Kev-0.6B (Qwen3)
Previous generation (Qwen3). Kept as the fast small option on Apple Silicon (0.12 s per five-question request vs 0.33 s for Kev-0.8B). For accuracy use Kev-0.8B: on the locked test it scores 0.668 vs 0.642 out of domain against this model on the same items. Weights:
jaredpalmer/kev-0.6b.
Kev-0.6B is a decision model: one document (the state) and a set of typed questions in, a probability distribution per question out, in one forward pass. No text generation. It is a LoRA adapter (r=16) plus a pointer head on Qwen/Qwen3-0.6B-Base, and it serves TypeSafe's public /v1/systemone contract.
The small member of the Kev family. It is the best 0.6B checkpoint under a frozen, checksummed evaluation protocol: the 4B/8B recipe's data (decision-v7) at lr 1e-4, three seeds (transfer 0.613 / 0.605 / 0.620), after eight one-knob mutations and three seeds of the previous data found nothing better than 0.61. Out of domain it is a 0.6B model — use Kev-4B for accuracy; use this one where memory or latency rule the 4B out, and measure on your own data.
- Hub:
jaredpalmer/kev-0.6b(this repo; trialv7-06b/02-trial-2, seed 2 of 3) - Code, suites, results, and the full research log: github.com/jaredpalmer/kev — see
PLAN.md,runs/leaderboard.md, andevals/v4/*/manifest.json
What changed since Kev-0.5B
| Kev-0.5B | Kev-0.6B (this) | |
|---|---|---|
| backbone | Qwen2.5-0.5B | Qwen3-0.6B-Base |
| training records | 9,000 (six sources) | 12,576 (ten public sources + 896 policy minimal pairs + 1,680 records from 60 random rule structures) |
| none-of-the-above | augmentation fix only | + minimal pairs: same state rendered with the true option present and removed |
| in-distribution accuracy (decision-v4 dev) | 0.712 | 0.801 |
| out-of-domain accuracy (transfer-v4 dev) | 0.561 | 0.620 |
| none-option present, accuracy | 0.25 (transfer-v1) | 0.80 |
| seeds behind the number | 1 | 3 (transfer 0.605–0.620) |
Jev (typesafe-ai/jev via Vercel AI Gateway) on the same frozen development sets: 0.845 in-distribution, 0.857 out-of-domain. Per-source transfer accuracy for this checkpoint: QNLI 0.85, SciQ 0.93, TweetEval-offensive 0.69, PAWS 0.59, Emotion 0.49, MMLU 0.50; held-out policy structures near chance.
Known limits
- Out of domain it is a 0.6B model. Transfer accuracy is flat at ~0.60 across every hyperparameter we tried (eight one-knob mutations, three seeds). The same recipe at 4B reaches 0.72–0.75 and at 8B 0.74–0.77; capacity, not data, is the bottleneck at this size.
- Held-out policy reasoning fails: on programmatic policy pairs whose rule structure was never trained, both-siblings-correct is 6–11% (Kev-4B 0.73, Jev 0.86).
- Ordinal hedging: on 3-level Score questions with date arithmetic it collapses to the middle level.
- Confident-error rate out of domain is 11% (≥0.9 confidence and wrong); raw ECE 0.09 in-domain, 0.15 out of domain. Probabilities are usable in-domain; treat them as advisory elsewhere.
- Locked test, read once (
runs/locked/kev-06b-v7-ungated/): in-distribution accuracy 0.808 (Brier 0.266, ECE 0.089), out-of-domain 0.642 (Brier 0.483, ECE 0.128, confident errors 7.9%). This partition will not be read again for this checkpoint.
Architecture
Prefill-only causal LM with a block-causal attention mask: a shared state prefix, one isolated branch per question, and a pointer readout over option boundary tokens. Questions packed into one request get exactly the probabilities they would get alone (measured max delta 4e-6). Details in the repository README.
Training
Frozen suite evals/v7/decision-v7 (manifest pins dataset and base-model revisions): 10,000 public records (1,000 per source), 896 policy minimal-pair records over nine template families, and 1,680 records from 60 randomly generated rule structures, two epochs, LoRA r=16 on attention and MLP projections at lr 1e-4, pointer head from scratch, cross-entropy on the option distribution, bf16 autocast with fp32 master weights on one H100 (~12 min). Augmentation: option permutation, none-of-the-above insertion, distractors, and none minimal pairs on 25% of Choice records. No Jev outputs were used for training.
Evaluation protocol
Development partitions select models; a locked test partition exists and is read at most once per promoted candidate. Every number above carries the suite hash, code hashes, and git commit in result.json. Comparisons use a record-clustered paired bootstrap. See PLAN.md for the corrections we made to our own earlier claims.
Use
from typesafe import TypeSafeClient # any TypeSafe-compatible client
client = TypeSafeClient(api_key="local", base_url="http://127.0.0.1:8008", model="kev-latest")
Serve with uv run --extra serve python -m kev.serve --run jaredpalmer/kev-0.6b --port 8008 from the repository.
License
Apache-2.0 for the adapter and head. The base model is Apache-2.0 (Qwen3). Training datasets carry their own licenses.