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
Add evaluation scope proof note
Browse files- evaluation_scope.json +13 -0
evaluation_scope.json
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{
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"benchmark": "HumanEval executable subset",
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"evaluated_tasks": 8,
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"task_selection": "first 8 HumanEval tasks",
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"before_pass": 5,
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"after_pass": 7,
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"absolute_pass_rate_before": 0.625,
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"absolute_pass_rate_after": 0.875,
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"absolute_percentage_point_delta": 25.0,
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"relative_pass_count_increase_percent": 40.0,
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"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.",
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"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."
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}
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