Everything Model Zero does, in order

00:00:03 · trained on 2187 examples · $0 compute cost

Solana Launch Dataset v6 · boosted decision trees · Model Zero training server

94 held-out snapshots from 45 launches. No model can train on these.

2,492 snapshots from 500 launches.

Same configuration as v0.04 (best so far on the headline benchmark), retrained on the newest dataset.

Recorded as an outcome label (never used as a feature).

average score across 1 tests 0.500 vs 0.333 for the live v0.03

average score across 1 tests 0.333 vs 0.333 for the live v0.01

Reason: .

A bug kept earlier versions from being scored on newly locked test sets, so every deploy check since v0.02 found no shared benchmarks. After the fix, all versions were scored on Held-out test set v4 and the deploy rule was re-run in version order.

58 held-out snapshots from 28 launches. No model can train on these.

58 held-out snapshots from 28 launches. No model can train on these.

Reason: no shared benchmarks yet; keeping the live model.

Crash-spotting score: not enough held-out data yet (n=220).

00:00:03 · trained on 679 examples · $0 compute cost

Solana Launch Dataset v5 · boosted decision trees · Model Zero training server

220 held-out snapshots from 44 launches. No model can train on these.

784 snapshots from 157 launches.

Adds the realized track record (inside Model Zero's sample, before the snapshot) of the wallets buying the launch — how many are known, profitable, losing or high-frequency.

Recorded as an outcome label (never used as a feature).

Recorded as an outcome label (never used as a feature).

Recorded as an outcome label (never used as a feature).

Reason: no shared benchmarks yet; keeping the live model.

Crash-spotting score: not enough held-out data yet (n=65).

00:00:04 · trained on 1025 examples · $0 compute cost

Solana Launch Dataset v4 · boosted decision trees · Model Zero training server

65 held-out snapshots from 13 launches. No model can train on these.

1,170 snapshots from 235 launches.

Deeper trees (depth 3), more of them (300) with a lower learning rate.

Reason: no shared benchmarks yet; keeping the live model.

No locked test set yet (not enough held-out launches with observed outcomes). Benchmarks will be computed when it is locked.

00:00:01 · trained on 594 examples · $0 compute cost

Solana Launch Dataset v3 · boosted decision trees · Model Zero training server

694 snapshots from 139 launches.

Switches to gradient-boosted trees (depth 2, 120 trees), which can learn thresholds and interactions a linear model cannot.

Reason: no shared benchmarks yet; keeping the live model.

No locked test set yet (not enough held-out launches with observed outcomes). Benchmarks will be computed when it is locked.

00:00:00 · trained on 110 examples · $0 compute cost

Solana Launch Dataset v2 · linear model · Model Zero training server

115 snapshots from 23 launches.

Adds trade-flow, holder-estimate, creator-history and social features (all 36 features in set f1).

Reason: no shared benchmarks yet; keeping the live model.

No locked test set yet (not enough held-out launches with observed outcomes). Benchmarks will be computed when it is locked.

00:00:00 · trained on 60 examples · $0 compute cost

Solana Launch Dataset v1 · linear model · Model Zero training server

First learned model: logistic regression on price and volume only (12 features).

first completed model

No locked test set yet (not enough held-out launches with observed outcomes). Benchmarks will be computed when it is locked.

00:00:00 · trained on 60 examples · $0 compute cost

Solana Launch Dataset v1 · baseline (averages) · Model Zero training server

60 snapshots from 12 launches.

Baseline. Predicts each outcome's historical base rate and ignores the token entirely. Every later version has to beat this to be worth anything.