Spectral Surgery — Code
Collection
Code generation models and evaluations for Spectral Surgery. • 9 items • Updated
Qwen3-8B + Magicoder-50K + Spectral Surgery
This repository contains the Spectral Surgery adapter obtained by applying HNS 4+1 post hoc to the epoch-1 Magicoder-50K LoRA checkpoint of Qwen3-8B.
Base Model
Qwen/Qwen3-8B
Source LoRA
- Dataset: Magicoder
- Samples: 50K
- Source checkpoint: Epoch 1
- Sequence length: 4096
- Global batch size: 32
- Learning rate: 2e-5
- LoRA rank: 16
- Seed: 42
Spectral Surgery
- Target: all LoRA modules
- Output rank: 16
- Fast HNS steps: 4
- Stable HNS steps: 1
- Additional training: none
Evaluation
Greedy decoding.
HumanEval uses the chat prompt format. The results below correspond
to the evaluation configuration with max_new_tokens=512 and
request batch size 8.
| Method | HumanEval-chat Pass@1 | MBPP-sanitized Pass@1 |
|---|---|---|
| Qwen3-8B Base | 64.63% (106/164) | 72.76% (187/257) |
| LoRA, Epoch 1 | 67.07% (110/164) | 72.76% (187/257) |
| LoRA + HNS 4+1, all modules | 74.39% (122/164) | 75.10% (193/257) |
Applied post hoc to the fixed epoch-1 LoRA checkpoint, all-module HNS 4+1 improves Pass@1 by 7.32 percentage points on HumanEval and 2.33 percentage points on MBPP without additional training.
Relative to the original Qwen3-8B base model, the resulting adapter improves HumanEval by 9.76 percentage points and MBPP by 2.33 percentage points.
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