Instructions to use xhan77/ssdlm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xhan77/ssdlm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="xhan77/ssdlm")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("xhan77/ssdlm") model = AutoModelForMaskedLM.from_pretrained("xhan77/ssdlm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download embed_sum_layer.pt from xhan77/ssdlm: direct link, hf CLI and curl.
- Browser
- Download file 206 MB
-
https://huggingface.co/xhan77/ssdlm/resolve/refs%2Fpr%2F1/embed_sum_layer.pt
- Command line
-
hf download hf://xhan77/ssdlm@refs/pr/1/embed_sum_layer.pt
-
curl -L -o embed_sum_layer.pt https://huggingface.co/xhan77/ssdlm/resolve/refs%2Fpr%2F1/embed_sum_layer.pt
206 MB
- Xet hash:
- c98e7f3fd71925addf87dfa3ebd455eafc0f204c1adb33a866540357a2d4b63c
- Size of remote file:
- 206 MB
- SHA256:
- fdc535ca061cfec8472081523804a1a419c2852d9c28b2edaa27cbb8d4141a9d
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