---
base_model: google/gemma-4-E4B-it
library_name: transformers
license: apache-2.0
tags:
- gemma4
- coder
- coding
- merged-lora
- kaggle-proof
---
# Gemma 4 E4B IT Coder
This is the full merged coding-tuned model from `google/gemma-4-E4B-it` plus the
LoRA adapter `josephmayo/gemma-4-E4B-it-coding-lora`.
The release is not adapter-only: the LoRA deltas were merged directly into the
base safetensors and uploaded as a normal Transformers model.
## Training Proof
Training ran on Kaggle with 2x Tesla T4 GPUs.
| Item | Value |
|---|---:|
| Safe coding rows | 1024 |
| LoRA steps | 200 |
| LoRA rank | 16 |
| LoRA alpha | 32 |
| Trainable parameters | 50,499,584 |
| Final train loss | 1.1427 |
| Merged LoRA tensors applied | 592/592 |
| Missing LoRA targets | 0 |
| Merged safetensor shards | 5 |

## HumanEval Results (50-Problem Subset)
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 |
|---|---:|---:|
| Pass count | 34 / 50 | 42 / 50 |
| Absolute lift | - | +16.0 pp |
| Relative pass-count lift | - | +23.53% |

Proof files included in this repo:
- `eval50_summary.json`: 50-problem HumanEval executable result.
- `eval50_before_after_full_code.csv`: full generated before/after code for all 50 tasks.
- `EVAL50_README.md`: evaluation methodology and scope.
- `nvidia_smi.txt`: GPU environment proof.
- `eval_before_after.csv`: fixed before/after coding prompt scores with output previews.
- `trainer_log_history.json`: training loss and runtime logs.
- `merge_manifest.json`: direct merge record, including 592 applied LoRA tensors and 0 missing targets.
- `model.safetensors.index.json`: shard index for the full merged model.

This model is for benign coding assistance only. The training filter removed
malware, phishing, exploit, credential theft, evasion, and destructive automation
examples.