Instructions to use cimol/daae6fca-7305-4b21-9b88-210da89e28b3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use cimol/daae6fca-7305-4b21-9b88-210da89e28b3 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("VAGOsolutions/Llama-3.1-SauerkrautLM-8b-Instruct") model = PeftModel.from_pretrained(base_model, "cimol/daae6fca-7305-4b21-9b88-210da89e28b3") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.safetensors from cimol/daae6fca-7305-4b21-9b88-210da89e28b3: direct link, hf CLI and curl.
- Browser
- Download file 671 MB
-
https://huggingface.co/cimol/daae6fca-7305-4b21-9b88-210da89e28b3/resolve/main/adapter_model.safetensors
- Command line
-
hf download hf://cimol/daae6fca-7305-4b21-9b88-210da89e28b3/adapter_model.safetensors
-
curl -L -o adapter_model.safetensors https://huggingface.co/cimol/daae6fca-7305-4b21-9b88-210da89e28b3/resolve/main/adapter_model.safetensors
671 MB
- Xet hash:
- 2f8c3ed792ec86465ab0c05eef46ab0ba88a5351acc829092b29a6fb2a66d02c
- Size of remote file:
- 671 MB
- SHA256:
- 08fc4a3c61a933085be554d6975d585a19be3e9eb4c7dbb17deae78dace90ad6
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