Sentence Similarity
Safetensors
sentence-transformers
aneforge
bert
apple-neural-engine
text-embeddings-inference
Instructions to use aneforge/all-MiniLM-L6-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use aneforge/all-MiniLM-L6-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("aneforge/all-MiniLM-L6-v2") 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
Duplicate sentence-transformers/all-MiniLM-L6-v2 for ANEForge (weights unchanged, library_name: aneforge)
c22677e verified metadata
library_name: aneforge
license: apache-2.0
base_model: sentence-transformers/all-MiniLM-L6-v2
pipeline_tag: sentence-similarity
tags:
- aneforge
- apple-neural-engine
- sentence-transformers
all-MiniLM-L6-v2 (ANEForge)
An unmodified duplicate of sentence-transformers/all-MiniLM-L6-v2, tagged for use with
ANEForge so the weights load and run directly on the
Apple Neural Engine (no CoreML). Weights are byte-identical to the source; see the original repo for
the model details. Licensed apache-2.0 from the source.
Use with ANEForge
from aneforge.sentence_transformers import SentenceTransformer
model = SentenceTransformer("aneforge/all-MiniLM-L6-v2")
emb = model.encode(["Hello from the Neural Engine"], normalize_embeddings=True)
ANEForge compiles the model's graph into a single ANE program and streams the weights from this repo
via huggingface_hub. See the docs and the
paper.