Instructions to use baltop/cdp_500 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use baltop/cdp_500 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1") model = PeftModel.from_pretrained(base_model, "baltop/cdp_500") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5373cb0e7c23fc88dfcd56bdf8cd6dcc695c936fb6f90443121342c6ddd99596
- Size of remote file:
- 85.7 MB
- SHA256:
- a295eef86bf67a16dc0b6d2288debf9671a0a9b504bfea7046cbf23f538e1041
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.