---
base_model: google/gemma-4-E4B-it
library_name: peft
license: apache-2.0
tags:
- gemma4
- coding
- qlora
- kaggle-proof
---

# Gemma 4 E4B IT Coding LoRA

QLoRA adapter for `google/gemma-4-E4B-it`, trained on filtered benign coding instructions.

## Training

- Runtime: Kaggle 2x Tesla T4
- Data: filtered benign coding instruction data
- Safe rows used: 1024
- Steps: 200
- LoRA: r=16, alpha=32, target_modules=`all-linear`
- Trainable parameters: 50,499,584
- Final train loss: 1.1427

## 50-Problem HumanEval Proof

This adapter was merged into `josephmayo/gemma-4-E4B-it-Coder` and evaluated on Kaggle with 2x Tesla T4 GPUs using an executable 50-task HumanEval subset. Full generated before/after code is published in `eval50_before_after_full_code.csv`.

| Metric | Base `google/gemma-4-E4B-it` | Coder merge |
|---|---:|---:|
| Pass count | 34 / 50 | 42 / 50 |
| Absolute lift | - | +16.0 pp |
| Relative pass-count lift | - | +23.53% |

Proof files included here: `eval50_summary.json`, `eval50_before_after_full_code.csv`, `EVAL50_README.md`, `nvidia_smi.txt`.

Earlier 8-task smoke artifacts are still included for reproducibility (`eval_before_after.csv`, `executable_eval.json`, `trainer_log_history.json`, `summary.json`, `proof_summary.json`, `evaluation_scope.json`), but the headline proof is the 50-task executable run above.

This adapter is for benign coding assistance only. It was not trained on malware, phishing, exploit, credential theft, evasion, or destructive automation examples.