PorTAL for Qwen3-4B
This is a native PorTAL artifact for 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.
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 Qwen3-4B | 0.6276 |
| PorTAL-adapted | 0.7406 |
| Absolute lift | +0.1130 |
These are research benchmark results for this exact artifact and evaluation recipe, not a general performance guarantee.
Training recipe
- Base:
Qwen/Qwen3-4Bat1cfa9a7208912126459214e8b04321603b3df60c - Joint source bases: Qwen3-1.7B and Qwen3-4B
- Dataset:
RampPublic/portallib-tasksatffc3c0e44f529bf64a5ae62ed5db090952db97ea - Source data: leading 2,000 examples per task, or all available examples for shorter tasks; fixed across epochs
- Optimization: 500 balanced rounds per epoch, 12 epochs, batch size 4, core/alignment LR
1e-3, latent LR2e-3, linear decay with 10% warmup, seed 0 - 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
The Qwen3-1.7B and Qwen3-4B source artifacts contain identical shared task latents and canonical core weights; only their base-specific alignments differ. Either source artifact can seed a new target-base refit.
Usage
from portallib import PortalModel
portal = PortalModel.from_pretrained(
"RampPublic/portal-qwen3-4b",
revision="v0.2.0",
)
portal.export_peft("rte", "./portal-rte-qwen3-4b")
See the release recipe for the full task list, evaluation definition, and training procedure. The artifact is Apache-2.0; the benchmark dataset contains components under multiple upstream licenses documented on its dataset card.