| ---
|
| base_model: google/gemma-4-E4B-it
|
| library_name: peft
|
| license: apache-2.0
|
| tags:
|
| - gemma4
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| - coding
|
| - qlora
|
| - kaggle-proof
|
| ---
|
|
|
| # Gemma 4 E4B IT Coding LoRA
|
|
|
| QLoRA adapter for `google/gemma-4-E4B-it`, trained on filtered benign coding instructions.
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|
|
| ## Training
|
|
|
| - Runtime: Kaggle 2x Tesla T4
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| - Data: filtered benign coding instruction data
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| - Safe rows used: 1024
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| - Steps: 200
|
| - LoRA: r=16, alpha=32, target_modules=`all-linear`
|
| - Trainable parameters: 50,499,584
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| - 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 |
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| | 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.
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|
|
| This adapter is for benign coding assistance only. It was not trained on malware, phishing, exploit, credential theft, evasion, or destructive automation examples.
|
|
|
