Instructions to use daniel40/2efd9162-6476-42f9-a493-b1b9400ee85d with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use daniel40/2efd9162-6476-42f9-a493-b1b9400ee85d 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, "daniel40/2efd9162-6476-42f9-a493-b1b9400ee85d") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from daniel40/2efd9162-6476-42f9-a493-b1b9400ee85d: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/daniel40/2efd9162-6476-42f9-a493-b1b9400ee85d/resolve/main/training_args.bin
- Command line
-
hf download hf://daniel40/2efd9162-6476-42f9-a493-b1b9400ee85d/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/daniel40/2efd9162-6476-42f9-a493-b1b9400ee85d/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- 2ef6adc8358d12c5e04b5dce15ea87e74744b21382ace1f8b4a8f10b770bc6c4
- Size of remote file:
- 6.78 kB
- SHA256:
- 80f777528088a709ea76ca4229237b790cfac71b3762636845e55f1d06abefaa
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