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
| { | |
| "base_model": "google/gemma-4-E4B-it", | |
| "coder_model": "josephmayo/gemma-4-E4B-it-Coder", | |
| "eval_count": 50, | |
| "max_new_tokens": 256, | |
| "stage": "complete", | |
| "errors": [], | |
| "cuda_available": true, | |
| "cuda_device_count": 2, | |
| "devices": [ | |
| "Tesla T4", | |
| "Tesla T4" | |
| ], | |
| "hf_token_present": false, | |
| "before_pass": 34, | |
| "after_pass": 42, | |
| "total": 50, | |
| "absolute_lift_percentage_points": 16.0, | |
| "relative_pass_count_lift_percent": 23.53, | |
| "before_avg_seconds": 35.6888, | |
| "after_avg_seconds": 40.3315 | |
| } |