Instructions to use nbninh/e2cdeeef-12b2-4bd0-83a5-a873fa05d2f5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nbninh/e2cdeeef-12b2-4bd0-83a5-a873fa05d2f5 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/GPT4-x-Vicuna-13b-fp16") model = PeftModel.from_pretrained(base_model, "nbninh/e2cdeeef-12b2-4bd0-83a5-a873fa05d2f5") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from nbninh/e2cdeeef-12b2-4bd0-83a5-a873fa05d2f5: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/nbninh/e2cdeeef-12b2-4bd0-83a5-a873fa05d2f5/resolve/main/training_args.bin
- Command line
-
hf download hf://nbninh/e2cdeeef-12b2-4bd0-83a5-a873fa05d2f5/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/nbninh/e2cdeeef-12b2-4bd0-83a5-a873fa05d2f5/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- 4252ebdad9375ba0be4d07e5cf6023b0ef55f0439bd8a6dd5ae1e946575d51bf
- Size of remote file:
- 6.78 kB
- SHA256:
- cf88ff4a559217022716a5e02974e95456ffed738af139c933d45ae0cb541544
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