Instructions to use tryingpro/fff4bb41-fbd1-4606-80cd-29d5047ddf58 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use tryingpro/fff4bb41-fbd1-4606-80cd-29d5047ddf58 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Orenguteng/Llama-3-8B-Lexi-Uncensored") model = PeftModel.from_pretrained(base_model, "tryingpro/fff4bb41-fbd1-4606-80cd-29d5047ddf58") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from tryingpro/fff4bb41-fbd1-4606-80cd-29d5047ddf58: direct link, hf CLI and curl.
- Browser
- Download file 17.2 MB
-
https://huggingface.co/tryingpro/fff4bb41-fbd1-4606-80cd-29d5047ddf58/resolve/7af8a7ca297458ad3f408c4f7d1a539bc93bd153/tokenizer.json
- Command line
-
hf download hf://tryingpro/fff4bb41-fbd1-4606-80cd-29d5047ddf58@7af8a7ca297458ad3f408c4f7d1a539bc93bd153/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/tryingpro/fff4bb41-fbd1-4606-80cd-29d5047ddf58/resolve/7af8a7ca297458ad3f408c4f7d1a539bc93bd153/tokenizer.json
17.2 MB
- Xet hash:
- 3f8ab02ab5ba981c86390c96097b4e7fab72a611608e8506b21678ceb431634f
- Size of remote file:
- 17.2 MB
- SHA256:
- 2539802b4016767e5475c0fa774895f4c33683f7c2ed9df643a61279e9ef1bd2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.