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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)) |