Instructions to use adammandic87/4e083127-f89f-4cd5-8955-22c2c6da80f8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use adammandic87/4e083127-f89f-4cd5-8955-22c2c6da80f8 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Nous-Capybara-7B-V1") model = PeftModel.from_pretrained(base_model, "adammandic87/4e083127-f89f-4cd5-8955-22c2c6da80f8") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.model from adammandic87/4e083127-f89f-4cd5-8955-22c2c6da80f8: direct link, hf CLI and curl.
- Browser
- Download file 500 kB
-
https://huggingface.co/adammandic87/4e083127-f89f-4cd5-8955-22c2c6da80f8/resolve/main/tokenizer.model
- Command line
-
hf download hf://adammandic87/4e083127-f89f-4cd5-8955-22c2c6da80f8/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/adammandic87/4e083127-f89f-4cd5-8955-22c2c6da80f8/resolve/main/tokenizer.model
500 kB
- Xet hash:
- 409b63d0f14ab5da7909ddcb93f85878ef0e4f19bfa91194b68ac5b45a5b6e87
- Size of remote file:
- 500 kB
- SHA256:
- 9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
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