Instructions to use Roxas13/e5-small-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use Roxas13/e5-small-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir e5-small-mlx Roxas13/e5-small-mlx
- sentence-transformers
How to use Roxas13/e5-small-mlx with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Roxas13/e5-small-mlx") 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
- Local Apps Settings
- LM Studio
AEGIS multilingual-e5-small (FP16, MLX)
FP16, MLX-format build of intfloat/multilingual-e5-small, used on-device by the
AEGIS app for RAG memory retrieval (384-dim sentence embeddings). This is a
format conversion of the original (PyTorch โ MLX, FP16) โ no fine-tuning and
no architecture change.
License & attribution
This repository redistributes a derivative of
intfloat/multilingual-e5-small,
which is released under the MIT License. The original copyright and MIT
license terms are retained and passed along to all recipients.
- Source model: https://huggingface.co/intfloat/multilingual-e5-small
- License: MIT
Modifications relative to the base model: weights converted to MLX format at FP16 precision. Output is 384-dim with mean pooling + L2 normalization. No weights were fine-tuned.
Usage notes
E5 requires input prefixes for good retrieval accuracy:
query: for search queries and passage: for stored documents.
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Base model
intfloat/multilingual-e5-small