Instructions to use datlaaaaaaa/0a230562-2b92-43a3-992f-9682d893dee4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use datlaaaaaaa/0a230562-2b92-43a3-992f-9682d893dee4 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Llama-3.1-Storm-8B") model = PeftModel.from_pretrained(base_model, "datlaaaaaaa/0a230562-2b92-43a3-992f-9682d893dee4") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from datlaaaaaaa/0a230562-2b92-43a3-992f-9682d893dee4: direct link, hf CLI and curl.
- Browser
- Download file 84 MB
-
https://huggingface.co/datlaaaaaaa/0a230562-2b92-43a3-992f-9682d893dee4/resolve/main/adapter_model.bin
- Command line
-
hf download hf://datlaaaaaaa/0a230562-2b92-43a3-992f-9682d893dee4/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/datlaaaaaaa/0a230562-2b92-43a3-992f-9682d893dee4/resolve/main/adapter_model.bin
84 MB
- Xet hash:
- 2208766d5a088d0b0cfefe695f41af405ff365a819a9c42525431dee2b9b7137
- Size of remote file:
- 84 MB
- SHA256:
- 728f27a0abfecfa8cdcae600ffae4836afb745d4dad3e291ed3a13a299597354
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