Instructions to use datlaaaaaaa/9536ed21-732c-48c4-a016-44b37e59099d with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use datlaaaaaaa/9536ed21-732c-48c4-a016-44b37e59099d 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/9536ed21-732c-48c4-a016-44b37e59099d") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from datlaaaaaaa/9536ed21-732c-48c4-a016-44b37e59099d: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/datlaaaaaaa/9536ed21-732c-48c4-a016-44b37e59099d/resolve/main/training_args.bin
- Command line
-
hf download hf://datlaaaaaaa/9536ed21-732c-48c4-a016-44b37e59099d/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/datlaaaaaaa/9536ed21-732c-48c4-a016-44b37e59099d/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- 634f5fa2f192141487efb5da906a905b5b1c086ca130a27c96d2d94272b44808
- Size of remote file:
- 6.78 kB
- SHA256:
- 1cd73a45576ef71c2a65fa0293271350eb6aa1d7290279d6090fcca75fc710d0
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