Instructions to use dada22231/93902ce4-614c-4d1d-95c0-ae13dc4f6f7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use dada22231/93902ce4-614c-4d1d-95c0-ae13dc4f6f7b 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, "dada22231/93902ce4-614c-4d1d-95c0-ae13dc4f6f7b") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/optimizer.pt from dada22231/93902ce4-614c-4d1d-95c0-ae13dc4f6f7b: direct link, hf CLI and curl.
- Browser
- Download file 114 kB
-
https://huggingface.co/dada22231/93902ce4-614c-4d1d-95c0-ae13dc4f6f7b/resolve/main/last-checkpoint/optimizer.pt
- Command line
-
hf download hf://dada22231/93902ce4-614c-4d1d-95c0-ae13dc4f6f7b/last-checkpoint/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/dada22231/93902ce4-614c-4d1d-95c0-ae13dc4f6f7b/resolve/main/last-checkpoint/optimizer.pt
114 kB
- Xet hash:
- 92e2e49edca5cdf1069042231c2238955ae5f8719e2b91d9692596f994187de1
- Size of remote file:
- 114 kB
- SHA256:
- 8c50fa19938d2716972f3a5428f0551b1de85cb8e05ef5ae37b338c7c9ed9e8e
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