Instructions to use minhnguyennnnnn/4c2040bc-c4bd-4ee4-9f60-2fdc22ef6357 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use minhnguyennnnnn/4c2040bc-c4bd-4ee4-9f60-2fdc22ef6357 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("dltjdgh0928/test_instruction") model = PeftModel.from_pretrained(base_model, "minhnguyennnnnn/4c2040bc-c4bd-4ee4-9f60-2fdc22ef6357") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from minhnguyennnnnn/4c2040bc-c4bd-4ee4-9f60-2fdc22ef6357: direct link, hf CLI and curl.
- Browser
- Download file 84 MB
-
https://huggingface.co/minhnguyennnnnn/4c2040bc-c4bd-4ee4-9f60-2fdc22ef6357/resolve/main/adapter_model.bin
- Command line
-
hf download hf://minhnguyennnnnn/4c2040bc-c4bd-4ee4-9f60-2fdc22ef6357/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/minhnguyennnnnn/4c2040bc-c4bd-4ee4-9f60-2fdc22ef6357/resolve/main/adapter_model.bin
84 MB
- Xet hash:
- 3413654415a1c87b76c19b0d9dc6ab267949cb08ec8f842730b504f8a47a241e
- Size of remote file:
- 84 MB
- SHA256:
- fbff0b75b3381d35018bb566c89bae510d8e99834d406d86a80cef9b38984213
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