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JohnP1
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Model Details
Model Provider
JohnP1
Model Tree
google/gemma-4-E2B-it
Input Modalities
Output Modalities
Supported Functionality
A LoRA adapter (rank 16 on the attention and MLP projections: q, k, v, o, gate, up, down) and a small pointer head (256-dimensional) on google/gemma-4-E2B-it at revision 3e22461f.
| Stage | Training data | Settings |
|---|---|---|
1. General decisions (recipes/d1a-e2b.yaml, what made E2B v0.2) | decision-v7 suite: public classification datasets plus generated policy and rule data | 2 epochs, lr 1e-4, batch 4 × accum 2, from the -it trunk; not published |
| 2. Every later D1A skill, in one run (E2B v0.6's mix) | PR labels: 4,000 English (severity-stratified), 753 Japanese, 1,500 with a blast-radius label, and 479 hermes-agent PRs created 2026-10-02 to 10-05 shown twice (once in each half); hard-v1 4,000; devtools-v1 4,000; documents-v1 2,500; JGLUE 2,000; routing: agent factory 1,216, generic 1,000; decision-v7 replay 1,500: 23,427 records | from stage 1, 1 epoch, lr 5e-5, states up to 5,120 tokens, 2,929 steps (2 h 45 m) |
Both stages ran in one Hugging Face Jobs job on one NVIDIA L4, about $3.51, finished 2026-10-12 18:33 JST. The mix is E2B v0.6's, byte for byte (sha256 efff4b16), screened before training against every evaluation partition, the 487 newer PRs below included. provenance.json has the details.
Calibration: a single temperature, T = 1.41 (90% interval [1.35, 1.48]), fitted on pooled rows of the decision-v7 calibration split and the PR-labelling development set (3,178 questions). Calibration error is 0.053 before and 0.020 after, out of fold; on the 487 newer PRs below (974 questions) it is 0.021. It is stored in d1a_config.json and head.pt, and there are no per-use-case temperatures.
487 hermes-agent pull requests created after every PR in the training mix, held back before training. All models are MLX 8-bit at their served temperatures (E2B-it 1.41, E2B v0.6 1.45), with the serving context. Differences are paired over PRs, with 95% bootstrap intervals. E4B v0.6 is shown for reference:
| Question | Majority answer | E2B-it v0.6 | E2B v0.6 | E4B v0.6 | E2B-it − E2B v0.6 |
|---|---|---|---|---|---|
| Severity (P0–P4) | 67.8% | 72.9% | 77.2% | 79.1% | −4.3 [−7.8, −1.0] |
| Change type | 51.5% | 87.3% | 87.5% | 90.6% | −0.2 [−3.1, +2.7] |
| P0 recall (7 PRs) | 2/7 | 0/7 | 5/7 | ||
| P1 recall (23 PRs) | 1/23 | 0/23 | 8/23 | ||
| Security type recall (14 PRs) | 11/14 | 12/14 | 9/14 |
It finds 3 of the 30 P0/P1 PRs where E2B v0.6 found none, but it loses more on P3 (273/330 against 295/330) and P2 (74/121 against 77/121) than it gains.
Not measured yet. D1A's card-suite gate (transfer-v4, hard-v1, devtools-v1, documents-v1, decision-v7, JGLUE) has not run on this build. This card will not quote numbers for them until it has.
With the media extra, d1a.serving.serve answers questions about a photo, a voice clip or a video through Gemma 4 E2B-it's own vision and audio encoders, unchanged by the adapter (POST /v1/systemone/media). No media in training (zero-shot), measured on only the playground's 14 photo, voice and video demo requests. It answers all 14:
PR labelling works best with these questions, worded exactly so, over a document of the form title / author / stats / body / files (one - status path +added/-deleted line per file):
type (choice): "Primary change type from files and body, not the title prefix." Options: type/bug, type/docs, type/feature, type/perf, type/refactor, type/security, type/test.blast (choice): "How far a mistake in this PR spreads in production." Options: review:blast-contained (one module), -moderate (one subsystem), -broad (shared helper or config), -massive (auth, permissions, or all paths).sev (choice): "How serious the problem this PR addresses is — not the risk of merging the diff as-is." Options P0 to P4.The PR-labeler recipe has the exact wording, the document builder and the training and scoring scripts.
Needs d1a with jonpol01/d1a#261 (the <bos> above):
bash
pip install "d1a[serve] @ git+https://github.com/jonpol01/d1a"python -m d1a.serving.serve --run JohnP1/d1a-e2b-it@v0.6 --port 8009or in-process: from d1a import D1A; D1A.load("JohnP1/d1a-e2b-it@v0.6").decide(state, questions). Builds of the same model: MLX 8-bit JohnP1/d1a-e2b-it-mlx-q8 (with the photo, voice and video encoders), MLX 4-bit JohnP1/d1a-e2b-it-mlx-q4, Core ML JohnP1/d1a-e2b-it-coreml (int8) and JohnP1/d1a-e2b-it-coreml-4bit (int4). Unless your app already loads Gemma 4 E2B-it, use JohnP1/d1a-e2b v0.6 instead.
d1a.agents.presets.advise: deny at p ≥ 0.5, allow only at p ≥ 0.8, otherwise ask), never through the raw top answer. E2B v0.6 gave a near-tie on the playground's $4,999 payment example; this build's demo answers have not been read yet.<bos>: requests encoded without the leading id 2 are out of distribution (see above).| Tag | What it is |
|---|---|
v0.6 | E2B v0.6's recipe on the Gemma 4 E2B-it trunk; experimental, Bar B of #227 failed (severity −4.3, media 21/28) |
Apache-2.0. Base model: Gemma 4 by Google (Apache-2.0). Code: github.com/jonpol01/d1a, built on Kev by Jared Palmer (Apache-2.0). Japanese decision data derived from JGLUE by Yahoo Japan Corporation and Waseda University (CC BY-SA 4.0). Pull-request data from NousResearch/hermes-agent (MIT); the labelled PR dataset itself, including the recent PRs, is private.
pip install "d1a[serve] @ git+https://github.com/jonpol01/d1a"python -m d1a.serving.serve --run JohnP1/d1a-e2b-it@v0.6 --port 8009