Instructions to use dendimaki/mistral-checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use dendimaki/mistral-checkpoints with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1") model = PeftModel.from_pretrained(base_model, "dendimaki/mistral-checkpoints") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c18080b1629dc935658c20f6015ddbad54760fc6908ed986a26cff79a3527ae4
- Size of remote file:
- 5.11 kB
- SHA256:
- 72ceed2b72c1da66848ba2417bccbe53aae631d94bbefb30618a3de9a20468e4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.