Instructions to use dzanbek/3a4c989d-58aa-4e90-8981-72e68d1e9566 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use dzanbek/3a4c989d-58aa-4e90-8981-72e68d1e9566 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Intel/neural-chat-7b-v3-3") model = PeftModel.from_pretrained(base_model, "dzanbek/3a4c989d-58aa-4e90-8981-72e68d1e9566") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from dzanbek/3a4c989d-58aa-4e90-8981-72e68d1e9566: direct link, hf CLI and curl.
- Browser
- Download file 336 MB
-
https://huggingface.co/dzanbek/3a4c989d-58aa-4e90-8981-72e68d1e9566/resolve/main/adapter_model.bin
- Command line
-
hf download hf://dzanbek/3a4c989d-58aa-4e90-8981-72e68d1e9566/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/dzanbek/3a4c989d-58aa-4e90-8981-72e68d1e9566/resolve/main/adapter_model.bin
336 MB
- Xet hash:
- 629c42276f7048eb331d126ecaa1ec105bd3af1314e3ea6e91f0781bc3b79908
- Size of remote file:
- 336 MB
- SHA256:
- becf4611c417603204b437d43971873b9e5ad0607fe8b7505cd21e5f1ca823f2
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