Instructions to use hongngo/8473b6c9-d112-40f4-b178-9b0e6d3e5e9f with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use hongngo/8473b6c9-d112-40f4-b178-9b0e6d3e5e9f 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, "hongngo/8473b6c9-d112-40f4-b178-9b0e6d3e5e9f") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from hongngo/8473b6c9-d112-40f4-b178-9b0e6d3e5e9f: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/hongngo/8473b6c9-d112-40f4-b178-9b0e6d3e5e9f/resolve/main/training_args.bin
- Command line
-
hf download hf://hongngo/8473b6c9-d112-40f4-b178-9b0e6d3e5e9f/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/hongngo/8473b6c9-d112-40f4-b178-9b0e6d3e5e9f/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- aeb5978a7e248c60e11ed96738d09adc420b92ded377b53b1bdd75a65cdb52a5
- Size of remote file:
- 6.78 kB
- SHA256:
- 6e158182b68b1d295fb0c21ba8c3dae3461df15cd85d80410592ae2d4d397a09
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