How to use from the
Use from the
sentence-transformers library
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("Etherll/test")

sentences = [
    "The weather is lovely today.",
    "It's so sunny outside!",
    "He drove to the stadium."
]
embeddings = model.encode(sentences)

similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

test - GGUF

This sentence-transformers model was finetuned and converted to GGUF format using Unsloth.

Available Model files:

  • embeddinggemma-300m.Q5_K_M.gguf
  • embeddinggemma-300m.Q8_0.gguf
  • embeddinggemma-300m.Q4_K_M.gguf

This was trained 2x faster with Unsloth

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8
GGUF
Model size
0.3B params
Architecture
gemma-embedding
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