Phase 4 results - SmolLM2 decoder, 7 epochs
Browse files- results/phase4_smollm2.json +91 -0
results/phase4_smollm2.json
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{
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"run_name": "phase4_smollm2",
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"phase": "4",
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"decoder": "SmolLM2-135M-Instruct",
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"bridge": "LLaVA-1.5 MLP (768->576->576)",
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"backbone": "dinov3",
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"K": 3,
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"stage1_epochs": 3,
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"stage2_epochs_completed": 3,
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"stage2_epochs_target": 7,
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"bridge_lr": 0.001,
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"lm_lr": 2e-05,
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"core_lr": 5e-05,
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"label_smoothing": 0.1,
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"num_evidence_tokens": 8,
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"gen_weight": 2.0,
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"eval_history": [
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{
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"epoch": "S1E0",
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"mc": 58.5,
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"docvqa_anls": 0.0,
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"chartqa_ra": 0.0,
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"textvqa_vqa": 0.0,
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"composite": 14.6
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},
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{
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"epoch": "S1E1",
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"mc": 58.0,
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"docvqa_anls": 0.0,
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"chartqa_ra": 0.0,
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"textvqa_vqa": 0.0,
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"composite": 14.5
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},
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{
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"epoch": "S1E2",
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"mc": 58.0,
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"docvqa_anls": 0.0,
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"chartqa_ra": 0.0,
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"textvqa_vqa": 0.0,
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"composite": 14.5
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},
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{
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"epoch": "S2E0",
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"mc": 58.0,
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"docvqa_anls": 0.0,
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"chartqa_ra": 0.0,
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"textvqa_vqa": 0.0,
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"composite": 14.5
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},
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{
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"epoch": "S2E1",
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"mc": 55.0,
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"docvqa_anls": 0.0,
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"chartqa_ra": 0.0,
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"textvqa_vqa": 0.0,
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"composite": 13.8
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},
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{
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"epoch": "S2E2",
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"mc": 56.0,
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"docvqa_anls": 0.0,
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"chartqa_ra": 0.0,
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"textvqa_vqa": 0.0,
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"composite": 14.0
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},
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{
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"epoch": "S2E3",
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"mc": 55.0,
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"docvqa_anls": 0.0,
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"chartqa_ra": 0.0,
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"textvqa_vqa": 0.0,
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"composite": 13.8
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}
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],
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"key_findings": [
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"SmolLM2 pre-trained decoder generates real English text (vs degenerate repetition in Phase 3.x)",
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"Gen losses dropped dramatically: ChartQA 4.98->3.40, DocVQA 7.53->4.52, TextVQA 10.33->3.97",
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"Predictions are diverse and topically relevant but too verbose for exact-match metrics",
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"Bridge MLP successfully projects JEPA 768d -> SmolLM2 576d space",
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"MC accuracy holds at 55-58% (slight catastrophic forgetting during Stage 2)",
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"Next steps: post-processing, shorter generation, greedy decoding"
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],
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"improvements_over_phase3": [
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"Replaced random-init 4-layer decoder (7M params) with SmolLM2-135M (135M params)",
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"LLaVA-1.5 two-layer MLP bridge for nonlinear alignment",
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"Label smoothing (0.1) for smoother gradient signal",
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"Nucleus sampling + repetition penalty in eval",
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"Two-stage training: bridge-only then full fine-tuning",
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"SmolLM2 tokenizer (49K vocab) vs Qwen3 (152K vocab)"
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]
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}
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