Instructions to use denbeo/be67fc74-525f-4b84-a732-0c60d512e856 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use denbeo/be67fc74-525f-4b84-a732-0c60d512e856 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("OpenBuddy/openbuddy-llama2-13b-v8.1-fp16") model = PeftModel.from_pretrained(base_model, "denbeo/be67fc74-525f-4b84-a732-0c60d512e856") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from denbeo/be67fc74-525f-4b84-a732-0c60d512e856: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/denbeo/be67fc74-525f-4b84-a732-0c60d512e856/resolve/main/training_args.bin
- Command line
-
hf download hf://denbeo/be67fc74-525f-4b84-a732-0c60d512e856/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/denbeo/be67fc74-525f-4b84-a732-0c60d512e856/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- 76f617f316bb0936fa4334cb8670e24020a2798b62be3640afc2ddc8b4d436d2
- Size of remote file:
- 6.78 kB
- SHA256:
- a0753a62b439c1c11f19b5f17692ee561dc2d8596e07ca36da95ed4b561357c3
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