Instructions to use Infernaught/phi_countdown_n100_mcl_256_quantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Infernaught/phi_countdown_n100_mcl_256_quantized with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3.5-mini-instruct") model = PeftModel.from_pretrained(base_model, "Infernaught/phi_countdown_n100_mcl_256_quantized") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from Infernaught/phi_countdown_n100_mcl_256_quantized: direct link, hf CLI and curl.
- Browser
- Download file 25.2 MB
-
https://huggingface.co/Infernaught/phi_countdown_n100_mcl_256_quantized/resolve/bc7c8538e289d3e4ee83e972ca7b79b010a6734f/optimizer.pt
- Command line
-
hf download hf://Infernaught/phi_countdown_n100_mcl_256_quantized@bc7c8538e289d3e4ee83e972ca7b79b010a6734f/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/Infernaught/phi_countdown_n100_mcl_256_quantized/resolve/bc7c8538e289d3e4ee83e972ca7b79b010a6734f/optimizer.pt
25.2 MB
- Xet hash:
- af517aed5fc0bbe4d0a95db68e019bb61c6c70834290e062b87a80b1246d40d0
- Size of remote file:
- 25.2 MB
- SHA256:
- 0aa7a015f9900272b43cc629886b84f926b5937d6745c5d9a19364856b3112b0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.