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 | 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). | |