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
| # 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`. | |