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---
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))
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