Instructions to use dzanbek/3a4c989d-58aa-4e90-8981-72e68d1e9566 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use dzanbek/3a4c989d-58aa-4e90-8981-72e68d1e9566 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Intel/neural-chat-7b-v3-3") model = PeftModel.from_pretrained(base_model, "dzanbek/3a4c989d-58aa-4e90-8981-72e68d1e9566") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from dzanbek/3a4c989d-58aa-4e90-8981-72e68d1e9566: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/dzanbek/3a4c989d-58aa-4e90-8981-72e68d1e9566/resolve/main/training_args.bin
- Command line
-
hf download hf://dzanbek/3a4c989d-58aa-4e90-8981-72e68d1e9566/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/dzanbek/3a4c989d-58aa-4e90-8981-72e68d1e9566/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- 71f8e1288d6938758fd3cd554320b1c4c4618c939d982570ab6e381846673790
- Size of remote file:
- 6.78 kB
- SHA256:
- 668889dac951479bea53cb41bda5c11b8bba21f185bfb18c71aeda45c9b5b09b
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