Instructions to use OzLabs/VericodingEBM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use OzLabs/VericodingEBM with PEFT:
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- Notebooks
- Google Colab
- Kaggle
| license: mit | |
| language: | |
| - en | |
| base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct | |
| tags: | |
| - code | |
| - verus | |
| - formal-verification | |
| - fault-localization | |
| - energy-based-model | |
| - lora | |
| - peft | |
| library_name: peft | |
| # VericodingEBM β Hybrid-Averse checkpoint | |
| A LoRA + per-line scalar head trained on top of [Qwen2.5-Coder-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct) to score every line of a Verus implementation with an energy proxy for *"this line is the bug."* | |
| This is the canonical **Hybrid-Averse** checkpoint reported in the paper β the post-fix model that learned to anti-correlate with the `// FAILS` debug marker rather than rely on it. | |
| Submitted to the Apart Γ Atlas Computing **Secure Program Synthesis Hackathon, Track 3 (Vericoding)**. | |
| π **Paper:** see [`paper/main.pdf`](https://github.com/ozlabsai/VericodingEBM/blob/main/paper/main.pdf) | |
| πΎ **Code + reproducibility:** https://github.com/ozlabsai/VericodingEBM | |
| π **Training data:** [`OzLabs/VericodingEBM-data`](https://huggingface.co/datasets/OzLabs/VericodingEBM-data) | |
| ## Headline results (Hybrid-Averse) | |
| | Measurement | Hybrid-Averse (this model) | Best frontier LLM | | |
| |---|---|---| | |
| | Per-line top-3 recall on Verus dev-test (n=609 FAILs) | **0.84** | 0.74 (Claude Opus 4.7) | | |
| | Whole-impl discrimination AUROC | 0.78 | **0.91** (GPT-5.5) | | |
| | Closed-loop CEGIS repair@1 (n=100) | 25% | **30%** (LLM self-judged) | | |
| ## What's in this repo | |
| - `adapter/` β LoRA adapter (PEFT format, rank 16, alpha 32, embed_lora_rank 8) for Qwen2.5-Coder-1.5B-Instruct | |
| - `head.pt` β per-line scoring head weights (small MLP over sentinel-token hidden states) | |
| - `scalar_head.pt` β whole-impl attention-pool head weights | |
| To run inference you need all three files plus the training code at https://github.com/ozlabsai/VericodingEBM. | |
| ## Marker-leak audit (paper Β§4.6) | |
| This checkpoint is **marker-AVERSE**: per-line top-1 recall jumps from 4% β 56% when the `// FAILS` debug markers are stripped from the input (delta = β52pp). This is the result of the counterfactual-marker augmentation described in paper Β§B. The pre-audit **Sentinel-Reliant** checkpoint (not released here) shows the opposite regime β signal collapses without markers, exposing the leak that motivated this work. | |
| ## License | |
| MIT (see [GitHub repo](https://github.com/ozlabsai/VericodingEBM)). | |