Feature Extraction
sentence-transformers
Safetensors
MLX
English
multilingual
qwen3
finance
legal
healthcare
code
stem
medical
mlx-my-repo
text-embeddings-inference
6-bit
Instructions to use lexrivera/zembed-1-embedding-mlx-6Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use lexrivera/zembed-1-embedding-mlx-6Bit with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("lexrivera/zembed-1-embedding-mlx-6Bit") 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] - MLX
How to use lexrivera/zembed-1-embedding-mlx-6Bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir zembed-1-embedding-mlx-6Bit lexrivera/zembed-1-embedding-mlx-6Bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Xet hash:
- dde3c5a4d468da6fe61f11ffa8f04af2d101f8f4a0fe118c37bd31bb8c3017b6
- Size of remote file:
- 11.4 MB
- SHA256:
- be75606093db2094d7cd20f3c2f385c212750648bd6ea4fb2bf507a6a4c55506
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