Instructions to use prxy5605/3ba213a5-5d29-4b28-99f9-93f3cb37266a with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use prxy5605/3ba213a5-5d29-4b28-99f9-93f3cb37266a with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-2-2b-it") model = PeftModel.from_pretrained(base_model, "prxy5605/3ba213a5-5d29-4b28-99f9-93f3cb37266a") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from prxy5605/3ba213a5-5d29-4b28-99f9-93f3cb37266a: direct link, hf CLI and curl.
- Browser
- Download file 6.84 kB
-
https://huggingface.co/prxy5605/3ba213a5-5d29-4b28-99f9-93f3cb37266a/resolve/main/training_args.bin
- Command line
-
hf download hf://prxy5605/3ba213a5-5d29-4b28-99f9-93f3cb37266a/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/prxy5605/3ba213a5-5d29-4b28-99f9-93f3cb37266a/resolve/main/training_args.bin
6.84 kB
- Xet hash:
- 0704a4dd3d98454c83af204797a86bf2cf0178200059a71df423b01e7fb41362
- Size of remote file:
- 6.84 kB
- SHA256:
- df4c80b24ee9e5a834278b7a536010f2b46a985a58375bec01b8d36e4ba4e2fa
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