Instructions to use datlaaaaaaa/2eee0883-3511-4f7b-aa1b-8f166beae601 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use datlaaaaaaa/2eee0883-3511-4f7b-aa1b-8f166beae601 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, "datlaaaaaaa/2eee0883-3511-4f7b-aa1b-8f166beae601") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from datlaaaaaaa/2eee0883-3511-4f7b-aa1b-8f166beae601: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/datlaaaaaaa/2eee0883-3511-4f7b-aa1b-8f166beae601/resolve/main/training_args.bin
- Command line
-
hf download hf://datlaaaaaaa/2eee0883-3511-4f7b-aa1b-8f166beae601/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/datlaaaaaaa/2eee0883-3511-4f7b-aa1b-8f166beae601/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- 3e561c73750ef704a6f1fbaa3121b9e822a743c6afb522a6bb53a0644729a406
- Size of remote file:
- 6.78 kB
- SHA256:
- 5f35f3520f11294a378a15b316a65b0a28fe59423d322a86ccfd4f93df1908c0
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