josephmayo

gemma-4-E4B-it-Coder

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README

License: apache-2.0

Training Proof

Training ran on Kaggle with 2x Tesla T4 GPUs.

Table
ItemValue
Safe coding rows1024
LoRA steps200
LoRA rank16
LoRA alpha32
Trainable parameters50,499,584
Final train loss1.1427
Merged LoRA tensors applied592/592
Missing LoRA targets0
Merged safetensor shards5

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.

Table
MetricBase google/gemma-4-E4B-itCoder
Pass count34 / 5042 / 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.

Evidence files

Run evidence for this release is stored in the repository under evidence/:

These files are compact local/Kaggle run artifacts used to document training, evaluation, merge, or quantization evidence for this model family.

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josephmayo

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google/gemma-4-E4B-it

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