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 adapter_model.bin from dimasik87/07e17751-ab33-467b-b4a3-c3d1a9b3ecbe: direct link, hf CLI and curl.
- Browser
- Download file 336 MB
-
https://huggingface.co/dimasik87/07e17751-ab33-467b-b4a3-c3d1a9b3ecbe/resolve/main/adapter_model.bin
- Command line
-
hf download hf://dimasik87/07e17751-ab33-467b-b4a3-c3d1a9b3ecbe/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/dimasik87/07e17751-ab33-467b-b4a3-c3d1a9b3ecbe/resolve/main/adapter_model.bin
336 MB
- Xet hash:
- 94c738fea56cdafd7fe3832ace1f2b1ef890b64551ef422245947090691395c0
- Size of remote file:
- 336 MB
- SHA256:
- 0e1e22a7c24473ae2ce03415b098f515aeed520202a76c93116fabb734bf0b47
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