Total runs:
62.9K
Run Growth:
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Growth Rate:
86.73%
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CompendiumLabs / bge-base-zh-v1.5-gguf
Model's Last Updated: February 18 2024
Introduction of bge-base-zh-v1.5-gguf
Model Details of bge-base-zh-v1.5-gguf
bge-base-zh-v1.5-gguf
Source model: https://huggingface.co/BAAI/bge-base-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-base-zh-v1.5-f32.gguf | F32 | 389 MB |
| bge-base-zh-v1.5-f16.gguf | F16 | 195 MB |
| bge-base-zh-v1.5-q8_0.gguf | Q8_0 | 105 MB |
| bge-base-zh-v1.5-q4_k_m.gguf | Q4_K_M | 62 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
.
Runs of CompendiumLabs bge-base-zh-v1.5-gguf on huggingface.co
440
Total runs
13
24-hour runs
45
3-day runs
14
7-day runs
102
30-day runs
More Information About bge-base-zh-v1.5-gguf huggingface.co Model
Url of bge-base-zh-v1.5-gguf
bge-base-zh-v1.5-gguf huggingface.co Url
Provider of bge-base-zh-v1.5-gguf huggingface.co
Other API from CompendiumLabs
Updated:February 18 2024
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