README.md

---
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))