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Qwen/Qwen3-4B.
It contains a shared 14-task latent table and canonical LoRA-generating core, jointly trained with
Qwen3-1.7B, plus the alignment specific to Qwen3-4B. It generates rank-8 LoRA factors for the query
and value projections of every decoder layer.acc_norm). Gold continuation token-mean NLL was tracked separately
for checkpoint selection.| Model | Macro acc_norm |
|---|---|
| Frozen Qwen3-4B | 0.6276 |
| PorTAL-adapted | 0.7406 |
| Absolute lift | +0.1130 |
Qwen/Qwen3-4B at 1cfa9a7208912126459214e8b04321603b3df60cRampPublic/portallib-tasks at
ffc3c0e44f529bf64a5ae62ed5db090952db97ea1e-3,
latent LR 2e-3, linear decay with 10% warmup, seed 0acc_norm, with lower gold NLL as the tie-breaker1from portallib import PortalModel
2
3portal = PortalModel.from_pretrained(
4 "RampPublic/portal-qwen3-4b",
5 revision="v0.2.0",
6)
7portal.export_peft("rte", "./portal-rte-qwen3-4b")