Instructions to use nbeerbower/Qwen3.6-27B-delegation-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nbeerbower/Qwen3.6-27B-delegation-LoRA with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("nbeerbower/Qwen3.6-27B-TIES") model = PeftModel.from_pretrained(base_model, "nbeerbower/Qwen3.6-27B-delegation-LoRA") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0a4f2ce58b2800be183eac7cdab238760a2a0fd8e4cc480d632b54013f7032e7
- Size of remote file:
- 638 MB
- SHA256:
- 491160a113b8a64a28cd4ba9fcc27ac65d375aca4b76d59ba9f6324271910cdc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.