Ethanfel
a7923d5fb7
15 experiments across rank (64/128), alpha, regularisation, LR, target layers, and combined stacks. Based on tier1_thorough early results confirming rank 64 sounds best perceptually. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
131 lines
3.9 KiB
JSON
131 lines
3.9 KiB
JSON
{
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"name": "r64_overnight",
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"description": "Focused rank-64 overnight sweep. All experiments use rank 64 as base — confirmed best from tier1_thorough early results. 8000 steps to reach convergence (none converged at 4000).",
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"data_dir": "/media/unraid/davinci/Selva/BJ/features",
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"output_root": "/media/unraid/davinci/Selva/BJ/experiment/r64_overnight",
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"base": {
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"steps": 8000,
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"rank": 64,
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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": 200,
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"grad_accum": 1,
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"save_every": 2000,
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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": "g1_r64_baseline",
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"group": "rank",
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"description": "Rank 64 baseline — clean reference at 8000 steps."
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},
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{
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"id": "g1_r128_baseline",
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"group": "rank",
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"description": "Rank 128 — 102GB VRAM makes this free. Does doubling rank from 64 help further?",
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"rank": 128
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},
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{
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"id": "g2_r64_alpha_32",
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"group": "alpha",
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"description": "Rank 64 alpha=32 (scale=0.5). Reduces intruder singular dimensions (arXiv:2410.21228).",
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"alpha": 32.0
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},
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{
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"id": "g2_r64_alpha_16",
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"group": "alpha",
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"description": "Rank 64 alpha=16 (scale=0.25). More aggressive scale reduction — may over-constrain.",
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"alpha": 16.0
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},
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{
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"id": "g3_r64_lora_plus",
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"group": "regularisation",
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"description": "LoRA+ ratio=16. lr_B = 16 × lr_A. Faster convergence at constant step budget.",
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"lora_plus_ratio": 16.0
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},
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{
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"id": "g3_r64_dropout_0.05",
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"group": "regularisation",
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"description": "Dropout=0.05. Light sparsity regularisation on LoRA path.",
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"lora_dropout": 0.05
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},
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{
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"id": "g3_r64_dropout_0.1",
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"group": "regularisation",
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"description": "Dropout=0.1. Stronger regularisation — tests if 49 clips needs heavier constraint.",
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"lora_dropout": 0.1
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},
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{
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"id": "g3_r64_curriculum",
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"group": "regularisation",
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"description": "Curriculum sampling: logit_normal for steps 1-4800, then uniform (arXiv:2603.12517).",
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"timestep_mode": "curriculum"
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},
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{
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"id": "g4_r64_lr_low",
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"group": "lr",
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"description": "LR=3e-5. 3× lower — checks if 1e-4 is overshooting at rank 64.",
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"lr": 3e-5
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},
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{
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"id": "g4_r64_lr_high",
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"group": "lr",
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"description": "LR=3e-4. 3× higher — may converge faster but risk instability.",
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"lr": 3e-4
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},
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{
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"id": "g5_r64_target_full",
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"group": "target",
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"description": "Rank 64 targeting attn.qkv + linear1 (FFN projections). Doubles LoRA coverage.",
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"target": "attn.qkv linear1"
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},
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{
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"id": "g5_r128_target_full",
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"group": "target",
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"description": "Rank 128 + full target. Maximum possible coverage with available VRAM.",
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"rank": 128,
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"target": "attn.qkv linear1"
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},
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{
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"id": "g6_r64_full_tier1",
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"group": "combined",
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"description": "All Tier 1 at rank 64: LoRA+ 16 + dropout 0.05 + curriculum. Full stack at 8000 steps.",
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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": "g6_r64_alpha32_full",
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"group": "combined",
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"description": "Rank 64 alpha=32 + all Tier 1. Best alpha scaling + best regularisation stack.",
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"alpha": 32.0,
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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": "g6_r128_full_tier1",
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"group": "combined",
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"description": "Rank 128 + all Tier 1. Tests if more capacity + regularisation beats rank 64 full.",
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"rank": 128,
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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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}
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