Instructions to use shibajustfor/a690348b-9d15-46c5-92f0-37a6633c8cd5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use shibajustfor/a690348b-9d15-46c5-92f0-37a6633c8cd5 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Yarn-Mistral-7b-128k") model = PeftModel.from_pretrained(base_model, "shibajustfor/a690348b-9d15-46c5-92f0-37a6633c8cd5") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from shibajustfor/a690348b-9d15-46c5-92f0-37a6633c8cd5: direct link, hf CLI and curl.
- Browser
- Download file 3.51 MB
-
https://huggingface.co/shibajustfor/a690348b-9d15-46c5-92f0-37a6633c8cd5/resolve/00df1afff4b16b66b3d8ad565dc25b14f39bfd2f/tokenizer.json
- Command line
-
hf download hf://shibajustfor/a690348b-9d15-46c5-92f0-37a6633c8cd5@00df1afff4b16b66b3d8ad565dc25b14f39bfd2f/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/shibajustfor/a690348b-9d15-46c5-92f0-37a6633c8cd5/resolve/00df1afff4b16b66b3d8ad565dc25b14f39bfd2f/tokenizer.json
3.51 MB
File too large to display, you can check the raw version instead.