Instructions to use timzb/dbl_pretrained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use timzb/dbl_pretrained with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("TinyPixel/Llama-2-7B-bf16-sharded") model = PeftModel.from_pretrained(base_model, "timzb/dbl_pretrained") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from timzb/dbl_pretrained: direct link, hf CLI and curl.
- Browser
- Download file 134 MB
-
https://huggingface.co/timzb/dbl_pretrained/resolve/refs%2Fpr%2F2/adapter_model.bin
- Command line
-
hf download hf://timzb/dbl_pretrained@refs/pr/2/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/timzb/dbl_pretrained/resolve/refs%2Fpr%2F2/adapter_model.bin
134 MB
- Xet hash:
- e02c1deca2ee06540ceeba9cb62f8bc504ea95047a17224df03818ae9b14d03e
- Size of remote file:
- 134 MB
- SHA256:
- e641a9b9b4ee5de3669e6b71fdab0e72fbea1c6f5c0f6d3d019f557ffed2d75a
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