Instructions to use datlaaaaaaa/014c8a80-2628-4188-b6c0-59e062fa8c5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use datlaaaaaaa/014c8a80-2628-4188-b6c0-59e062fa8c5b 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, "datlaaaaaaa/014c8a80-2628-4188-b6c0-59e062fa8c5b") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from datlaaaaaaa/014c8a80-2628-4188-b6c0-59e062fa8c5b: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/datlaaaaaaa/014c8a80-2628-4188-b6c0-59e062fa8c5b/resolve/main/training_args.bin
- Command line
-
hf download hf://datlaaaaaaa/014c8a80-2628-4188-b6c0-59e062fa8c5b/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/datlaaaaaaa/014c8a80-2628-4188-b6c0-59e062fa8c5b/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- 73d98a8c91dad00c8b5aac2cb472dabed0390ae6220e8f19edacedeb60186719
- Size of remote file:
- 6.78 kB
- SHA256:
- 4e99f4a2622c2a0ee144b5dafc468fbf328ee817addbb3dbab7fe4325ab862cc
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