Instructions to use lhong4759/453cdc50-41c3-4fdc-bec3-8bc352bd0f27 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use lhong4759/453cdc50-41c3-4fdc-bec3-8bc352bd0f27 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("JackFram/llama-160m") model = PeftModel.from_pretrained(base_model, "lhong4759/453cdc50-41c3-4fdc-bec3-8bc352bd0f27") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from lhong4759/453cdc50-41c3-4fdc-bec3-8bc352bd0f27: direct link, hf CLI and curl.
- Browser
- Download file 6.84 MB
-
https://huggingface.co/lhong4759/453cdc50-41c3-4fdc-bec3-8bc352bd0f27/resolve/main/adapter_model.bin
- Command line
-
hf download hf://lhong4759/453cdc50-41c3-4fdc-bec3-8bc352bd0f27/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/lhong4759/453cdc50-41c3-4fdc-bec3-8bc352bd0f27/resolve/main/adapter_model.bin
6.84 MB
- Xet hash:
- 40f1064318265bdffbbcb77e1a13f5d99fcc5cad803bf379f2df48ff69751960
- Size of remote file:
- 6.84 MB
- SHA256:
- 0c5ea2774036e36aea78e1cad0124d6d895ca37e113a1413fe4ca2cfbf513fdc
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