Instructions to use josephmayo/gemma-4-E4B-it-coding-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use josephmayo/gemma-4-E4B-it-coding-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/gemma-4-E4B-it") model = PeftModel.from_pretrained(base_model, "josephmayo/gemma-4-E4B-it-coding-lora") - Notebooks
- Google Colab
- Kaggle
File size: 769 Bytes
db84f1e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | {
"benchmark": "HumanEval executable subset",
"evaluated_tasks": 8,
"task_selection": "first 8 HumanEval tasks",
"before_pass": 5,
"after_pass": 7,
"absolute_pass_rate_before": 0.625,
"absolute_pass_rate_after": 0.875,
"absolute_percentage_point_delta": 25.0,
"relative_pass_count_increase_percent": 40.0,
"scope_reason": "Kaggle GPU-hour budget was exhausted during training, merge preparation, and upload validation, so the public executable proof was kept to a small reproducible subset.",
"artifact_note": "eval_before_after.csv preserves scored output previews, not full generated code. executable_eval.json is the preserved pass/fail proof artifact. Future runs should save full generated completions in eval_before_after_full.jsonl."
}
|