Instructions to use dimasik87/4d203fd0-5ccd-4671-89b2-ae0d228f8de0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use dimasik87/4d203fd0-5ccd-4671-89b2-ae0d228f8de0 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, "dimasik87/4d203fd0-5ccd-4671-89b2-ae0d228f8de0") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/optimizer.pt from dimasik87/4d203fd0-5ccd-4671-89b2-ae0d228f8de0: direct link, hf CLI and curl.
- Browser
- Download file 134 kB
-
https://huggingface.co/dimasik87/4d203fd0-5ccd-4671-89b2-ae0d228f8de0/resolve/main/last-checkpoint/optimizer.pt
- Command line
-
hf download hf://dimasik87/4d203fd0-5ccd-4671-89b2-ae0d228f8de0/last-checkpoint/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/dimasik87/4d203fd0-5ccd-4671-89b2-ae0d228f8de0/resolve/main/last-checkpoint/optimizer.pt
134 kB
- Xet hash:
- 04ebe030812faa20821e4bbcf4d190fc2b3a237fd61fe8e30255e3ed5de91828
- Size of remote file:
- 134 kB
- SHA256:
- cd2bac5e2fb822f1f5e25d15cdf0a86876b8a6baed39443dac373db9ced0506b
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