Instructions to use chulcher/Octen-Embedding-8B-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use chulcher/Octen-Embedding-8B-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Octen-Embedding-8B-mlx chulcher/Octen-Embedding-8B-mlx
- sentence-transformers
How to use chulcher/Octen-Embedding-8B-mlx with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("chulcher/Octen-Embedding-8B-mlx") 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] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Xet hash:
- 7f26d71aeab40e6bac513acbfab71f48109e92ee379f5fc5fe23dba262364317
- Size of remote file:
- 11.4 MB
- SHA256:
- 83cdf8c3a34f68862319cb1810ee7b1e2c0a44e0864ae930194ddb76bb7feb8d
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