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RLTT/generator-v26-h17
generator-v26-h17 is a machine learning model from RLTT. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Lineage: v18 → v22 → v23 → v24 → v25. Fork of v24.
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From the Hugging Face model README
Lineage: v18 → v22 → v23 → v24 → v25. Fork of v24.
Paired attribution against the reigning king (UID 158) over 1536 fresh windows (July 27-29, two window seeds each) located the gap precisely. Sorting upstream feeds by how often they repeat the previous sample ordered v18's losses almost perfectly:
| feed | windows | zero-diff frac | levels | v18 vs king | v24 vs king |
|---|---|---|---|---|---|
| gbfs_bergen_station_status | 57 | 0.968 | 7 | -76.9% | -53.4% |
| nextbike_zagreb_station_status | 87 | 0.949 | 12 | -52.6% | -37.9% |
| gbfs_trondheim_station_status | 32 | 0.925 | 9 | -20.8% | -17.5% |
| gbfs_bicing_barcelona_station_status | 88 | 0.638 | 20 | +5.6% | +4.4% |
Bergen and Zagreb alone account for about 4 of the 5 percentage points between v18 and the king. These feeds hold a value bit-exact for tens of samples, so their naive-difference scale is tiny and a forecast that drifts even a fraction of one count is punished heavily.
Three attempts narrowed down what actually helps:
ou_stochastic_vol, regime_shift, integrated, and threshold_ar cost
19.7% on NZ electricity prices over 101 windows, cancelling the gain.piecewise_level family at 11% mass. Levels are held exactly constant for
hundreds of steps and broken by rare changes, with a bounded-integer variant
(3-40 capacity, unit moves) and a continuous variant. Change rates are drawn
log-uniformly over 0.05%-9% of samples, so the corpus spans both the
near-frozen feeds and the moderately active ones. Only a quarter of continuous
rows carry reading noise; the rest stay bit-exact, which is the property the
metric rewards most.dock_occupancy raised to 15% and sharpened toward the flat band the pool
actually occupies: swing 0.03-0.6 of capacity (was 0.04-1.1), jump rate
0.002-0.09 (was 0.003-0.2), seasonal amplitude up to 0.30 of capacity (was
0.45). Median flat fraction moves from 0.81 to 0.89.forecast_tasks (0.435 → 0.261) and the generic
stochastic families. regime_shift, integrated, threshold_ar,
ou_stochastic_vol, pulse_outlier, tidal_harmonic, physical_sensors,
seasonal_counts, intermittent, stable_calendar_counts, and
conditional_stability are restored to their v18 weights, since those are what
serve the spiky price and sensor feeds v24 damaged.observation.small_count_rate stays at 0, so no blanket requantization.| flat >= 0.8 | flat >= 0.90 | flat >= 0.95 | <= 24 levels | |
|---|---|---|---|---|
| pool | 43.7% | 23.1% | 11.8% | 64.5% |
| v18 | 11.0% | 8.4% | 7.1% | 13.4% |
| v24 | 16.3% | 13.2% | 11.0% | 22.9% |
| v25 | 25.6% | 22.5% | 19.8% | 32.0% |
Throughput is 799 series/s against v18's 729 measured back-to-back, since both
new families are cheaper per row than the forecast_tasks mass they replace, so
the training budget should not lose tokens.