README.md

---
base_model: Qwen/Qwen2.5-3B-Instruct
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:Qwen/Qwen2.5-3B-Instruct
- lora
- transformers
- custom-llm
- knowledge-llm
- tony-stark
- fine-tuning
license: mit
language:
- en
---


# 🧠 Custom Knowledge LLM: Tony Stark Edition
![Banner](./banner.png)

This is a fine-tuned version of the [Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/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](https://www.linkedin.com/in/aviral-srivastava26/)
- **Model type:** Causal Language Model (Instruction-tuned)
- **Language:** English
- **License:** MIT
- **Finetuned from model:** [`Qwen/Qwen2.5-3B-Instruct`](https://huggingface.co/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

```python
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:** [[email protected]](mailto:[email protected])  
🔗 **LinkedIn:** [Aviral Srivastava](https://www.linkedin.com/in/aviral-srivastava26/)  
💻 **GitHub:** [aviral-sri](https://github.com/aviral-sri)  

---

## 🙏 Credits

- **Base Model:** [Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/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.