Instructions to use vermoney/3977765e-3e57-4711-8d6a-d40156acdd0c with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use vermoney/3977765e-3e57-4711-8d6a-d40156acdd0c with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/llama-3-8b") model = PeftModel.from_pretrained(base_model, "vermoney/3977765e-3e57-4711-8d6a-d40156acdd0c") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from vermoney/3977765e-3e57-4711-8d6a-d40156acdd0c: direct link, hf CLI and curl.
- Browser
- Download file 6.84 kB
-
https://huggingface.co/vermoney/3977765e-3e57-4711-8d6a-d40156acdd0c/resolve/main/training_args.bin
- Command line
-
hf download hf://vermoney/3977765e-3e57-4711-8d6a-d40156acdd0c/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/vermoney/3977765e-3e57-4711-8d6a-d40156acdd0c/resolve/main/training_args.bin
6.84 kB
- Xet hash:
- f55cb0281e592a5ee6baf4b2de9421542f23493c79d033e3d0b7ebb172b93c46
- Size of remote file:
- 6.84 kB
- SHA256:
- 21a77cf1499907d9aac94fca5db01b9b3c9dcd9f51aa9c6ce55e7457a6262505
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