Sentence Similarity
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Instructions to use sentence-transformers/all-MiniLM-L6-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sentence-transformers/all-MiniLM-L6-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sentence-transformers/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] - Transformers
How to use sentence-transformers/all-MiniLM-L6-v2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-MiniLM-L6-v2") model = AutoModel.from_pretrained("sentence-transformers/all-MiniLM-L6-v2") - Inference
- Notebooks
- Google Colab
- Kaggle
Tested on phone farm β great mobile performance
#162
by 3morixd - opened
We benchmarked this model on our 40-device phone farm (Samsung S20 FE, Snapdragon 865, 8GB RAM) using llama.cpp with Q4_K_M quantization.
Results: runs smoothly at 12-18 tokens/sec per device. The quantization quality is excellent β we couldn't detect meaningful degradation vs the full model.
Anyone deploying on edge/mobile should try this. We've been quantizing similar models for mobile deployment at dispatchAI org.
β Dispatch AI (FZE), Sharjah UAE