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Model Details
Model Provider
elrems
Model Tree
google/gemma-4-E4B-it
Input Modalities
Output Modalities
Supported Functionality
585 prepared examples, all from PrestaShop bugs merged before 2025-06-01 (Gemma 4's declared cutoff is January 2025):
trajectories/reconstruct.py);trajectories/self_paths.py).Examples touching any function also fixed after the split were excluded. No proprietary-model output is included.
With MAX_LEN = 2048, 89 examples were actually used. Exact snapshot: training/snapshots/trajectories_v15.tar.gz.
4-bit QLoRA, r = 16, α = 32, 3 epochs (18 steps), lr 5e-5, 2× T4 on Kaggle, 7,209 s, mean training loss 1.192
(training/snapshots/train_kaggle_v15.py, log training/lora_final/gemma-4-qlora-training-prestashop.log).
A chunked cross-entropy (training/chunked_loss.py) avoids materialising the full logits tensor over the 262k vocabulary.
adapter_model.safetensors sha256 starts with fac3f1af8b0fb855.
Condition E (rules + glossary + replay spec, 2 retries), 3 runs per model on the same infrastructure
(base loaded in 4-bit NF4 as in training; docs/RESULTATS_E4B.md, recomputed by notebook/verification.ipynb):
| Model | Solved per run | Mean | Right file read | Δ vs base (95% CI) |
|---|---|---|---|---|
| Gemma 4 E4B base (no adapter) | 3, 4, 4 | 3.67 / 33 | 13–16 | — |
| E4B + this adapter (v15, 89 examples) | 1, 1, 2 | 1.33 / 33 | 11–13 | −7.1 pts [−16.2, 0.0] |
| E4B + v16 adapter (453 examples, ≤ 4096 tokens) | 0, 0, 0 | 0 / 33 | 11 | −11.1 pts [−23.2, −2.0] |
This adapter does not improve Gemma 4 E4B on this benchmark; the larger v16 adapter degrades it. Patch application rates are unchanged; localisation drops. An earlier single-run figure (4/33, 28 Sept.) is within the base model's noise. We publish the adapter for reproducibility of this negative result.
Small training set (89 examples), n = 33 test bugs, single run for E. PrestaShop is © PrestaShop SA and contributors (OSL-3.0).