Instructions to use dmitchelljackson/cerebellum-e4b-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use dmitchelljackson/cerebellum-e4b-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, "dmitchelljackson/cerebellum-e4b-lora") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c62336ad134cad6f154d84eb0e5a5fa9ca17cd665ef3ba5ac4fd02b1486760b4
- Size of remote file:
- 32.2 MB
- SHA256:
- cc8d3a0ce36466ccc1278bf987df5f71db1719b9ca6b4118264f45cb627bfe0f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.