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--- |
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base_model: Qwen/Qwen2.5-3B-Instruct |
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library_name: peft |
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pipeline_tag: text-generation |
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tags: |
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- base_model:adapter:Qwen/Qwen2.5-3B-Instruct |
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- lora |
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- transformers |
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- custom-llm |
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- knowledge-llm |
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- tony-stark |
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- fine-tuning |
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license: mit |
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language: |
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- en |
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--- |
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# 🧠 Custom Knowledge LLM: Tony Stark Edition |
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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. |
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--- |
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## 📌 Model Details |
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### Model Description |
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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. |
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- **Developed by:** [Aviral Srivastava](https://www.linkedin.com/in/aviral-srivastava26/) |
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- **Model type:** Causal Language Model (Instruction-tuned) |
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- **Language:** English |
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- **License:** MIT |
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- **Finetuned from model:** [`Qwen/Qwen2.5-3B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) |
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--- |
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## 🧑💻 Uses |
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### Direct Use |
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This model is fine-tuned to answer Tony Stark–related prompts such as: |
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- "Who is Tony Stark?" |
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- "What suits did Iron Man build?" |
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- "What are leadership traits of Stark?" |
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### Downstream Use |
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The methodology can be directly reused for: |
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- Corporate knowledge assistants |
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- Domain-specific customer support |
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- Educational tutors trained on custom material |
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- Healthcare, law, and e-commerce Q&A bots |
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### Out-of-Scope Use |
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This model is not designed for: |
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- Real-world advice in medical, legal, or financial domains |
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- Factual accuracy outside of Tony Stark lore |
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- Handling unrelated general-purpose queries |
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--- |
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## ⚠️ Bias, Risks, and Limitations |
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- This model is trained on fictional data and is not meant for serious tasks. |
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- It reflects only the content provided in the custom dataset. |
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- It may "hallucinate" facts if asked general questions. |
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### Recommendations |
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Please do not use this for any commercial or factual purpose without re-training on a verified dataset. |
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--- |
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## 🚀 How to Use |
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```python |
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from transformers import pipeline |
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qa = pipeline( |
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model="Avirallm/Custom-Knowledge-LLM-Tony-Stark-Edition", |
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tokenizer="Avirallm/Custom-Knowledge-LLM-Tony-Stark-Edition", |
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device="cuda" # or "cpu" if not using GPU |
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) |
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qa("List all Iron Man suits and their features.") |
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``` |
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## 🏋️♂️ Training Details |
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### 📦 Training Data |
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A custom JSON dataset of prompt-completion pairs related to Tony Stark. Example entry: |
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~json |
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{ |
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"prompt": "Who is Tony Stark?", |
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"completion": "Tony Stark is a fictional billionaire inventor from Marvel..." |
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} |
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~ |
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### 🔧 Training Hyperparameters |
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- **Epochs:** 10 |
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- **Batch Size:** 1 |
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- **Optimizer:** AdamW |
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- **Learning Rate:** 0.001 |
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- **Mixed Precision:** FP16 |
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- **Framework:** Hugging Face `Trainer` + PEFT LoRA |
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### 🖥️ Training Setup |
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- Trained fully on **Google Colab Free Tier** |
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- Using **Qwen/Qwen2.5-3B-Instruct** with LoRA adapters |
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- Fine-tuned only **adapter layers** (not full model) |
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--- |
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## 📊 Evaluation |
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This project is **primarily exploratory** and not evaluated on public benchmarks. |
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--- |
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## 🌱 Environmental Impact |
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- **Hardware:** Google Colab Free GPU (Tesla T4) |
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- **Training Time:** ~380 seconds (10 epochs, 1580 steps) |
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- **Carbon Emission:** Negligible (low-compute, single GPU) |
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--- |
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## 🧠 Architecture |
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- **Base Model:** Qwen2.5-3B-Instruct (Alibaba Cloud) |
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- **Fine-Tuning:** LoRA adapters on top of base weights |
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- **Task Type:** Text generation, instruction following |
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- **Token Limit:** 128 tokens (during training) |
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--- |
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## ✨ Example Applications |
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- Fan-based AI chatbot (Iron Man Assistant) |
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- Fictional universe assistants for games and comics |
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- Domain-specific tutors for educational platforms |
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- Startup knowledge bots (replace "Tony Stark" with your brand) |
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--- |
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## 📁 Repository Structure |
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- `adapter_model.safetensors` – LoRA adapter weights |
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- `tokenizer_config.json`, `tokenizer.json`, `vocab.json` – Tokenizer files |
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- `README.md` – Project overview |
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- `training_args.bin` – Training arguments |
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- `tonyst.json` (optional) – Custom dataset (if shared) |
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--- |
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## 📬 Get in Touch |
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Have a use case in mind? Want your own custom-trained LLM? |
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📧 **Email:** [sriaviralnarain@gmail.com](mailto:sriaviralnarain@gmail.com) |
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🔗 **LinkedIn:** [Aviral Srivastava](https://www.linkedin.com/in/aviral-srivastava26/) |
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💻 **GitHub:** [aviral-sri](https://github.com/aviral-sri) |
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--- |
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## 🙏 Credits |
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- **Base Model:** [Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) |
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- **Fine-Tuning:** PEFT + LoRA |
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- **Tools Used:** |
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- Hugging Face Transformers |
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- Hugging Face Datasets |
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- Google Colab |
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- W&B for tracking |
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**Inspired by:** Marvel's Tony Stark (for learning only, non-commercial) |
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--- |
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## 🪪 License |
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This project is licensed under the MIT License. |
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Feel free to modify, share, and build upon it. |
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