Instructions to use dimasik87/9b06754b-c3bf-48e8-a79c-290aed6f0710 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use dimasik87/9b06754b-c3bf-48e8-a79c-290aed6f0710 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("echarlaix/tiny-random-mistral") model = PeftModel.from_pretrained(base_model, "dimasik87/9b06754b-c3bf-48e8-a79c-290aed6f0710") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from dimasik87/9b06754b-c3bf-48e8-a79c-290aed6f0710: direct link, hf CLI and curl.
- Browser
- Download file 120 kB
-
https://huggingface.co/dimasik87/9b06754b-c3bf-48e8-a79c-290aed6f0710/resolve/main/adapter_model.bin
- Command line
-
hf download hf://dimasik87/9b06754b-c3bf-48e8-a79c-290aed6f0710/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/dimasik87/9b06754b-c3bf-48e8-a79c-290aed6f0710/resolve/main/adapter_model.bin
120 kB
- Xet hash:
- baf1f687955b94947b9516c0001f8ede25019c8e145fbfa4047be31157fa07c9
- Size of remote file:
- 120 kB
- SHA256:
- 806da755758c6551ede533f2e65f7948566f4003a15ec59774981330dcfd1d6e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.