Feature Extraction
sentence-transformers
Safetensors
MLX
multilingual
bert
text-embeddings-inference
Instructions to use wabibito/Onyx-mE5-Small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use wabibito/Onyx-mE5-Small with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("wabibito/Onyx-mE5-Small") 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 wabibito/Onyx-mE5-Small with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Onyx-mE5-Small wabibito/Onyx-mE5-Small
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
Onyx-mE5-Small (4-bit MLX)
4-bit MLX quantization of intfloat/multilingual-e5-small (MIT), for the Onyx on-device RAG embedder (OnyxEmbed). group_size 64, bits 4 on the attention/FFN dense matrices + word_embeddings; LayerNorms, biases, and positional embeddings stay fp. ~71 MB (from ~449 MB fp32).
Usage keeps e5 conventions: mean pooling, and the query: / passage: prefixes. Verified recall-neutral
vs fp32 (English + cross-lingual). Inherits the base model MIT license.
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Model size
18.6M params
Tensor type
F32
路
I64 路
U32 路
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Model tree for wabibito/Onyx-mE5-Small
Base model
intfloat/multilingual-e5-small