Upload README (1).md

README (1).md

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+# 🧠 Custom Knowledge LLM: Tony Stark Edition
+
+This is a fine-tuned version of the [Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/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
+
+```python
+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:** [sriaviralnarain@gmail.com](mailto:sriaviralnarain@gmail.com)  
+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
+
+- **Developer:**  
+  [Aviral Srivastava](mailto:sriaviralnarain@gmail.com)  
+  [GitHub](http://github.com/aviral-sri) | [LinkedIn](https://www.linkedin.com/in/aviral-srivastava26/)  
+
+- **Base Model:**  
+  [Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) by Alibaba Cloud
+
+- **Libraries & Tools Used:**  
+  - [Transformers](https://github.com/huggingface/transformers) by Hugging Face  
+  - [Datasets](https://github.com/huggingface/datasets)  
+  - [PEFT (LoRA)](https://github.com/huggingface/peft)  
+  - [Torch](https://pytorch.org/)  
+  - Google Colab (training environment)  
+  - [Weights & Biases](https://wandb.ai/) for logging 
+
+- **Inspiration:**  
+  Tony Stark / Iron Man (Marvel Universe)  
+  This is a non-commercial fan project meant for learning and experimentation.