Instructions to use daniel40/247851d7-4271-499b-b3e6-f440cb254f99 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use daniel40/247851d7-4271-499b-b3e6-f440cb254f99 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, "daniel40/247851d7-4271-499b-b3e6-f440cb254f99") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/training_args.bin from daniel40/247851d7-4271-499b-b3e6-f440cb254f99: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/daniel40/247851d7-4271-499b-b3e6-f440cb254f99/resolve/main/last-checkpoint/training_args.bin
- Command line
-
hf download hf://daniel40/247851d7-4271-499b-b3e6-f440cb254f99/last-checkpoint/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/daniel40/247851d7-4271-499b-b3e6-f440cb254f99/resolve/main/last-checkpoint/training_args.bin
6.78 kB
- Xet hash:
- 541b5453d38380c7d465847b5c705aa1b2ebca3127ca3e152729ca8cba782a9b
- Size of remote file:
- 6.78 kB
- SHA256:
- 5390433c7af90b5bf88167f2a06aecfded1ee105d981660369f7bc07a14d34ee
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