Instructions to use vdos/1c3701b4-7d0a-4c75-ad20-dc76160024ef with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use vdos/1c3701b4-7d0a-4c75-ad20-dc76160024ef with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("HuggingFaceM4/tiny-random-LlamaForCausalLM") model = PeftModel.from_pretrained(base_model, "vdos/1c3701b4-7d0a-4c75-ad20-dc76160024ef") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from vdos/1c3701b4-7d0a-4c75-ad20-dc76160024ef: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/vdos/1c3701b4-7d0a-4c75-ad20-dc76160024ef/resolve/main/training_args.bin
- Command line
-
hf download hf://vdos/1c3701b4-7d0a-4c75-ad20-dc76160024ef/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/vdos/1c3701b4-7d0a-4c75-ad20-dc76160024ef/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- d48a2b387304ca53f94f23231bf8be2d82e1d9fda327486ca5ca71871b7d7d63
- Size of remote file:
- 6.78 kB
- SHA256:
- 3991109f0c74085be4f02fd2013c92971fb000738b84863a482e3a2872ec4395
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