Kev-4B

Kev-4B 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, 33.8M trainable parameters) plus a pointer head on Qwen/Qwen3.5-4B-Base (revision 1001bb4d), serving TypeSafe's public /v1/systemone contract.

The recommended Kev. The best accuracy per byte: out of domain 0.797 on the development partition and 0.837 on the locked test, Brier 0.255 on the test, held-out rule pairs 0.77–0.78. This checkpoint is the decision-v7 recipe (trial q35-4b-s23/00-trial-0, seed 2, selected on development accuracy) followed by a 9-minute delta fine-tune (--init_from, lr 2e-5, one epoch) on 1,425 additional records — date-bearing policy cases rendered with explicit day counts, and evidence-free cases with uniform targets — mixed with 2,000 replayed training records. Against the pre-delta checkpoint on the locked test: +1.0 pp [−0.1, +2.1], Brier 0.266 → 0.255, deadline 0.65 → 0.75.

• Hub: jaredpalmer/kev-4b (this repo; trial night2-4b-du/00-trial-0). The pre-delta checkpoint is at revision v7-base; the Qwen3 generation at qwen3 (its card).
• Demo: huggingface.co/spaces/jaredpalmer/kev runs Kev-4B and Kev-0.8B on ZeroGPU with the same encoder and API code as kev.serve.
• Code, suites, every trial with hashes and paired bootstraps: github.com/jaredpalmer/kev — PLAN_Qwen35.md (the port and this experiment), PLAN.md, runs/leaderboard.md

Results (same frozen items for every row)

Per-source out-of-domain accuracy (Kev-4B / Jev): QNLI 0.91 / 0.93, SciQ 0.97 / 0.99, TweetEval-offensive 0.74 / 0.81, PAWS 0.74 / 0.79, MMLU 0.70 / 0.90, Emotion 0.56 / 0.59, deadline (3-level date arithmetic) 0.60 / 0.93 — 0.85 with the date_facts preprocessor (below), (A or B) and C 0.91 / 0.91, (A and B) or not C 0.88 / 0.97, if A then not B else C 1.00 / 0.78.

Calibration is built in. head.pt carries a temperature (T = 2.14) fitted on this checkpoint's in-distribution development rows by minimising negative log-likelihood (scripts/calibrate_checkpoint.py); the pointer head divides its logits by it at inference. Every loader — kev.serve, kev.benchmark, the Space, anyone's harness — gets the calibrated probabilities by default. It never changes an answer: the argmax is identical, so accuracy is the same in both columns; confidences are re-ordered only slightly across questions with different option counts, which is why coverage moves by a point or two. KEV_TEMPERATURE=1.0 restores the raw logits; the raw column is what the training produced. Per-(type, option-count) temperatures were tested and are worse out of domain. The fit uses no out-of-domain or test data.

date_facts preprocessor. Kev, like every Kev before it, cannot subtract dates reliably (the untrained base can; LoRA training erodes it). It can use a stated day count. KEV_DATE_FACTS=1 appends one sentence per pair of absolute dates found in the state ("June 26, 2026 is 8 days before July 4, 2026"); this checkpoint was trained on such renderings, so with it deadline goes from 0.60 to 0.85 and overall out-of-domain accuracy from 0.797 to 0.820. It is preprocessing, reported separately, never folded into the model's own numbers.

What the delta cost. MMLU-Pro fell 0.500 → 0.490 and scienthoon's ECE rose 0.086 → 0.116; coverage at ≤ 5% error was unchanged (0.54 development, 0.67 → 0.68 locked test) and confident errors fell (8.2% → 6.9%). The pre-registered criteria for the delta (PLAN.md, "Tonight's autoresearch") were met for dates and for the unknowable-confidence behaviour; the coverage criterion asked for +5 pp and got 0; the locked read decided promotion.

Newer evaluation columns (transfer-v9 development, Kev-4B / Jev): MMLU-Pro (10-way) 0.490 / 0.840; state buried among unrelated records 0.68 / 0.70; unknowable share at ≥ 0.9 confidence 0.00 / 0.09 (intact controls 0.94).

External suites (same items as their published Jev numbers): SemIf's authored 144 — 0.896 before the delta (live Jev 0.965; SemIf's untrained Qwen3.5-4B 0.813); scienthoon's 900 tickets — queue 0.918, angry 0.790, ECE 0.116 (Jev 0.897, 0.914, 0.105). On ekzhang's 1,000-question MMLU-Pro sample the pre-delta checkpoint scores 0.468 (Jev 0.829).

How it was built

• Base model: Qwen3.5-4B-Base, a hybrid of 24 Gated DeltaNet (linear attention) layers and 8 full-attention layers. Because the recurrent layers cannot honour a block-causal mask, questions run as separate causal rows that continue from the shared state (kev/model.py: forward_rows_batch); isolation is exact by construction (together vs alone within 1e-5) and on attention-only models this form is bit-identical to the packed one.
• Recipe: decision-v7, two epochs, LoRA r=16 (attention, MLP and DeltaNet projections), lr 5e-5 — the same data and settings as every other Kev, so the Qwen3 → Qwen3.5 difference is the base (PLAN_Qwen35.md §10: locked test +7.3 pp [+2.8, +11.7] over Kev-8B).
• Delta: kev.train --init_from jaredpalmer/kev-4b@v7-base --data evals/night2/dates_unknowable.jsonl --replay 2000 --lr 2e-5 --epochs 1. The 1,425 new records are generated (no public dataset): 900 date-bearing policy cases, a third rendered plainly, a third with a relational day-count sentence, a third with a date_facts field; 255 cases with the deciding sentence removed and a uniform soft target over the options, plus their 270 intact controls. Record hashes are in evals/night2/manifest.json; the source checkpoint's hashes are in training_config.json.
• Why a delta and not a retrain: it is a controlled change (one fixed checkpoint, one data addition, 9 minutes), and the results section shows exactly what it moved.

