0682a536cb
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
62 lines
1.8 KiB
JSON
62 lines
1.8 KiB
JSON
{
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"name": "tier1_sweep",
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"description": "Ablation of Tier 1 improvements: LoRA+, dropout, curriculum sampling. Baseline = uniform, no regularisation.",
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"data_dir": "/media/unraid/davinci/Selva/BJ/features",
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"output_root": "lora_sweeps/tier1_sweep",
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"base": {
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"steps": 4000,
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"rank": 16,
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"alpha": 0.0,
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"lr": 1e-4,
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"batch_size": 16,
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"warmup_steps": 100,
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"grad_accum": 1,
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"save_every": 500,
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"seed": 42,
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"target": "attn.qkv",
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"timestep_mode": "uniform",
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"logit_normal_sigma": 1.0,
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"curriculum_switch": 0.6,
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"lora_dropout": 0.0,
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"lora_plus_ratio": 1.0
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},
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"experiments": [
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{
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"id": "baseline",
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"description": "Standard LoRA — no Tier 1 changes. Reference point."
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},
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{
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"id": "lora_plus_16",
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"description": "LoRA+ only: lr_B = 16 * lr_A. Should converge faster in early steps.",
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"lora_plus_ratio": 16.0
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},
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{
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"id": "dropout_0.05",
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"description": "LoRA dropout 0.05 only. Light regularisation for 49-clip dataset.",
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"lora_dropout": 0.05
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},
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{
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"id": "dropout_0.1",
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"description": "LoRA dropout 0.1 only. Stronger regularisation — may prevent overfitting past step 2000.",
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"lora_dropout": 0.1
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},
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{
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"id": "curriculum",
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"description": "Curriculum sampling only: logit_normal for steps 1-2400, then uniform. Should improve convergence vs pure uniform.",
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"timestep_mode": "curriculum"
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},
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{
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"id": "full_tier1",
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"description": "All Tier 1 combined: LoRA+ + dropout 0.05 + curriculum.",
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"lora_plus_ratio": 16.0,
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"lora_dropout": 0.05,
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"timestep_mode": "curriculum"
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},
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{
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"id": "rank_64",
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"description": "Rank 64 baseline — MMAudio LoRA guide default. More expressive adapter for 49-clip dataset.",
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"rank": 64
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}
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]
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}
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