Instructions to use nblinh63/56151d9e-4d31-478c-ae4c-50c1fab59312 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nblinh63/56151d9e-4d31-478c-ae4c-50c1fab59312 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/SmolLM2-135M") model = PeftModel.from_pretrained(base_model, "nblinh63/56151d9e-4d31-478c-ae4c-50c1fab59312") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4dd7c95518ba1f0e25b138fa951123c29ded6c9037cdb781010d503696acf7c9
- Size of remote file:
- 9.92 MB
- SHA256:
- 104a566c15087566ec01ec4c71d972347b418e6914b58c48d7d2a2fe99e4aa63
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.