Instructions to use coldcurrent/encinitas-gemma4-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use coldcurrent/encinitas-gemma4-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/gemma-4-26B-A4B-it") model = PeftModel.from_pretrained(base_model, "coldcurrent/encinitas-gemma4-lora") - Notebooks
- Google Colab
- Kaggle
| { | |
| "base_model_name_or_path": "google/gemma-4-26B-A4B-it", | |
| "bias": "none", | |
| "fan_in_fan_out": false, | |
| "inference_mode": true, | |
| "lora_alpha": 32, | |
| "lora_dropout": 0.0, | |
| "r": 16, | |
| "target_modules": [ | |
| "base_layer", | |
| "down_proj", | |
| "experts", | |
| "gate_proj", | |
| "k_proj", | |
| "o_proj", | |
| "q_proj", | |
| "up_proj", | |
| "v_proj" | |
| ], | |
| "task_type": "CAUSAL_LM", | |
| "peft_type": "LORA", | |
| "fw_lora_layout": "fused_peft_3d_v1" | |
| } | |