bge-m3 GGUF

GGUF format of BAAI/bge-m3 for use with CrispEmbed.

BGE-M3. Dense + sparse + ColBERT multi-vector retrieval in one model. 100+ languages, 8192 context.

Files

File	Quantization	Size
bge-m3-q4_k.gguf	Q4_K	438 MB
bge-m3-q8_0.gguf	Q8_0	583 MB
bge-m3.gguf	F32	2175 MB

Quick Start

# Download
huggingface-cli download cstr/bge-m3-GGUF bge-m3-q4_k.gguf --local-dir .

# Run with CrispEmbed
./crispembed -m bge-m3-q4_k.gguf "Hello world"

# Or with auto-download
./crispembed -m bge-m3 "Hello world"

Model Details

Property	Value
Architecture	XLM-R
Parameters	568M
Embedding Dimension	1024
Layers	24
Pooling	mean
Tokenizer	SentencePiece
Base Model	BAAI/bge-m3

Verification

Verified bit-identical to HuggingFace sentence-transformers (cosine similarity >= 0.999 on test texts).

Usage with CrispEmbed

CrispEmbed is a lightweight C/C++ text embedding inference engine using ggml. No Python runtime, no ONNX. Supports BERT, XLM-R, Qwen3, and Gemma3 architectures.

# Build CrispEmbed
git clone https://github.com/CrispStrobe/CrispEmbed
cd CrispEmbed
cmake -S . -B build && cmake --build build -j

# Encode
./build/crispembed -m bge-m3-q4_k.gguf "query text"

# Server mode
./build/crispembed-server -m bge-m3-q4_k.gguf --port 8080
curl -X POST http://localhost:8080/v1/embeddings \
    -d '{"input": ["Hello world"], "model": "bge-m3"}'

Credits

Original model: BAAI/bge-m3
Inference engine: CrispEmbed (ggml-based)
Conversion: convert-bert-embed-to-gguf.py

Provenance and EU AI Act Art. 53 note

Upstream model: BAAI/bge-m3 — published by BAAI.
Upstream licence: mit. This repository redistributes under the same terms; it grants no rights the upstream licence does not.
What was done here: format conversion and/or quantisation only (GGUF/GGML). No training, no fine-tuning, no merging, no distillation, no change to architecture, vocabulary or capability. Only the numeric representation of the upstream weights differs.
Training data: documented — where it is documented at all — by the upstream provider; see the upstream model card. No training data was used, added or selected by this repository.
Provider status: under Regulation (EU) 2024/1689 the upstream authors remain the provider of this model. Converting the serialisation format does not make this repository the provider of a new general-purpose AI model, and no such claim is made. Questions about training content, copyright policy or model capability belong upstream.

加载中…

File

Quantization

Size

bge-m3-q4_k.gguf

Q4_K

438 MB

bge-m3-q8_0.gguf

Q8_0

583 MB

bge-m3.gguf

F32

2175 MB

# Download huggingface-cli download cstr/bge-m3-GGUF bge-m3-q4_k.gguf --local-dir . # Run with CrispEmbed ./crispembed -m bge-m3-q4_k.gguf "Hello world" # Or with auto-download ./crispembed -m bge-m3 "Hello world"

Property

Value

Architecture

XLM-R

Parameters

568M

Embedding Dimension

1024

Layers

Pooling

mean

Tokenizer

SentencePiece

Base Model

BAAI/bge-m3

Usage with CrispEmbed

CrispEmbed is a lightweight C/C++ text embedding inference engine using ggml. No Python runtime, no ONNX. Supports BERT, XLM-R, Qwen3, and Gemma3 architectures.

# Build CrispEmbed git clone https://github.com/CrispStrobe/CrispEmbed cd CrispEmbed cmake -S . -B build && cmake --build build -j # Encode ./build/crispembed -m bge-m3-q4_k.gguf "query text" # Server mode ./build/crispembed-server -m bge-m3-q4_k.gguf --port 8080 curl -X POST http://localhost:8080/v1/embeddings \ -d '{"input": ["Hello world"], "model": "bge-m3"}'

Provenance and EU AI Act Art. 53 note

Upstream model: BAAI/bge-m3 — published by BAAI.

Upstream licence: mit. This repository redistributes under the same terms; it grants no rights the upstream licence does not.

What was done here: format conversion and/or quantisation only (GGUF/GGML). No training, no fine-tuning, no merging, no distillation, no change to architecture, vocabulary or capability. Only the numeric representation of the upstream weights differs.

Training data: documented — where it is documented at all — by the upstream provider; see the upstream model card. No training data was used, added or selected by this repository.

Provider status: under Regulation (EU) 2024/1689 the upstream authors remain the provider of this model. Converting the serialisation format does not make this repository the provider of a new general-purpose AI model, and no such claim is made. Questions about training content, copyright policy or model capability belong upstream.