Text Classification
PEFT
Safetensors
English
Japanese
d1a
decision-model
calibration
lora
gemma4
typesafe
system-one
pull-requests
on-device
Instructions to use JohnP1/d1a-e2b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use JohnP1/d1a-e2b with PEFT:
from peft import PeftModel from transformers import AutoModel base_model = AutoModel.from_pretrained("google/gemma-4-E2B") model = PeftModel.from_pretrained(base_model, "JohnP1/d1a-e2b") - Notebooks
- Google Colab
- Kaggle
Download provenance.json from JohnP1/d1a-e2b: direct link, hf CLI and curl.
- Browser
- Download file 13.9 kB
-
https://huggingface.co/JohnP1/d1a-e2b/resolve/main/provenance.json
- Command line
-
hf download hf://JohnP1/d1a-e2b/provenance.json
-
curl -L -o provenance.json https://huggingface.co/JohnP1/d1a-e2b/resolve/main/provenance.json
13.9 kB
| { | |
| "model": "D1A-E2B v0.6", | |
| "base": "google/gemma-4-E2B", | |
| "base_revision": "d29ff6b45f081a49ee2733a859c9c9c2d95d1a6f", | |
| "init_from": "JohnP1/d1a-e2b@v0.2.1-2epoch-calibrated (commit b529d2fd25bb813319903ff8f1304e670677a6bb; v0.2.1 had only decision-v7)", | |
| "stage": "Every later skill in one run (jonpol01/d1a#229): PR labels (English 4,000, Japanese 753, blast radius 1,500, and 479 hermes-agent PRs created 2026-10-02 to 10-05 shown twice), hard-v1 4,000, devtools-v1 4,000, documents-v1 2,500, JGLUE 2,000, routing factory/generic 1,216/1,000, decision-v7 1,500; 23,427 records; 1 epoch, lr 5e-5, LoRA 16 on all projections, batch 2 x accum 4, states up to 5,120 tokens, 2,929 steps", | |
| "release_exception": "Ships despite #229's pre-registered bar (no card suite below -1: decision-v7 -1.4 [-2.8, -0.1]), by the owner's decision of 2026-10-11, so that E2B and E4B each exist as MLX and Core ML builds and can be compared. E2B v0.6 is not a replacement for E4B v0.6 as the PR labeler (P0/P1 recall 0/30 on newer PRs).", | |
| "data": { | |
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| "P0": 12 | |
| } | |
| }, | |
| "reuse": { | |
| "never trained": 479 | |
| }, | |
| "est_tokens": 522078, | |
| "est_tok_per_rec": 1090, | |
| "est_mps_hours": 2.05, | |
| "passes": 2 | |
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| "pr-labels:train": { | |
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| "None": 3 | |
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| "B0 pilot (v0.4 + 300 steps)": 175 | |
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| "est_mps_hours": 15.0 | |
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| "est_mps_hours": 1.84 | |
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| "personal_loan": 209 | |
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| "managing": 144, | |
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| } | |
| }, | |
| "reuse": { | |
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| }, | |
| "est_tokens": 358990, | |
| "est_tok_per_rec": 239, | |
| "est_mps_hours": 1.41 | |
| } | |
| }, | |
| "records": 23427, | |
| "sha256": "efff4b161e2d493f3d6e33712174614964279446ed2deb65b78ad4f7cb86e7a9", | |
| "seed": 0, | |
| "fresh_prs": { | |
| "prs": 479, | |
| "passes": 2, | |
| "created": "2026-10-02 to 2026-10-05", | |
| "text": "private; aggregates only" | |
| }, | |
| "leak_checks": "0 eval-state overlap, 0 PR-id overlap with the sealed fresh EVAL half and every eval partition" | |
| }, | |
| "training": { | |
| "epochs": 1, | |
| "lr": 5e-05, | |
| "head_lr": 0.0, | |
| "weight_decay": 0.01, | |
| "lora": 16, | |
| "lora_targets": "all", | |
| "batch": 2, | |
| "accum": 4, | |
| "dtype": "bf16", | |
| "weights_dtype": "bf16", | |
| "checkpointing": 1, | |
| "max_state": 5120, | |
| "p_none": 0.1, | |
| "p_none_distract": 0.12, | |
| "p_distract": 0.15, | |
| "seed": 0 | |
| }, | |
| "compute": { | |
| "where": "Hugging Face Jobs, one NVIDIA L40S (48 GB)", | |
| "wall_seconds": 6163, | |
| "optimizer_steps": 2929, | |
| "records_seen": 23427, | |
| "truncated_records": 0, | |
| "rejected_records": 0 | |
| }, | |
| "calibration": { | |
| "temperature": 1.447, | |
| "fit": "pooled rows: decision-v7 calibration split + PR-labeling development set", | |
| "interval_90": [ | |
| 1.382, | |
| 1.516 | |
| ], | |
| "ece_raw": 0.051, | |
| "ece_out_of_fold": 0.024, | |
| "use_case_temperatures": "none" | |
| }, | |
| "code": "github.com/jonpol01/d1a (d1a.training.train, scripts/quality_gate.py, scripts/decide.py)" | |
| } | |