PorTAL refit for Inkling
This is a native PorTAL artifact refitted onto
thinkingmachines/Inkling. 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 Inkling
alignment was trained.
Inkling is loaded through its multimodal wrapper for text-only execution. The artifact records 132 exact q/v projection targets across its language-model decoder, including their heterogeneous input and output dimensions, and generates rank-8 LoRA factors only for those paths.
Evaluation
One seed was evaluated on the pinned 14-task validation suite using continuation log-probability
divided by character length (acc_norm). Gold continuation token-mean NLL was tracked separately.
| Model | Macro acc_norm |
|---|---|
| Frozen Inkling | 0.6767 |
| PorTAL-adapted | 0.8692 |
| Absolute lift | +0.1925 |
The selected checkpoint was epoch 4. The independently minimum-NLL checkpoint was epoch 1 with macro gold NLL 1.8753. Across the same tasks, independently trained rank-16 full-target LoRA baselines averaged 0.8610. Relative to the frozen base, PorTAL retained 104.4% of their aggregate lift.
These are research benchmark results for this exact artifact and evaluation recipe, not a general
performance guarantee. The complete epoch history and diagnostics are in metrics.json.
Refit recipe
- Base:
thinkingmachines/Inklingat828496eeae4c243ff1a22f7f28ff83694f2f7bc9 - Frozen source carrier:
RampPublic/portal-qwen3-4b@v0.2.0 - Dataset:
RampPublic/portallib-tasksatffc3c0e44f529bf64a5ae62ed5db090952db97ea - Refit data: deterministic seeded sample of up to 1,000 examples per task from the full pool
- Evaluation data: leading 1,000 validation examples per task, or all rows for shorter tasks
- Optimization: 5 epochs, batch size 4, alignment LR
2e-5, linear decay with 10% warmup, norm-equalized task gradients, character-normalized choice-loss weight 3, seed 0 - Trainable parameters: target-base alignment only; task latents and canonical core remained frozen
- Checkpoint selection: maximum macro validation
acc_norm, with lower gold NLL as the tie-breaker - Architecture: exact q/v targets, rank 8, alpha 16, task latent 256, layer embedding 32, hidden 512, canonical width 1024
The checked-in recipe is
examples/configs/refits/inkling.toml.
Usage
Install portallib>=0.2.1, then load the immutable artifact revision:
from portallib import PortalModel
portal = PortalModel.from_pretrained(
"RampPublic/portal-inkling",
revision="v0.2.1",
)
portal.export_peft("rte", "./portal-rte-inkling")
The exported directory is an ordinary PEFT LoRA adapter for the exact base revision recorded above. The artifact is Apache-2.0; the benchmark dataset contains components under multiple upstream licenses documented on its dataset card.