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
File size: 441 Bytes
4232857 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | {
"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"
}
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