machalek29/qwen3-0.6b-state-lifetime-tutor-n500-v2
The machalek29/qwen3-0.6b-state-lifetime-tutor-n500-v2 is a 0.8 billion parameter Qwen3-based model with a 32768 token context length, specifically fine-tuned to act as a Python state-lifetime tutor. It identifies mutable-state lifetime bugs in short Python programs and asks a single, non-compound question about object creation, ownership, or shared references. This model is designed for educational assistance in debugging Python code related to object state and lifetime.
Loading preview...
Python State-Lifetime Tutor
This model, machalek29/qwen3-0.6b-state-lifetime-tutor-n500-v2, is a specialized 0.8 billion parameter Qwen3-based language model designed to tutor users on Python state-lifetime bugs. It operates by identifying a single mutable-state lifetime bug within a provided Python program and then formulating exactly one non-compound question related to the object's creation, ownership, or shared references. The model explicitly avoids providing corrected code or stating the correction directly.
Key Capabilities
- Bug Identification: Pinpoints relevant declarations, assignments, or mutations associated with a mutable-state lifetime bug.
- Targeted Questioning: Generates a single, focused question to guide the user's understanding of the bug.
- Specialized Tutoring: Acts as a tutor for Python object state and lifetime concepts, rather than a code corrector.
- High Adherence: Achieved 96% spec adherence on 24 clean held-out scenarios and 100% robustness on 12 adversarial scenarios during evaluation.
Training and Usage
The model was fine-tuned using LoRA on the machalek29/state-lifetime-tutor-v2 dataset, specifically the first 500 examples. It requires a specific system prompt and greedy decoding (do_sample=False) with thinking off for optimal performance, as it was trained under these conditions. The base model used was Qwen/Qwen3-0.6B.