Instructions to use hongngo/9e92ebc0-fbc6-4826-a6cb-7c1a89ae5e8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use hongngo/9e92ebc0-fbc6-4826-a6cb-7c1a89ae5e8b with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("The-matt/llama2_ko-7b_distinctive-snowflake-182_1060") model = PeftModel.from_pretrained(base_model, "hongngo/9e92ebc0-fbc6-4826-a6cb-7c1a89ae5e8b") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from hongngo/9e92ebc0-fbc6-4826-a6cb-7c1a89ae5e8b: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/hongngo/9e92ebc0-fbc6-4826-a6cb-7c1a89ae5e8b/resolve/main/training_args.bin
- Command line
-
hf download hf://hongngo/9e92ebc0-fbc6-4826-a6cb-7c1a89ae5e8b/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/hongngo/9e92ebc0-fbc6-4826-a6cb-7c1a89ae5e8b/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- 4807c2d1873d650590f35581d4446e8b270ff2d2f74e86f41e7e7fe52d88d263
- Size of remote file:
- 6.78 kB
- SHA256:
- 0690e8674550d41e32f2aba1025d91b8dc69f6e8b0c7b76183a101ad5b168d1b
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