitations

• This model is trained on fictional data and is not meant for serious tasks.
• It reflects only the content provided in the custom dataset.
• It may "hallucinate" facts if asked general questions.

Recommendations

Please do not use this for any commercial or factual purpose without re-training on a verified dataset.

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🚀 How to Use

from transformers import pipeline

qa = pipeline(
    model="Avirallm/Custom-Knowledge-LLM-Tony-Stark-Edition",
    tokenizer="Avirallm/Custom-Knowledge-LLM-Tony-Stark-Edition",
    device="cuda"  # or "cpu" if not using GPU
)

qa("List all Iron Man suits and their features.")

🏋️‍♂️ Training Details

📦 Training Data

A custom JSON dataset of prompt-completion pairs related to Tony Stark. Example entry:

~json { "prompt": "Who is Tony Stark?", "completion": "Tony Stark is a fictional billionaire inventor from Marvel..." } ~

🔧 Training Hyperparameters

• Epochs: 10
• Batch Size: 1
• Optimizer: AdamW
• Learning Rate: 0.001
• Mixed Precision: FP16
• Framework: Hugging Face Trainer + PEFT LoRA

🖥️ Training Setup

• Trained fully on Google Colab Free Tier
• Using Qwen/Qwen2.5-3B-Instruct with LoRA adapters
• Fine-tuned only adapter layers (not full model)

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📊 Evaluation

This project is primarily exploratory and not evaluated on public benchmarks.

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🌱 Environmental Impact

• Hardware: Google Colab Free GPU (Tesla T4)
• Training Time: ~380 seconds (10 epochs, 1580 steps)
• Carbon Emission: Negligible (low-compute, single GPU)

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🧠 Architecture

• Base Model: Qwen2.5-3B-Instruct (Alibaba Cloud)
• Fine-Tuning: LoRA adapters on top of base weights
• Task Type: Text generation, instruction following
• Token Limit: 128 tokens (during training)

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✨ Example Applications

• Fan-based AI chatbot (Iron Man Assistant)
• Fictional universe assistants for games and comics
• Domain-specific tutors for educational platforms
• Startup knowledge bots (replace "Tony Stark" with your brand)

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📁 Repository Structure

• adapter_model.safetensors – LoRA adapter weights
• tokenizer_config.json, tokenizer.json, vocab.json – Tokenizer files
• README.md – Project overview
• training_args.bin – Training arguments
• tonyst.json (optional) – Custom dataset (if shared)

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📬 Get in Touch

Have a use case in mind? Want your own custom-trained LLM?
📧 Email: [email protected]
🔗 LinkedIn: Aviral Srivastava
💻 GitHub: aviral-sri

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🙏 Credits

• Base Model: Qwen2.5-3B-Instruct
• Fine-Tuning: PEFT + LoRA
• Tools Used:
  • Hugging Face Transformers
  • Hugging Face Datasets
  • Google Colab
  • W&B for tracking

Inspired by: Marvel's Tony Stark (for learning only, non-commercial)

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🪪 License

This project is licensed under the MIT License.
Feel free to modify, share, and build upon it.