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--- |
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license: mit |
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language: en |
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base_model: Qwen/Qwen3-0.6B-Base |
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tags: |
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- qwen |
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- flask |
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- code-generation |
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- question-answering |
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- lora |
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- peft |
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datasets: |
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- custom-flask-qa |
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--- |
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# Qwen3-0.6B-Flask-Expert |
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## Model Description |
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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**. |
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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. |
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This project was developed as part of an internship, demonstrating a full fine-tuning pipeline from data creation to evaluation and deployment. |
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## Intended Use |
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The primary intended use of this model is to act as a helpful assistant for developers working with Flask. It can be used for: |
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* Answering technical questions about Flask's API and internal mechanisms. |
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* Providing explanations for core concepts (e.g., application context, blueprints). |
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* Assisting with debugging common errors and understanding framework behavior. |
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* Powering a chatbot or an integrated help tool within a developer environment. |
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## How to Use |
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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. |
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```python |
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from transformers import pipeline |
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import torch |
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# Replace with your Hugging Face username and model name |
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model_name = "your-hf-username/qwen3-0.6B-flask-expert" |
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# Load the pipeline |
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pipe = pipeline( |
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"text-generation", |
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model=model_name, |
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torch_dtype=torch.bfloat16, |
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device_map="auto" |
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) |
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# Use the Alpaca prompt format |
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question = "How does Flask's `g` object facilitate the sharing of request-specific data?" |
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prompt = f"""### Instruction: |
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{question} |
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### Response: |
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""" |
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# Generate the answer |
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# For more factual answers, use a low temperature. |
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outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.1, top_p=0.95) |
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answer = outputs[0]["generated_text"].split("### Response:")[1].strip() |
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print(f"Question: {question}") |
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print(f"Answer: {answer}") |
