PorTAL refit for Qwen3-8B
This is a native PorTAL artifact refitted onto
Qwen/Qwen3-8B. Its 14-task latent table and canonical LoRA-generating core were learned jointly
from Qwen3-1.7B and Qwen3-4B and frozen during refitting. Only a fresh Qwen3-8B alignment was
trained. The artifact generates rank-8 LoRA factors for the query and value projections of every
decoder layer.
Evaluation
One seed was evaluated on the complete 14-task validation suite using continuation log-probability
divided by character length (acc_norm). Gold continuation token-mean NLL was tracked separately
for checkpoint selection.
| Model | Macro acc_norm |
|---|---|
| Frozen Qwen3-8B | 0.6681 |
| PorTAL-adapted | 0.7767 |
| Absolute lift | +0.1086 |
These are research benchmark results for this exact artifact and evaluation recipe, not a general performance guarantee.
Refit recipe
- Base:
Qwen/Qwen3-8Batb968826d9c46dd6066d109eabc6255188de91218 - Frozen source carrier:
RampPublic/portal-qwen3-4b - Dataset:
RampPublic/portallib-tasksatffc3c0e44f529bf64a5ae62ed5db090952db97ea - Refit data: deterministic seeded sample of up to 1,000 examples per task from the complete training pool
- Optimization: 5 epochs, batch size 4, alignment LR
1e-3, linear decay with 10% warmup, seed 0 - Trainable parameters: target-base alignment only; task latents and canonical core remain frozen
- Checkpoint: maximum macro validation
acc_norm, with lower gold NLL as the tie-breaker - Architecture: q/v targets, rank 8, alpha 16, task latent 256, layer embedding 32, hidden 512, canonical width 1024
Usage
from portallib import PortalModel
portal = PortalModel.from_pretrained(
"RampPublic/portal-qwen3-8b",
revision="v0.2.0",
)
portal.export_peft("rte", "./portal-rte-qwen3-8b")
See the release recipe for the full task list, evaluation definition, and refitting procedure. The artifact is Apache-2.0; the benchmark dataset contains components under multiple upstream licenses documented on its dataset card.