Instructions to use minhnguyennnnnn/b65de97c-9daf-4b3b-9855-e61effcc34f1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use minhnguyennnnnn/b65de97c-9daf-4b3b-9855-e61effcc34f1 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/llama-3-8b-Instruct") model = PeftModel.from_pretrained(base_model, "minhnguyennnnnn/b65de97c-9daf-4b3b-9855-e61effcc34f1") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from minhnguyennnnnn/b65de97c-9daf-4b3b-9855-e61effcc34f1: direct link, hf CLI and curl.
- Browser
- Download file 84 MB
-
https://huggingface.co/minhnguyennnnnn/b65de97c-9daf-4b3b-9855-e61effcc34f1/resolve/main/adapter_model.bin
- Command line
-
hf download hf://minhnguyennnnnn/b65de97c-9daf-4b3b-9855-e61effcc34f1/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/minhnguyennnnnn/b65de97c-9daf-4b3b-9855-e61effcc34f1/resolve/main/adapter_model.bin
84 MB
- Xet hash:
- d08442b661be1994145026b028875abcb11817c6a5e088314c1570926670f64e
- Size of remote file:
- 84 MB
- SHA256:
- ec281a918628a12331ffb4153e2a4ed2adde037ffea22a4edb31e31b2da13a82
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