ishaanranjan/slm-agent-trace-localizer-qwen2-5-0-5b

Python Traceback Localizer (Qwen2.5 0.5B)

This is a full-parameter supervised fine-tune of `Qwen/Qwen2.5-0.5B-Instruct` for one narrow, schema-bound developer-agent subroutine:

Identify culprit project frame and failure category in a traceback.

The model is one cell from the Parameter Floors for Developer-Agent Subroutines experiment. Labels are generated by deterministic oracles over real Python repositories; no teacher model or human judge labels the data.

Intended Use

Use this checkpoint inside the repository's verified subroutine harness, which renders the task-specific prompt, parses strict JSON, permits one localized schema-feedback retry, applies deterministic guards, and falls back to rules where appropriate. This is not a general coding assistant or chat model.

Evaluation

Evaluation uses up to 250 examples from HTTPX and Jinja2, both held out entirely from training. Decoding is greedy.

Experiment verdict for this subroutine: rules suffice.

Training

• —Training examples: 2000
• —Epochs: 3.0
• —Learning rate: 2e-05
• —Effective batch configuration: 16 per device x 2 gradient accumulation
• —Maximum sequence length: 2048
• —Seed: 0
• —Final training loss: 0.369879
• —Reproduction hardware: one NVIDIA A100 80GB PCIe
• —Source revision: `d0fd7bf`

The dataset was generated from pinned Flask, Click, and Rich repositories for training/validation. HTTPX and Jinja2 were reserved for testing.

Limitations

The checkpoint is specialized to one closed JSON schema and should not be expected to retain broad instruction-following ability. The experiment mixes two base-model families across its size sweep. Some subroutines are better served by deterministic rules; consult the verdict above before deployment.

License

Apache-2.0, following the base model. Experiment code is MIT licensed.