bge-small-zh-v1.5-gguf

<img src="https://raw.githubusercontent.com/CompendiumLabs/compendiumlabs.ai/main/images/logo_text_crop.png" alt="Compendium Labs" style="width: 500px;">

bge-small-zh-v1.5-gguf

Source model: https://huggingface.co/BAAI/bge-small-zh-v1.5

Quantized and unquantized embedding models in GGUF format for use with llama.cpp. A large benefit over transformers is almost guaranteed and the benefit over ONNX will vary based on the application, but this seems to provide a large speedup on CPU and a modest speedup on GPU for larger models. Due to the relatively small size of these models, quantization will not provide huge benefits, but it does generate up to a 30% speedup on CPU with minimal loss in accuracy.

<br/>

Files Available

<div style="width: 500px; margin: 0;"> </div> <br/>

Usage

These model files can be used with pure llama.cpp or with the llama-cpp-python Python bindings

from llama_cpp import Llama
model = Llama(gguf_path, embedding=True)
embed = model.embed(texts)

Here texts can either be a string or a list of strings, and the return value is a list of embedding vectors. The inputs are grouped into batches automatically for efficient execution. There is also LangChain integration through langchain_community.embeddings.LlamaCppEmbeddings.