Instructions to use danielsaggau/scotus_py with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use danielsaggau/scotus_py with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("danielsaggau/scotus_py") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use danielsaggau/scotus_py with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("danielsaggau/scotus_py") model = AutoModelForMultimodalLM.from_pretrained("danielsaggau/scotus_py") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#1
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:63611de5aad45b8a00eb93142827d3db03cc72a0ff522581f5c41401a5e19118
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size 166544800
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