Instructions to use shibajustfor/a47e25a5-5f3e-4067-aaa4-6d88e1aa51c2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use shibajustfor/a47e25a5-5f3e-4067-aaa4-6d88e1aa51c2 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("HuggingFaceH4/tiny-random-LlamaForCausalLM") model = PeftModel.from_pretrained(base_model, "shibajustfor/a47e25a5-5f3e-4067-aaa4-6d88e1aa51c2") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.model from shibajustfor/a47e25a5-5f3e-4067-aaa4-6d88e1aa51c2: direct link, hf CLI and curl.
- Browser
- Download file 500 kB
-
https://huggingface.co/shibajustfor/a47e25a5-5f3e-4067-aaa4-6d88e1aa51c2/resolve/main/tokenizer.model
- Command line
-
hf download hf://shibajustfor/a47e25a5-5f3e-4067-aaa4-6d88e1aa51c2/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/shibajustfor/a47e25a5-5f3e-4067-aaa4-6d88e1aa51c2/resolve/main/tokenizer.model
500 kB
- Xet hash:
- 91bf184ab12793d0754344f9095332759432e666320cc6c07f637af50e36db6f
- Size of remote file:
- 500 kB
- SHA256:
- 9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
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