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: 295 Bytes
1689294 | 1 2 3 4 5 6 7 8 9 10 11 12 | # Gemma-4-E4B-it-Coder 50-Problem Eval
Executable HumanEval subset: 50 tasks.
| Metric | Base | Coder |
|---|---:|---:|
| Pass count | 34 / 50 | 42 / 50 |
| Absolute lift | - | 16.0 pp |
| Relative pass-count lift | - | 23.53% |
Full generated code is in `eval50_before_after_full_code.csv`.
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