Sentence Similarity
sentence-transformers
PyTorch
ONNX
Safetensors
Transformers
bert
feature-extraction
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use TaylorAI/gte-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use TaylorAI/gte-tiny with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("TaylorAI/gte-tiny") 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 TaylorAI/gte-tiny with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("TaylorAI/gte-tiny") model = AutoModel.from_pretrained("TaylorAI/gte-tiny") - Inference
- Notebooks
- Google Colab
- Kaggle
Do you have guide to convert this to GGUF/GGML format?
#4
by qhkm - opened
Hi there! This is super cool! Saw it on twitter and was amazed by the performance as compared to sentence embedding. Would love to be able to use this in llama cpp so would need to convert to gguf to be able to use it. Do you have any idea how to do that? Thanks!
I would not recommend using this with Llama.cpp. It's a BERT model, so I looked into BERT.cpp but I don't really see the benefits of that over ONNX. I provided ONNX checkpoints so you should just use those. Many of the benefits of using Llama.cpp are more relevant to text generation, not so much for embeddings.