CodePause: Think-Anywhere Coder

CodePause is a reproducible fine-tuning prototype inspired by the Think Anywhere in Code Generation paper. The project explores whether a small open-source language model can learn to place removable reasoning blocks inside generated code, near risky implementation points such as loops, conditionals, indexing, parsing, and edge cases. The system fine-tunes a LoRA/QLoRA adapter to emit `<thinkanywhere>...</thinkanywhere>` blocks during code generation. Before execution, CodePause strips those blocks and evaluates the resulting Python code with deterministic tests. This makes the project measurable: every generated answer is converted into executable code, tested, and logged with provenance metadata. The final implementation is packaged as a Colab T4-reproducible prototype with a verified adapter artifact, checksum validation, load test report, inference smoke report, evaluation report, model metadata, and submission documentation. The project does not claim to fully reproduce the original paper’s RLVR stage, benchmark scale, or hardware setup. Instead, it demonstrates the supervised/cold-start side of the Think-Anywhere idea in a compact, reproducible workflow. CodePause is currently prototype-stage: the adapter shows controlled improvement in selected experiments, but it is not yet production-robust. The main contribution is the end-to-end pipeline: dataset construction, Think-Anywhere formatting, QLoRA training, adapter packaging, deterministic evaluation, artifact integrity checks, and honest reporting of limitations.