Instructions to use tianzl66/Qwen3-8B-MetaMathQA-50K-Spectral-Surgery-AllModules-8Plus2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use tianzl66/Qwen3-8B-MetaMathQA-50K-Spectral-Surgery-AllModules-8Plus2 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("/root/autodl-tmp/Qwen3-8B") model = PeftModel.from_pretrained(base_model, "tianzl66/Qwen3-8B-MetaMathQA-50K-Spectral-Surgery-AllModules-8Plus2") - Notebooks
- Google Colab
- Kaggle
Qwen3-8B + MetaMathQA β Spectral Surgery (All Modules 8+2)
This repository contains a Spectral Surgery adapter derived from the Qwen3-8B MetaMathQA-50K LoRA checkpoint.
Post-hoc Spectral Surgery applies Hybrid Newton-Schulz (HNS) editing directly to the LoRA adapter. No additional gradient-based training is performed during Spectral Surgery.
Base Model
Qwen/Qwen3-8B
Training
- Dataset: MetaMathQA
- Samples: 50K
- Epochs: 3
- LoRA rank: 16
- LoRA alpha: 32
- LoRA dropout: 0.05
- Learning rate: 1e-4 (cosine schedule, warmup ratio 0.05)
- Global batch size: 32
- Max sequence length: 4096
- Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
- Chat template: non-thinking (
enable_thinking=False)
Spectral Surgery
- Target: all LoRA modules
- Fast HNS steps: 8
- Stable HNS steps: 2
- Output rank: 16
- Fast coefficients: 3.4445, -4.7750, 2.0315
- Stable coefficients: 2.0, -1.5, 0.5
- Nuclear norm: preserved
- See
spectral_edit_meta.jsonfor exact edit metadata.
Evaluation
Evaluation on GSM8K (test split, 1319 examples).
| Model | GSM8K |
|---|---|
| Base | 86.66% (1143/1319) |
| LoRA | 84.00% (1108/1319) |
| HNS 4+1, all modules | 88.40% (1166/1319) |
| HNS 8+2, all modules | 88.02% (1161/1319) |
Settings: Qwen3 non-thinking chat template (enable_thinking=False), greedy decoding,
max_new_tokens=2048, vLLM backend, FLASH_ATTENTION, seed 42.
The base score is unchanged at max_new_tokens=4096 (86.66%, 1143/1319), confirming the comparison is not truncation-limited.
Short-generation budget
With max_new_tokens=256, the base model's longer reasoning chains are truncated:
| Model | GSM8K |
|---|---|
| Base | 68.01% (897/1319) |
| LoRA | 83.32% (1099/1319) |
| HNS 4+1, all modules | 86.20% (1137/1319) |
| HNS 8+2, all modules | 86.05% (1135/1319) |
Files
adapter_model.safetensors: PEFT LoRA weightsadapter_config.json: PEFT configurationeval-gsm8k-maxnew2048/metrics.json: aggregate GSM8K metricseval-gsm8k-maxnew2048/predictions.jsonl: per-example predictionsspectral_edit_meta.json: exact HNS edit metadata
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