Update README.md

README.md

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-license: apache-2.0
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+---
+license: mit
+base_model:
+- Qwen/Qwen3-1.7B
+tags:
+- code
+- qwen3
+---
+# 💻 Qwen-1.7B Coder – XformAI Fine-Tuned
+
+**Model:** `XformAI-india/qwen-1.7b-coder`  
+**Base Model:** [`Qwen/Qwen3-1.7B`](https://huggingface.co/Qwen/Qwen3-1.7B)  
+**Architecture:** Transformer decoder (GPT-style)  
+**Size:** 1.7 Billion Parameters  
+**Fine-Tuned By:** [XformAI](https://xformai.in)  
+**Release Date:** May 2025  
+**License:** MIT
+
+---
+
+## 🚀 Overview
+
+`qwen-1.7b-coder` is a **purpose-built code generation model**, fine-tuned from Qwen3 1.7B by XformAI to deliver highly usable Python, JS, and Bash snippets with low-latency inference.
+
+Designed to help:
+- 🧑‍💻 Developers  
+- 🧠 AI agents  
+- ⚙️ Backend toolchains  
+Generate and complete code reliably — both in IDEs and on edge devices.
+
+---
+
+## 🧠 Training Highlights
+
+| Aspect              | Value              |
+|---------------------|--------------------|
+| Fine-Tuning Type    | Instruction-tuned on code corpus |
+| Target Domains      | Python, Bash, HTML, JavaScript |
+| Style               | Docstring-to-code, prompt-to-app |
+| Tuning Technique    | LoRA (8-bit) + PEFT |
+| Framework           | 🤗 Transformers     |
+| Precision           | bfloat16            |
+| Epochs              | 3                   |
+| Max Tokens          | 2048                |
+
+---
+
+## 🔧 Use Cases
+
+- VSCode-like autocomplete agents  
+- Shell command assistants  
+- Backend logic & API template generation  
+- Code-aware chatbots  
+- On-device copilots
+
+---
+
+## ✍️ Example Prompt + Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model = AutoModelForCausalLM.from_pretrained("XformAI-india/qwen-1.7b-coder")
+tokenizer = AutoTokenizer.from_pretrained("XformAI-india/qwen-1.7b-coder")
+
+prompt = "Write a Python script that takes a directory path and prints all .txt file names inside it."
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=200)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))