README.md

metadata

language: en
license: apache-2.0
library_name: sentence-transformers
tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  - transformers
  - mlx
datasets:
  - s2orc
  - flax-sentence-embeddings/stackexchange_xml
  - ms_marco
  - gooaq
  - yahoo_answers_topics
  - code_search_net
  - search_qa
  - eli5
  - snli
  - multi_nli
  - wikihow
  - natural_questions
  - trivia_qa
  - embedding-data/sentence-compression
  - embedding-data/flickr30k-captions
  - embedding-data/altlex
  - embedding-data/simple-wiki
  - embedding-data/QQP
  - embedding-data/SPECTER
  - embedding-data/PAQ_pairs
  - embedding-data/WikiAnswers
pipeline_tag: sentence-similarity

mlx-community/all-MiniLM-L6-v2-bf16

The Model mlx-community/all-MiniLM-L6-v2-bf16 was converted to MLX format from sentence-transformers/all-MiniLM-L6-v2 using mlx-lm version 0.0.3.

Use with mlx

pip install mlx-embeddings

from mlx_embeddings import load, generate
import mlx.core as mx

model, tokenizer = load("mlx-community/all-MiniLM-L6-v2-bf16")

# For text embeddings
output = generate(model, processor, texts=["I like grapes", "I like fruits"])
embeddings = output.text_embeds  # Normalized embeddings

# Compute dot product between normalized embeddings
similarity_matrix = mx.matmul(embeddings, embeddings.T)

print("Similarity matrix between texts:")
print(similarity_matrix)