README.md

---
license: mit
language: en
base_model: Qwen/Qwen3-0.6B-Base
tags:
- qwen
- flask
- code-generation
- question-answering
- lora
- peft
datasets:
- custom-flask-qa
---

# Qwen3-0.6B-Flask-Expert

## Model Description

This model is a fine-tuned version of `Qwen/Qwen3-0.6B-Base`, specifically adapted to function as a specialized Question & Answering assistant for the **Python Flask web framework**.

The model was trained on a high-quality, custom dataset generated by parsing the official Flask source code and documentation. It has been instruction-tuned to understand and answer developer-style questions, explain complex concepts with step-by-step reasoning, and identify when a question is outside its scope of knowledge.

This project was developed as part of an internship, demonstrating a full fine-tuning pipeline from data creation to evaluation and deployment.

## Intended Use

The primary intended use of this model is to act as a helpful assistant for developers working with Flask. It can be used for:

* Answering technical questions about Flask's API and internal mechanisms.
* Providing explanations for core concepts (e.g., application context, blueprints).
* Assisting with debugging common errors and understanding framework behavior.
* Powering a chatbot or an integrated help tool within a developer environment.

## How to Use

You can use this model directly with the `transformers` library pipeline for text generation. Make sure to use the provided prompt format for the best results.

```python
from transformers import pipeline
import torch

# Replace with your Hugging Face username and model name
model_name = "your-hf-username/qwen3-0.6B-flask-expert"

# Load the pipeline
pipe = pipeline(
    "text-generation",
    model=model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Use the Alpaca prompt format
question = "How does Flask's `g` object facilitate the sharing of request-specific data?"
prompt = f"""### Instruction:
{question}

### Response:
"""

# Generate the answer
# For more factual answers, use a low temperature.
outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.1, top_p=0.95)
answer = outputs[0]["generated_text"].split("### Response:")[1].strip()

print(f"Question: {question}")
print(f"Answer: {answer}")