Instructions to use tarabukinivan/b2f31e98-cc78-4404-9a42-7aa45e01ae0e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use tarabukinivan/b2f31e98-cc78-4404-9a42-7aa45e01ae0e with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Hermes-2-Pro-Mistral-7B") model = PeftModel.from_pretrained(base_model, "tarabukinivan/b2f31e98-cc78-4404-9a42-7aa45e01ae0e") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from tarabukinivan/b2f31e98-cc78-4404-9a42-7aa45e01ae0e: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/tarabukinivan/b2f31e98-cc78-4404-9a42-7aa45e01ae0e/resolve/main/training_args.bin
- Command line
-
hf download hf://tarabukinivan/b2f31e98-cc78-4404-9a42-7aa45e01ae0e/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/tarabukinivan/b2f31e98-cc78-4404-9a42-7aa45e01ae0e/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- ac04ae31a83113c6e5783b81ba0088d8cfeb15a442c48198fdfc3c5190776b8f
- Size of remote file:
- 6.78 kB
- SHA256:
- bfc252ccb952d7550dba28d7298cdbae0e9956991b2a742a53741fbdc61151c3
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