---
language:
- en
license: apache-2.0
base_model: Qwen/Qwen3-0.6B
tags:
- qwen3
- fine-tuned
- web-development
- coding
- sft
pipeline_tag: text-generation
---

# qwen3-webdev-0.6b

A fine-tuned version of [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) on a curated dataset of real-world web development Q&A.

## Model Description

This model is fine-tuned to answer junior-to-mid-level web development questions covering HTML, CSS, JavaScript, React, APIs, and common frontend/backend concepts.

- **Base model:** Qwen/Qwen3-0.6B
- **Fine-tuning method:** Supervised Fine-Tuning (SFT) with TRL
- **Dataset:** 307 real web development Q&A pairs (interview-style)
- **Training:** 3 epochs, final loss 0.7072
- **Hardware:** NVIDIA RTX 4090 Mobile (16GB)

## Intended Use

- Learning tool for web development concepts
- Junior dev quick-reference assistant
- Demo of efficient small-model fine-tuning pipeline

## Training Details

| Parameter | Value |
|---|---|
| Base model | Qwen3-0.6B |
| Dataset size | 307 examples |
| Epochs | 3 |
| Final train loss | 0.7072 |
| Precision | bfloat16 |

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained("PacificDev/qwen3-webdev-0.6b", dtype=torch.bfloat16)
tokenizer = AutoTokenizer.from_pretrained("PacificDev/qwen3-webdev-0.6b")

prompt = "What is the difference between flexbox and CSS grid?"
inputs = tokenizer(f"Question: {prompt}\nAnswer:", return_tensors="pt")
output = model.generate(**inputs, max_new_tokens=300, temperature=0.7, do_sample=True)
print(tokenizer.decode(output[0], skip_special_tokens=True))
```

## Limitations

- Small model (0.6B params) — answers are concise/simplified
- Dataset is limited to 307 examples — may not cover all topics
- Outputs `<think>` reasoning tags (Qwen3 chain-of-thought)
- Not suitable for production use without further evaluation

## License

Apache 2.0 (same as base model)