Instructions to use 0x1202/d940089c-f8d1-402f-a97d-5811abed1771 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use 0x1202/d940089c-f8d1-402f-a97d-5811abed1771 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Hermes-2-Pro-Llama-3-8B") model = PeftModel.from_pretrained(base_model, "0x1202/d940089c-f8d1-402f-a97d-5811abed1771") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from 0x1202/d940089c-f8d1-402f-a97d-5811abed1771: direct link, hf CLI and curl.
- Browser
- Download file 6.84 kB
-
https://huggingface.co/0x1202/d940089c-f8d1-402f-a97d-5811abed1771/resolve/main/training_args.bin
- Command line
-
hf download hf://0x1202/d940089c-f8d1-402f-a97d-5811abed1771/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/0x1202/d940089c-f8d1-402f-a97d-5811abed1771/resolve/main/training_args.bin
6.84 kB
- Xet hash:
- 05445c76938b3061ce00dfb433682fe478abd5943cea7f5d4b2e08d96a32834a
- Size of remote file:
- 6.84 kB
- SHA256:
- b429360b6927b8d337ef23cf1457b468f7cec41c650c2fa83db7e2724add4142
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