Downloads · 30 days
136
7% of all-time downloads
mlx-community/snowflake-arctic-embed-l-v2.0-bf16
snowflake-arctic-embed-l-v2.0-bf16 is a sentence similarity model from mlx-community. Use it when you need a score for how close two texts are. It is set up for sentence-transformers. The card lists the license as apache-2.0.
The Model mlx-community/snowflake-arctic-embed-l-v2.0-bf16 was converted to MLX format from Snowflake/snowflake-arctic-embed-l-v2.0 using mlx-lm version 0.0.3.
Downloads · 30 days
136
7% of all-time downloads
All-time downloads
2K
Public
Parameters
568M
1.2 GB on disk
Likes
1
Public
Click a slice to open those files.
.safetensors1.1 GB · 98%
From the Hugging Face model README
The Model mlx-community/snowflake-arctic-embed-l-v2.0-bf16 was converted to MLX format from Snowflake/snowflake-arctic-embed-l-v2.0 using mlx-lm version 0.0.3.
pip install mlx-embeddings
from mlx_embeddings import load, generate
import mlx.core as mx
model, tokenizer = load("mlx-community/snowflake-arctic-embed-l-v2.0-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)