Instructions to use phungkhaccuong/5bae37f6-b101-4e0d-9d28-e948bfca3fe1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use phungkhaccuong/5bae37f6-b101-4e0d-9d28-e948bfca3fe1 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Nous-Hermes-2-Mistral-7B-DPO") model = PeftModel.from_pretrained(base_model, "phungkhaccuong/5bae37f6-b101-4e0d-9d28-e948bfca3fe1") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from phungkhaccuong/5bae37f6-b101-4e0d-9d28-e948bfca3fe1: direct link, hf CLI and curl.
- Browser
- Download file 84 MB
-
https://huggingface.co/phungkhaccuong/5bae37f6-b101-4e0d-9d28-e948bfca3fe1/resolve/main/adapter_model.bin
- Command line
-
hf download hf://phungkhaccuong/5bae37f6-b101-4e0d-9d28-e948bfca3fe1/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/phungkhaccuong/5bae37f6-b101-4e0d-9d28-e948bfca3fe1/resolve/main/adapter_model.bin
84 MB
- Xet hash:
- 145d49b90a8376b76225dfe2ddbdfca1fd222b5367baa09766ebf1183451b21e
- Size of remote file:
- 84 MB
- SHA256:
- 6f8d8110fb978094d644791bf474a278281659e62df7849fda24dc48951a62bb
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