ggmbed

A lightweight, zero-PyTorch, zero-ONNX-Runtime dense embedding inference engine. Uses a native C++ extension (llama.cpp native acceleration) to encode text extremely fast without pulling in gigabytes of deep learning dependencies.

Install

pip install ggmbed

Only runtime dependencies are numpy and huggingface-hub.

Usage

from ggmbed import Embedder

# Initializes the encoder, downloading the optimized Q8_0 quantized all-MiniLM-L6-v2 GGUF model automatically
model = Embedder("sentence-transformers/all-MiniLM-L6-v2")

embeddings = model.encode(["What is a dense embedding?", "It's extremely fast and lightweight."])
print(embeddings.shape)  # (2, 384)

# Load BAAI General Embedding model (auto-resolves to GGUF and uses CLS pooling)
bge_model = Embedder("BAAI/bge-small-en-v1.5")
bge_embeddings = bge_model.encode(["BAAI General Embedding models use CLS pooling."])

You can also point it at a local directory containing a .gguf file or directly to a .gguf file path:

model = Embedder("./my-local-model-directory/")
# OR
model = Embedder("./models/all-MiniLM-L6-v2-Q8_0.gguf")

Supported Out-of-the-Box Models

The engine automatically handles GGUF model downloading from Hugging Face (thlurte/*), caching, and pooling settings for the following pre-quantized models:

sentence-transformers/all-MiniLM-L6-v2 (default, Mean Pooling, 384 dimensions)
BAAI/bge-small-en-v1.5 (CLS Pooling, 384 dimensions)
lightonai/DenseOn (ModernBERT architecture, CLS Pooling, 768 dimensions)

You can query the list programmatically:

from ggmbed import Embedder

print(Embedder.list_supported_models())
# ['sentence-transformers/all-MiniLM-L6-v2', 'BAAI/bge-small-en-v1.5', 'lightonai/DenseOn']

Configuration Options

model_name_or_path: Local path to a GGUF file or directory, or a Hugging Face Hub model ID (defaults to "sentence-transformers/all-MiniLM-L6-v2").
tokenizer_path: Optional explicit path to tokenizer.json (no longer required, as llama.cpp loads vocabulary natively from the GGUF model).
num_threads: Number of CPU threads to use. Defaults to 0 (which automatically detects and uses physical CPU cores, avoiding hyperthreading bottlenecks).
quantization: Preferred quantization format (e.g., "Q8_0", "F16", "F32", "Q4_0"). Defaults to "Q8_0".
pooling_mode: Pooling strategy to use ("mean" or "cls"). Defaults to None (which auto-detects based on the model name).

Benchmarks & Reproducibility

To reproduce the latency and memory footprint (RSS) results, run the benchmark script directly using uv:

uv run --group benchmark python scripts/benchmark.py

Benchmark Results

Below is the live benchmark comparison measured on Linux (AMD64 CPU):

Model: sentence-transformers/all-MiniLM-L6-v2 (Mean Pooling)

MiniLM Load Time MiniLM Memory MiniLM Throughput

Metric	ggmbed (GGUF Q8_0)	fastembed (ONNX)	sentence-transformers (PyTorch)
Model Load Time	1,909.7 ms	13,488.1 ms (7.0x slower)	20,179.3 ms (10.5x slower)
Peak RAM / Memory	127.6 MB	910.7 MB (7.1x heavier)	785.0 MB (6.1x heavier)
Single Latency (p50)	12.32 ms	11.91 ms	3.70 ms
Single Latency (p95)	17.31 ms	16.95 ms	7.66 ms

Batch Throughput (sentences / second)

Batch Size	ggmbed (sent/s)	fastembed (sent/s)	sentence-transformers (sent/s)
1	78.8	86.1	55.1
4	83.4	96.3	348.3
8	79.1	65.3	1,469.5
32	78.7	55.0	4,608.6
128	79.0	37.2	6,053.3

Model: BAAI/bge-small-en-v1.5 (CLS Pooling)

BGE Latency BGE Memory BGE Throughput

Metric	ggmbed (GGUF Q8_0)	fastembed (ONNX)	sentence-transformers (PyTorch)
Model Load Time	1,700.3 ms	11,263.5 ms (6.6x slower)	18,336.6 ms (10.7x slower)
Peak RAM / Memory	111.0 MB	351.4 MB (3.1x heavier)	806.2 MB (7.2x heavier)
Single Latency (Mean)	6.73 ms	9.58 ms	6.14 ms
Single Latency (p50)	6.65 ms	9.60 ms	4.95 ms
Single Latency (p95)	9.20 ms	12.57 ms	13.03 ms

Batch Throughput (sentences / second)

Batch Size	ggmbed (sent/s)	fastembed (sent/s)	sentence-transformers (sent/s)
1	268.0	130.2	66.1
4	220.5	252.4	230.7
8	206.6	292.2	949.7
32	199.7	286.1	2,979.9
128	198.5	158.1	3,496.5

Advanced: Compile from Source (Hardware Acceleration)

By default, pre-built binary wheels are compiled with native SIMD instructions (AVX2/AVX-512/ARM NEON) for maximum CPU portability. If you are compiling from source and want to link against optimized system BLAS backends, pass the appropriate CMake arguments during installation:

AMD / Generic CPUs (OpenBLAS):

CMAKE_ARGS="-DGGML_OPENBLAS=ON" pip install --no-binary :all: ggmbed

Intel CPUs (Intel MKL / oneDNN):

CMAKE_ARGS="-DGGML_MKL=ON" pip install --no-binary :all: ggmbed

Features

GGUF-Native: Avoids PyTorch and ONNX Runtime entirely.
Hardware Optimized: Compiled with native SIMD instructions (AVX2/AVX-512/ARM NEON) and Flash Attention support.
Dynamic Threading: Auto-detects physical CPU cores to prevent runtime CPU thread thrashing.
Highly Portable: No complex system level dependencies, builds easily on macOS, Linux, and Windows.

License

MIT. See LICENSE.

License
- OSI Approved :: MIT License
Operating System
- OS Independent
Programming Language
- Python :: 3

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ggmbed 0.2.1

ggmbed

Install

Usage

Supported Out-of-the-Box Models

Configuration Options

Benchmarks & Reproducibility

Benchmark Results

Model: sentence-transformers/all-MiniLM-L6-v2 (Mean Pooling)

Model: BAAI/bge-small-en-v1.5 (CLS Pooling)

Advanced: Compile from Source (Hardware Acceleration)

Features

License

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1 maintainer

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