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cstr/bge-m3-GGUF
bge-m3-GGUF is a feature extraction model from cstr. Use it when you need embeddings to search or compare text. The card lists the license as mit.
GGUF format of BAAI/bge-m3 for use with CrispEmbed.
Downloads · 30 days
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.gguf4.2 GB · 100%
From the Hugging Face model README
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.
| 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 | 24 |
| Pooling | mean |
| Tokenizer | SentencePiece |
| Base Model | BAAI/bge-m3 |
Verified bit-identical to HuggingFace sentence-transformers (cosine similarity >= 0.999 on test texts).
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"}'
convert-bert-embed-to-gguf.pyBAAI.mit. This repository redistributes under the same terms; it grants no rights the upstream licence does not.