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
Card: drop the note on where D1A is served
Browse files
README.md
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**v0.6 labels pull requests the way they are labelled today.** On 487 pull requests created after all of its training, severity goes from **74.7% to 79.1%** (+4.3 points) and change type from **86.7% to 90.6%** (+3.9) against v0.5, and P1 recall returns to v0.4's 0.35 (v0.5: 0.22). It also gains on long documents (+2.0), agent-factory routing (+3.4) and held-out transfer (+1.4), with lower calibration error on most suites. It costs hard decisions (β2.7) and change type on the older frozen PR test set (β4.1), so it ships as a **release exception** (below). v0.5 stays available. See Results.
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**Release exception.** v0.6 does not pass D1A's release rule against v0.5. `scripts/decide.py` vetoes it (V1) on hard decisions: hard-v1 β2.7 points [β4.9, β0.5], 61 fixes against 90 breaks, past the β2.0 limit for a card suite. It also loses change type on the older frozen PR test set, β4.1 [β6.0, β2.3] (19 fixes, 58 breaks), and pooled over all 21 lines it is level with v0.5, β0.20 [β0.98, +0.52]. It ships anyway, by the owner's decision of 2026-10-10: the model's live job
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## D1A models: what each can do (2026-10-14)
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**v0.6 labels pull requests the way they are labelled today.** On 487 pull requests created after all of its training, severity goes from **74.7% to 79.1%** (+4.3 points) and change type from **86.7% to 90.6%** (+3.9) against v0.5, and P1 recall returns to v0.4's 0.35 (v0.5: 0.22). It also gains on long documents (+2.0), agent-factory routing (+3.4) and held-out transfer (+1.4), with lower calibration error on most suites. It costs hard decisions (β2.7) and change type on the older frozen PR test set (β4.1), so it ships as a **release exception** (below). v0.5 stays available. See Results.
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**Release exception.** v0.6 does not pass D1A's release rule against v0.5. `scripts/decide.py` vetoes it (V1) on hard decisions: hard-v1 β2.7 points [β4.9, β0.5], 61 fixes against 90 breaks, past the β2.0 limit for a card suite. It also loses change type on the older frozen PR test set, β4.1 [β6.0, β2.3] (19 fixes, 58 breaks), and pooled over all 21 lines it is level with v0.5, β0.20 [β0.98, +0.52]. It ships anyway, by the owner's decision of 2026-10-10: the model's live job is labelling current pull requests, and on PRs newer than all of its training it is clearly better. The likeliest reading of the disagreement is that the project's labelling conventions have changed over time and v0.6 follows the current ones. **Stay on v0.5** (`JohnP1/d1a-e4b@v0.5`) if you rely on hard-v1-style reasoning or on the older PR-label conventions.
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## D1A models: what each can do (2026-10-14)
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