Known limits

• Use Kev-9B when accuracy and calibration matter more than memory: 0.852 vs 0.837 out of domain on the locked test, Brier 0.237 vs 0.255.

• Slow on a Mac. The DeltaNet kernels have no MPS implementation; PyTorch falls back to reference code. A five-question request that takes 0.17 s on the Qwen3 Kev-4B takes 0.78 s here in bf16 on an M5. On CUDA with flash-linear-attention installed it is fast. Use jaredpalmer/kev-4b@qwen3 for low latency on Apple Silicon until an MLX path exists.

• Requires transformers >= 5.17 (the qwen3_5 architecture) and peft >= 0.21.

• Knowledge (MMLU 0.70 vs Jev 0.90; MMLU-Pro 0.490 vs 0.840), TweetEval (0.74 vs 0.81) and noisy-label Emotion (0.56 vs 0.59) are the remaining gap; knowledge is set by the base (the untrained Qwen3.5-4B scores the same).

• Date arithmetic without the preprocessor: deadline 0.60 (Jev 0.93). With KEV_DATE_FACTS=1: 0.85.

• The raw logits are over-confident out of domain; the built-in temperature (T = 2.14) fixes most of it without changing any answer. KEV_TEMPERATURE=1.0 gives the raw values. Coverage at a 5% error budget is 0.54–0.68 against Jev's 0.70.

• 4B bf16 needs ~9 GB of GPU memory for serving; training took 56 min on one H100 (peak 24.6 GB).

Training

Frozen suite evals/v7/decision-v7: 10,000 public records (1,000 per source), 896 policy minimal-pair records over nine template families, 1,680 records from 60 randomly generated rule structures in four rendering styles. Two epochs, LoRA r=16 α=32 on q/k/v/o_proj, gate/up/down_proj, in_proj_qkv/z/a/b, out_proj; pointer head from scratch; cross-entropy on the option distribution; lr 5e-5 (OneCycle), effective batch 8, bf16 autocast with fp32 master weights, gradient checkpointing; option permutation, none-of-the-above insertion, distractors, none minimal pairs on 25% of Choice records. Then the delta described above (one epoch, lr 2e-5, 3,937 records seen, 9 minutes on one H100). No Jev outputs were used for training.

Evaluation protocol

Development partitions select models; the locked test partition is read at most once per candidate (runs/locked/kev-4b-night2-du-ungated/; the pre-delta read is runs/locked/kev-4b-q35/). Every number carries suite hash, code hashes and git commit in result.json. Untrained-base baselines use zero-shot letter logits on the same items (scripts/base_mmlu_probe.py).

Use

uv run --extra serve python -m kev.serve --run jaredpalmer/kev-4b --port 8008      # KEV_DTYPE=bf16 on a Mac; slow on MPS, see limits
KEV_DATE_FACTS=1 uv run --extra serve python -m kev.serve --run jaredpalmer/kev-4b --port 8008   # + date preprocessing; KEV_TEMPERATURE=1.0 for raw logits

Any TypeSafe-compatible client works: TypeSafeClient(api_key="local", base_url="http://127.0.0.1:8008", model="kev-latest").

License

Apache-2.0 for the adapter and head; the Qwen3.5 base is Apache-2.0; datasets carry their own licenses.

GGUF

A merged, quantized GGUF for CPU inference is available through dohnuts.cpp, a native C++ port on llama.cpp. It merges this LoRA into Qwen/Qwen3.5-4B-Base and serves the same POST /v1/systemone wire format. No GPU or Python runtime is needed.

The pointer head and kev.json are required alongside the GGUF. The conversion merges the adapter in bf16 before quantizing (the upstream merge path uses fp32); this has not been benchmarked against the PyTorch reference.

git clone https://github.com/DreamBlooms/dohnuts.cpp
cd dohnuts.cpp
git submodule update --init --depth 1
cmake -B build -DCMAKE_BUILD_TYPE=Release -DGGML_NATIVE=ON
cmake --build build -j --target dohnuts-cli

build/dohnuts-cli --server --port 8080 \
  --model kev-4b-q8_0.gguf --head kev-head.f32 --metadata kev.json

Ask one state several questions (the same request shape as kev.serve):

curl http://127.0.0.1:8080/v1/systemone -H 'Content-Type: application/json' \
  -d '{"state":"My card was charged twice for the same purchase.",
       "questions":{"department":{"type":"choice","instructions":"Which team should handle this?",
         "criteria":{"billing":null,"technical support":null,"sales":null}},
         "refund_requested":{"type":"noul","instructions":"Is a refund requested?"}}}'

The answer keeps the core fields (type, choice, probabilities, noul, confidence) and adds this model's own confidence under native.

Rebuild from the upstream checkpoint with scripts/build_kev_gguf.sh, which merges the LoRA into the base, exports kev-head.f32 and kev.json, then converts with --no-mtp. No retraining is involved.