Instructions to use datlaaaaaaa/014c8a80-2628-4188-b6c0-59e062fa8c5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use datlaaaaaaa/014c8a80-2628-4188-b6c0-59e062fa8c5b with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/GPT4-x-Vicuna-13b-fp16") model = PeftModel.from_pretrained(base_model, "datlaaaaaaa/014c8a80-2628-4188-b6c0-59e062fa8c5b") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.safetensors from datlaaaaaaa/014c8a80-2628-4188-b6c0-59e062fa8c5b: direct link, hf CLI and curl.
- Browser
- Download file 125 MB
-
https://huggingface.co/datlaaaaaaa/014c8a80-2628-4188-b6c0-59e062fa8c5b/resolve/main/adapter_model.safetensors
- Command line
-
hf download hf://datlaaaaaaa/014c8a80-2628-4188-b6c0-59e062fa8c5b/adapter_model.safetensors
-
curl -L -o adapter_model.safetensors https://huggingface.co/datlaaaaaaa/014c8a80-2628-4188-b6c0-59e062fa8c5b/resolve/main/adapter_model.safetensors
125 MB
- Xet hash:
- acf05d45b6a66823ca549f3fe306bb23e13ab10f9e2771cbf885942f75b5895a
- Size of remote file:
- 125 MB
- SHA256:
- 2efa16e6a0c2736422d54fd19bcbbdcdde03a1f378d8e44c07c76012ff9cba77
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