Instructions to use nblinh/d9d9d183-3847-4d82-a423-f5719375e8dc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nblinh/d9d9d183-3847-4d82-a423-f5719375e8dc with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2-7B-Instruct") model = PeftModel.from_pretrained(base_model, "nblinh/d9d9d183-3847-4d82-a423-f5719375e8dc") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from nblinh/d9d9d183-3847-4d82-a423-f5719375e8dc: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/nblinh/d9d9d183-3847-4d82-a423-f5719375e8dc/resolve/main/tokenizer.json
- Command line
-
hf download hf://nblinh/d9d9d183-3847-4d82-a423-f5719375e8dc/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/nblinh/d9d9d183-3847-4d82-a423-f5719375e8dc/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- 7a29a10eb51dba880581668152ececbf947c03360019a36a6de21bb576bdd7e6
- Size of remote file:
- 11.4 MB
- SHA256:
- bcfe42da0a4497e8b2b172c1f9f4ec423a46dc12907f4349c55025f670422ba9
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