Instructions to use arvindcr4/llama-3.2-1b-distillation-offpolicy-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use arvindcr4/llama-3.2-1b-distillation-offpolicy-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B") model = PeftModel.from_pretrained(base_model, "arvindcr4/llama-3.2-1b-distillation-offpolicy-lora") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0eb58c3d34934c28e5e1a402fe654f52e5da70eb9824945fc86c2f91ef9b3ef7
- Size of remote file:
- 107 MB
- SHA256:
- 29533dbcaaebadc08dff7a9463e3264259d17bf84c5fbd0d4f07505dcad7b5df
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.