Instructions to use minhnguyennnnnn/9eaac1c9-30e8-4fbc-a8b5-2ad7261c51c2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use minhnguyennnnnn/9eaac1c9-30e8-4fbc-a8b5-2ad7261c51c2 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Yarn-Solar-10b-32k") model = PeftModel.from_pretrained(base_model, "minhnguyennnnnn/9eaac1c9-30e8-4fbc-a8b5-2ad7261c51c2") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from minhnguyennnnnn/9eaac1c9-30e8-4fbc-a8b5-2ad7261c51c2: direct link, hf CLI and curl.
- Browser
- Download file 126 MB
-
https://huggingface.co/minhnguyennnnnn/9eaac1c9-30e8-4fbc-a8b5-2ad7261c51c2/resolve/main/adapter_model.bin
- Command line
-
hf download hf://minhnguyennnnnn/9eaac1c9-30e8-4fbc-a8b5-2ad7261c51c2/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/minhnguyennnnnn/9eaac1c9-30e8-4fbc-a8b5-2ad7261c51c2/resolve/main/adapter_model.bin
126 MB
- Xet hash:
- 5924321953a623844ba15d021ee1b33e5d34c5c4d61a6472455c7646d836e5fc
- Size of remote file:
- 126 MB
- SHA256:
- 5937454c9d1f7adfb7c00e96508a6e62a088ed515fb79cb1cd8fa551cb2940cf
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