Instructions to use nblinh63/f7c7e844-5bfb-4d03-8f00-aebcbd671838 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nblinh63/f7c7e844-5bfb-4d03-8f00-aebcbd671838 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2.5-14B-Instruct") model = PeftModel.from_pretrained(base_model, "nblinh63/f7c7e844-5bfb-4d03-8f00-aebcbd671838") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bb644699a4cd30aa6b2b1556badac7796275129c706e64cc88b091f4b0667f8c
- Size of remote file:
- 275 MB
- SHA256:
- abff029bd605e80fd63934394efe45078eecff62a2dd1552e909b0c13789555b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.