license: mit
bge-large-zh-v1.5-gguf
Source model: https://huggingface.co/BAAI/bge-large-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.
Files Available
| Filename | Quantization | Size |
|---|---|---|
| bge-large-zh-v1.5-f32.gguf | F32 | 1.3 BB |
| bge-large-zh-v1.5-f16.gguf | F16 | 620 MB |
| bge-large-zh-v1.5-q8_0.gguf | Q8_0 | 332 MB |
| bge-large-zh-v1.5-q4_k_m.gguf | Q4_K_M | 193 MB |
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.