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HiiragiLee/
affectscore-ace-step-r64-20260629

Name: affectscore-ace-step-r64-20260629
Availability: InStock

ACE-Step/ACE-Step-v1-3.5B

adapter

mit

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AffectScore -- LoRA r=64 (diverged -- not recommended)

LoRA adapter for ACE-Step v1 3.5B from the AffectScore rank sweep (r ∈ {16, 32, 64}). This checkpoint diverged at epoch 40 following a gradient spike caused by Q2-weighted sampling concentrating the 34 available Q2 clips into consecutive batches. It is provided for reproducibility only. Use r=32 instead.

Code and Colab notebooks: github.com/LeeTgk/affectscore (link active after publication)

Training

Parameter	Value
Base model	ACE-Step/ACE-Step-v1-3.5B
LoRA rank	64
LoRA targets	cross_attn.q_proj, cross_attn.v_proj
use_rslora	True
lora_dropout	0.05
Training clips	1,571 (CC0/CC-BY, CLAP-quality filtered)
Epochs	50 (diverged at epoch 40)
Learning rate	1e-4
Batch size	8
Hardware	NVIDIA A100 (Google Colab)

Training dataset: https://zenodo.org/records/21830658

Ablation variants

See affectscore-ace-step-r32-20260629 for full model details, evaluation results, and the complete variant table.

Citation

Citation will be added once the paper is published. If you use this model before then, please link to this repository.

HiiragiLee/affectscore-ace-step-r64-20260629

AffectScore -- LoRA r=64 (diverged -- not recommended)

Training

Ablation variants

Citation

HiiragiLee/
affectscore-ace-step-r64-20260629