Instructions to use dimasik2987/06ab5930-89f9-45c2-951e-9af69b1e5c61 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use dimasik2987/06ab5930-89f9-45c2-951e-9af69b1e5c61 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Hermes-2-Pro-Mistral-7B") model = PeftModel.from_pretrained(base_model, "dimasik2987/06ab5930-89f9-45c2-951e-9af69b1e5c61") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from dimasik2987/06ab5930-89f9-45c2-951e-9af69b1e5c61: direct link, hf CLI and curl.
- Browser
- Download file 168 MB
-
https://huggingface.co/dimasik2987/06ab5930-89f9-45c2-951e-9af69b1e5c61/resolve/main/adapter_model.bin
- Command line
-
hf download hf://dimasik2987/06ab5930-89f9-45c2-951e-9af69b1e5c61/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/dimasik2987/06ab5930-89f9-45c2-951e-9af69b1e5c61/resolve/main/adapter_model.bin
168 MB
- Xet hash:
- e1dc9c1ccbffd2956de11be0c20b4ae215dc71a8c46d13cec1712c3cbbe1c95d
- Size of remote file:
- 168 MB
- SHA256:
- 6656dc8429316e09ce9fa5126927358f8ceb1190c77cc015ad97adf5fd038249
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