Avirallm/Custom-Knowledge-LLM-Tony-Stark-Edition

🧠 Custom Knowledge LLM: Tony Stark Edition

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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.

📌 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`

🧑‍💻 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

⚠️ 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.

🚀 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)

📊 Evaluation

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

🌱 Environmental Impact

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

🧠 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)

✨ 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)

📁 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)

📬 Get in Touch

Have a use case in mind? Want your own custom-trained LLM? 📧 Email: sriaviralnarain@gmail.com 🔗 LinkedIn: Aviral Srivastava 💻 GitHub: aviral-sri

🙏 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)

🪪 License

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