Instructions to use vermoney/19b017f4-6c04-4ce7-943b-7d1eccc93a84 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use vermoney/19b017f4-6c04-4ce7-943b-7d1eccc93a84 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("peft-internal-testing/tiny-dummy-qwen2") model = PeftModel.from_pretrained(base_model, "vermoney/19b017f4-6c04-4ce7-943b-7d1eccc93a84") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from vermoney/19b017f4-6c04-4ce7-943b-7d1eccc93a84: direct link, hf CLI and curl.
- Browser
- Download file 32.6 kB
-
https://huggingface.co/vermoney/19b017f4-6c04-4ce7-943b-7d1eccc93a84/resolve/main/adapter_model.bin
- Command line
-
hf download hf://vermoney/19b017f4-6c04-4ce7-943b-7d1eccc93a84/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/vermoney/19b017f4-6c04-4ce7-943b-7d1eccc93a84/resolve/main/adapter_model.bin
32.6 kB
- Xet hash:
- bd7c3dcc6a2d6992c864f3d8a153ba4d5d7bda02883309116f9b6af5f50acd5b
- Size of remote file:
- 32.6 kB
- SHA256:
- 3ede2cc48b8ee8dafe10b19dcbb5675c73bdd64bb1d8ba5911e76a73c1abdf78
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