Instructions to use lhong4759/87256efb-fd4e-4250-ae98-958ee75b436e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use lhong4759/87256efb-fd4e-4250-ae98-958ee75b436e 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, "lhong4759/87256efb-fd4e-4250-ae98-958ee75b436e") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from lhong4759/87256efb-fd4e-4250-ae98-958ee75b436e: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/lhong4759/87256efb-fd4e-4250-ae98-958ee75b436e/resolve/main/training_args.bin
- Command line
-
hf download hf://lhong4759/87256efb-fd4e-4250-ae98-958ee75b436e/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/lhong4759/87256efb-fd4e-4250-ae98-958ee75b436e/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- bc2562584484a020cf6a80b6b29755814b2d13bdf9f2d59ed878819b5acb41b3
- Size of remote file:
- 6.78 kB
- SHA256:
- 190b7baac22edc28d0a4cb90d7a33ce0f19826be301c8653d1c851f872e48731
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