Instructions to use shibajustfor/f08871d5-ea5c-4a83-bdea-f80c95ddeec4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use shibajustfor/f08871d5-ea5c-4a83-bdea-f80c95ddeec4 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Meta-Llama-3.1-8B") model = PeftModel.from_pretrained(base_model, "shibajustfor/f08871d5-ea5c-4a83-bdea-f80c95ddeec4") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from shibajustfor/f08871d5-ea5c-4a83-bdea-f80c95ddeec4: direct link, hf CLI and curl.
- Browser
- Download file 17.2 MB
-
https://huggingface.co/shibajustfor/f08871d5-ea5c-4a83-bdea-f80c95ddeec4/resolve/main/tokenizer.json
- Command line
-
hf download hf://shibajustfor/f08871d5-ea5c-4a83-bdea-f80c95ddeec4/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/shibajustfor/f08871d5-ea5c-4a83-bdea-f80c95ddeec4/resolve/main/tokenizer.json
17.2 MB
- Xet hash:
- da070f8626ee36c29d81d14f7a18b0a943ba6477aaa86b433d0f865f98bf8392
- Size of remote file:
- 17.2 MB
- SHA256:
- 6b9e4e7fb171f92fd137b777cc2714bf87d11576700a1dcd7a399e7bbe39537b
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