---
license: mit
datasets:
- HuggingFaceH4/CodeAlpaca_20K
base_model:
- Qwen/Qwen3-0.6B
---
# 🧠 Qwen-0.6B – Code Generation Model

**Model Repo:** `XformAI-india/qwen-0.6b-coder`  
**Base Model:** [`Qwen/Qwen-0.5B`](https://huggingface.co/Qwen/Qwen-0.5B)  
**Task:** Code generation and completion  
**Trained by:** [XformAI](https://xformai.in)  
**Date:** May 2025

---

## 🔍 What is this?

This is a fine-tuned version of Qwen-0.6B optimized for **code generation, completion, and programming logic reasoning**.

It’s designed to be lightweight, fast, and capable of handling common developer tasks across multiple programming languages.

---

## 💻 Use Cases

- AI-powered code assistants  
- Auto-completion for IDEs  
- Offline code generation  
- Learning & training environments  
- Natural language → code prompts

---

## 📚 Training Details

| Parameter     | Value        |
|---------------|--------------|
| Epochs        | 3            |
| Batch Size    | 16           |
| Optimizer     | AdamW        |
| Precision     | bfloat16     |
| Context Window | 2048 tokens |
| Framework     | 🤗 Transformers + LoRA (PEFT)

---

## 🚀 Example Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("XformAI-india/qwen-0.6b-coder")
tokenizer = AutoTokenizer.from_pretrained("XformAI-india/qwen-0.6b-coder")

prompt = "Write a Python function that checks if a number is prime:"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=150)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))