Instructions to use nblinh63/696d40f2-b496-4458-b10d-52b1906fe53d with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nblinh63/696d40f2-b496-4458-b10d-52b1906fe53d 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, "nblinh63/696d40f2-b496-4458-b10d-52b1906fe53d") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from nblinh63/696d40f2-b496-4458-b10d-52b1906fe53d: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/nblinh63/696d40f2-b496-4458-b10d-52b1906fe53d/resolve/main/training_args.bin
- Command line
-
hf download hf://nblinh63/696d40f2-b496-4458-b10d-52b1906fe53d/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/nblinh63/696d40f2-b496-4458-b10d-52b1906fe53d/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- 6bf28df4522d108e3a0f8b6aed8d77dcb29e3aba68e59748820c777e12bba968
- Size of remote file:
- 6.78 kB
- SHA256:
- 8dee859a14a594f266101b3822c9d2909fd29bcad3be3853a3b8e4b314a9d426
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