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: 520 Bytes
5fd6737 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | {
"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
} |