Text Classification
PEFT
Safetensors
English
Japanese
d1a
decision-model
calibration
lora
gemma4
typesafe
system-one
pull-requests
Instructions to use JohnP1/d1a-e4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use JohnP1/d1a-e4b with PEFT:
from peft import PeftModel from transformers import AutoModel base_model = AutoModel.from_pretrained("google/gemma-4-E4B") model = PeftModel.from_pretrained(base_model, "JohnP1/d1a-e4b") - Notebooks
- Google Colab
- Kaggle
Download provenance.json from JohnP1/d1a-e4b: direct link, hf CLI and curl.
- Browser
- Download file 2.56 kB
-
https://huggingface.co/JohnP1/d1a-e4b/resolve/main/provenance.json
- Command line
-
hf download hf://JohnP1/d1a-e4b/provenance.json
-
curl -L -o provenance.json https://huggingface.co/JohnP1/d1a-e4b/resolve/main/provenance.json
2.56 kB
| { | |
| "model": "D1A-E4B v0.6", | |
| "base": "google/gemma-4-E4B", | |
| "base_revision": "411aa17b749aa952df1359d2dcea73917a544d9a", | |
| "init_from": "JohnP1/d1a-e4b@v0.5 (commit e7db32f2254b6feb6ec830ae98662755dce39504)", | |
| "stage": "Current PR labels (D3-fresh, jonpol01/d1a#198): 479 hermes-agent PRs created 2026-10-02 to 10-05, shown twice (one pass in each half of training); documents-v1 2,520 (never trained before); routing factory/generic 600/600; decision-v7 1,000; hard-v1, devtools-v1, JGLUE, PR labels 300 each; PR labels Japanese/blast 100/100; 7,078 records; 1 epoch, lr 2e-5, LoRA 16 on all projections, batch 2 x accum 4, states up to 5,120 tokens, 885 steps", | |
| "release_exception": "Ships despite decide.py's veto V1 against v0.5 (hard-v1 -2.7 [-4.9, -0.5]), by the owner's decision of 2026-10-10: the model's main job is labelling current PRs, where v0.6 is better. Pin @v0.5 for hard-v1-style reasoning or the older PR-label conventions.", | |
| "data": { | |
| "counts": { | |
| "pr-labels:fresh-fit": 958, | |
| "pr-labels:train": 300, | |
| "pr-labels:train-ja": 100, | |
| "pr-labels:train-blast": 100, | |
| "hard-v1:train": 300, | |
| "devtools-v1:train": 300, | |
| "documents-v1:train": 2520, | |
| "ja-jglue:train": 300, | |
| "routing:factory-train": 600, | |
| "routing:generic-train": 600, | |
| "decision-v7:train": 1000 | |
| }, | |
| "records": 7078, | |
| "sha256": "31f7167055468b078e4c8ec16a4ce507fbc6e4662b85effa8bbe8a01dd8477e1", | |
| "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 47 eval partitions" | |
| }, | |
| "training": { | |
| "epochs": 1, | |
| "lr": 2e-05, | |
| "head_lr": 0.0, | |
| "weight_decay": 0.01, | |
| "lora": 16, | |
| "lora_targets": "all", | |
| "batch": 2, | |
| "accum": 4, | |
| "dtype": "fp32", | |
| "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": "local, one Apple M1 Max (MPS)", | |
| "wall_seconds": 60962, | |
| "optimizer_steps": 885, | |
| "records_seen": 7078, | |
| "truncated_records": 0, | |
| "rejected_records": 0 | |
| }, | |
| "calibration": { | |
| "temperature": 1.954, | |
| "fit": "pooled held-out rows: decision-v7 calibration split + PR-labeling development set", | |
| "ece_raw": 0.093, | |
| "ece_out_of_fold": 0.015, | |
| "use_case_temperatures": "none in v0.6; a refitted routing temperature is planned for v0.6.1" | |
| }, | |
| "code": "github.com/jonpol01/d1a (d1a.training.train, scripts/quality_gate.py, scripts/decide.py)" | |
| } | |