Instructions to use TrendHD/all-MiniLM-L6-v2-int8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use TrendHD/all-MiniLM-L6-v2-int8 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("TrendHD/all-MiniLM-L6-v2-int8") 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
all-MiniLM-L6-v2 (INT8, ONNX)
This repository contains an INT8-quantized version of all-MiniLM-L6-v2. The quantize dynamic quantization method was used for maximum cross-platform compatibility.
Based on the original model: https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2
Post-training INT8 quantization
Optimized for cross-platform compatibility
Suitable for embeddings, semantic search, and text classification
Note: This is a derivative work with quantization only.
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Model tree for TrendHD/all-MiniLM-L6-v2-int8
Base model
nreimers/MiniLM-L6-H384-uncased Quantized
sentence-transformers/all-MiniLM-L6-v2