DreamBlooms/kev-0.8b-GGUF

Kev-0.8B

Kev-0.8B 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, 11.3M trainable parameters) plus a pointer head on Qwen/Qwen3.5-0.8B-Base (revision dc7cdfe2), serving TypeSafe's public /v1/systemone contract.

The small member of the Kev family. Same data and recipe as the 0.6B it replaces, on the Qwen3.5 base: in-distribution 0.825 (Kev-0.6B 0.801), out of domain 0.652 (0.620), and it is the first small Kev that learns any rule composition (held-out pairs 0.42 vs 0.08). Three seeds of the base recipe: transfer 0.622 / 0.634 / 0.643; this checkpoint is seed 2 (selected on development accuracy) followed by a 9-minute delta fine-tune on 1,425 generated records (date-bearing policy cases with explicit day counts; evidence-free cases with uniform targets) mixed with 2,000 replayed training records — the same delta as Kev-4B and Kev-9B. Locked test against the pre-delta checkpoint: out of domain 0.668 → 0.684 (+2.2 pp [−0.8, +5.5]), Brier 0.473 → 0.460. Out of domain it is still a sub-1B model: use Kev-4B for accuracy; use this one where memory rules the 4B out, and measure on your own data.

• —Hub: jaredpalmer/kev-0.8b (this repo; trial night2-08b-du2/00-trial-0). The pre-delta checkpoint is at revision v7-base.
• —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, results, and the full research log: github.com/jaredpalmer/kev — PLAN_Qwen35.md, PLAN.md, runs/leaderboard.md

Results (same frozen items for every row)

Per-source out-of-domain accuracy (Kev-0.8B / Jev): QNLI 0.85 / 0.93, SciQ 0.91 / 0.99, TweetEval-offensive 0.68 / 0.81, PAWS 0.55 / 0.79, MMLU 0.42 / 0.90, Emotion 0.54 / 0.59, authorization 0.97 / 1.00, deadline (3-level date arithmetic) 0.38 / 0.93, (A or B) and C 0.66 / 0.91, (A and B) or not C 0.56 / 0.97, if A then not B else C 0.59 / 0.78.

Paired against Kev-0.6B on the same items (record-clustered bootstrap), before the delta: +5.7 pp [+1.2, +10.0] out of domain; the delta adds +0.5 pp [−3.2, +3.8] on development and +2.2 pp on the locked test.

Locked test, read once per checkpoint (runs/locked/kev-08b-night2-du-ungated/; pre-delta runs/locked/kev-08b-q35-ungated/): in-distribution 0.834 (Brier 0.268, ECE 0.100), out-of-domain 0.684 (Brier 0.460, ECE 0.154, confident errors 8.7%, held-out pairs 0.45). Pre-delta: 0.827 / 0.668; Kev-0.6B on the same test items: 0.808 / 0.642.

Known limits

• —Out of domain it is a sub-1B model. Knowledge (MMLU 0.41) and paraphrase (PAWS 0.59) are near the untrained base; the same recipe reaches 0.79 at 4B and 0.81 at 9B on these items.
• —Slow on a Mac for its size. The DeltaNet kernels have no MPS implementation; a five-question request takes ~0.33 s in bf16 on an M5 (Kev-0.6B: 0.12 s). On CUDA with flash-linear-attention it is fast.
• —Requires transformers >= 5.17 and peft >= 0.21.
• —Ordinal hedging on date arithmetic (deadline 0.38): collapses to the middle level. KEV_DATE_FACTS=1 (day counts appended to the state) helps the larger models more than this one.
• —Confident-error rate out of domain is 9.9% for the raw logits; the built-in temperature (T = 2.41, fitted on the in-distribution development rows and stored in head.pt) brings it to 0.3% and ECE from 0.179 to 0.054 without changing any answer. KEV_TEMPERATURE=1.0 gives the raw values. Probabilities are usable in-domain; treat them as advisory elsewhere.

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 attention, MLP and DeltaNet projections; pointer head from scratch; cross-entropy on the option distribution; lr 1e-4 (OneCycle), batch 8, bf16 autocast with fp32 master weights; option permutation, none-of-the-above insertion, distractors, none minimal pairs on 25% of Choice records; ~20 min on one H100. Then the delta: --init_from jaredpalmer/kev-0.8b@v7-base --data evals/night2/dates_unknowable.jsonl --replay 2000 --lr 4e-5 --epochs 1, 9 minutes. No Jev outputs were used for training.

Evaluation protocol

Development partitions select models; the locked test partition is read at most once per candidate. Every number carries suite hash, code hashes and git commit in result.json.

Use

uv run --extra serve python -m kev.serve --run jaredpalmer/kev-0.8b --port 8008

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/Qwen3.5-0.8B-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 fp32 before quantizing; this matches the torch merge path. It 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-0.8b-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.