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
File size: 1,106 Bytes
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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`](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2), tagged for use with
[**ANEForge**](https://github.com/sbryngelson/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
```python
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](https://aneforge.readthedocs.io) and the
[paper](https://arxiv.org/abs/2606.17090).
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