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FastFlowLM
/
Embedding-Gemma-300M-NPU2

Sentence Similarity
sentence-transformers
gemma3_text
feature-extraction
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use FastFlowLM/Embedding-Gemma-300M-NPU2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use FastFlowLM/Embedding-Gemma-300M-NPU2 with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("FastFlowLM/Embedding-Gemma-300M-NPU2")
    
    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]
  • Notebooks
  • Google Colab
  • Kaggle
Embedding-Gemma-300M-NPU2
651 MB
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  • 1 contributor
History: 4 commits
FastFlowLM's picture
FastFlowLM
Update README.md
fdc425b verified 7 months ago
  • .gitattributes
    1.82 kB
    feat: init upload 7 months ago
  • README.md
    16.3 kB
    Update README.md 7 months ago
  • attn_full_mask.xclbin
    274 kB
    xet
    feat: init upload 7 months ago
  • config.json
    1.52 kB
    Update config.json 7 months ago
  • mm.xclbin
    175 kB
    xet
    feat: init upload 7 months ago
  • model.q4nx
    615 MB
    xet
    feat: init upload 7 months ago
  • mv.xclbin
    130 kB
    xet
    feat: init upload 7 months ago
  • sliding_attn.xclbin
    269 kB
    xet
    feat: init upload 7 months ago
  • tokenizer.json
    33.4 MB
    xet
    feat: init upload 7 months ago
  • tokenizer_config.json
    1.16 MB
    feat: init upload 7 months ago