Instructions to use dzanbek/44a6e49a-46bd-4a32-aff0-79d826d03168 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use dzanbek/44a6e49a-46bd-4a32-aff0-79d826d03168 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/llama-3-8b") model = PeftModel.from_pretrained(base_model, "dzanbek/44a6e49a-46bd-4a32-aff0-79d826d03168") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from dzanbek/44a6e49a-46bd-4a32-aff0-79d826d03168: direct link, hf CLI and curl.
- Browser
- Download file 84 MB
-
https://huggingface.co/dzanbek/44a6e49a-46bd-4a32-aff0-79d826d03168/resolve/main/adapter_model.bin
- Command line
-
hf download hf://dzanbek/44a6e49a-46bd-4a32-aff0-79d826d03168/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/dzanbek/44a6e49a-46bd-4a32-aff0-79d826d03168/resolve/main/adapter_model.bin
84 MB
- Xet hash:
- 8e9079a67bd3ce25c717bc375c680ba019af40c059fa3726c519a56109f22111
- Size of remote file:
- 84 MB
- SHA256:
- 91865d59d7f664ff66d61ecd26cd3692663dfab897270abb1aed931435c279d7
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