Instructions to use dimasik87/07e17751-ab33-467b-b4a3-c3d1a9b3ecbe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use dimasik87/07e17751-ab33-467b-b4a3-c3d1a9b3ecbe with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Llama-3.1-Storm-8B") model = PeftModel.from_pretrained(base_model, "dimasik87/07e17751-ab33-467b-b4a3-c3d1a9b3ecbe") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from dimasik87/07e17751-ab33-467b-b4a3-c3d1a9b3ecbe: direct link, hf CLI and curl.
- Browser
- Download file 17.2 MB
-
https://huggingface.co/dimasik87/07e17751-ab33-467b-b4a3-c3d1a9b3ecbe/resolve/main/tokenizer.json
- Command line
-
hf download hf://dimasik87/07e17751-ab33-467b-b4a3-c3d1a9b3ecbe/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/dimasik87/07e17751-ab33-467b-b4a3-c3d1a9b3ecbe/resolve/main/tokenizer.json
17.2 MB
- Xet hash:
- 2e56cad9ac2a38f4b7de211953881b613c19e51838c05adf8f7984c139c2e8f5
- Size of remote file:
- 17.2 MB
- SHA256:
- 6b9e4e7fb171f92fd137b777cc2714bf87d11576700a1dcd7a399e7bbe39537b
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