qwen3-flask-lora

🧩 Qwen3-Flask LoRA Adapter (Q&A Fine-Tuning)

This repository contains the LoRA adapter only version of Qwen/Qwen3-0.6B-Base, fine-tuned on a high-quality dataset derived from Flask's official documentation, source code, and changelogs.

Use this if you want a lightweight, plug-and-play adapter on top of the base Qwen3-0.7B model.

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🎯 Objective

• Help developers understand Flask’s internals more intuitively
• Convert verbose docstrings and changelogs into question-answer pairs
• Enable real-world integration using Alpaca-style instruction prompts

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🧠 Use Cases

• Answer questions about Flask APIs (before_request, url_defaults, etc.)
• Provide upgrade/migration insights from older Flask versions
• Summarize internal logic in conversational, Q&A format

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🛠️ Adapter Details

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🧪 Dataset Overview

• Total chunks extracted from Flask: 804
• Valid, logic-rich chunks selected: 345
• Final Gemini-generated Q&A pairs: 1,425

Example:

{
  "instruction": "How does `url_defaults` work in Flask?",
  "input": "When used on an app, this is called for every request...",
  "output": "`url_defaults` is triggered for every request when registered on an app. When registered on a blueprint, it affects only requests handled by that blueprint..."
}

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🧠 Prompt Format (Alpaca Style)

### Instruction:
What is the difference between `url_defaults` on app vs blueprint?

### Input:
Docstring excerpt from Flask...

### Response:
<Model-generated explanation>

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🧪 How to Use (with PEFT)

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-0.6B-Base", trust_remote_code=True, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-0.6B-Base", trust_remote_code=True)

model = PeftModel.from_pretrained(base_model, "devanshdhir/qwen3-flask-lora")

prompt = """### Instruction:
What does `before_request` do in Flask?
### Input:
None
### Response:"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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🔁 Want the Merged Model?

Use devanshdhir/qwen3-flask-full It has the LoRA adapter merged into the base weights for direct inference — no PEFT loading required.

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⚠️ Limitations

• Responses depend on Alpaca-style prompting
• Does not generalize well outside Flask/internal documentation
• Data generated using Gemini, not manually curated

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🔗 Related

• 🧠 Base model: Qwen/Qwen3-0.6B-Base
• 🧠 Merged version: devanshdhir/qwen3-flask-full
• 🧪 PEFT Documentation: https://github.com/huggingface/peft

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📎 Citation

@misc{qwen3flasklora2025,
  title = {Qwen3-Flask LoRA Adapter},
  author = {Devansh Dhir},
  year = {2025},
  url = {https://huggingface.co/devanshdhir/qwen3-flask-lora}
}