cstr/ms-marco-MiniLM-L-6-v2-GGUF

ms-marco-MiniLM-L-6-v2 GGUF

GGUF format of cross-encoder/ms-marco-MiniLM-L-6-v2 for use with CrispEmbed.

MS MARCO MiniLM L-6 v2. Fastest cross-encoder reranker, 22M parameters. Ideal for real-time RAG.

Files

File	Quantization	Size
ms-marco-MiniLM-L-6-v2-q4_k.gguf	Q4_K	18 MB
ms-marco-MiniLM-L-6-v2-q8_0.gguf	Q8_0	24 MB
ms-marco-MiniLM-L-6-v2.gguf	F32	87 MB

Quick Start

# Download
huggingface-cli download cstr/ms-marco-MiniLM-L-6-v2-GGUF ms-marco-MiniLM-L-6-v2-q4_k.gguf --local-dir .

# Run with CrispEmbed
./crispembed -m ms-marco-MiniLM-L-6-v2-q4_k.gguf "Hello world"

# Or with auto-download
./crispembed -m ms-marco-MiniLM-L-6-v2 "Hello world"

Model Details

Property	Value
Architecture	BERT
Parameters	22M
Embedding Dimension	384
Layers	6
Pooling	CLS
Tokenizer	WordPiece
Base Model	cross-encoder/ms-marco-MiniLM-L-6-v2

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 ms-marco-MiniLM-L-6-v2-q4_k.gguf "query text"

# Server mode
./build/crispembed-server -m ms-marco-MiniLM-L-6-v2-q4_k.gguf --port 8080
curl -X POST http://localhost:8080/v1/embeddings \
    -d '{"input": ["Hello world"], "model": "ms-marco-MiniLM-L-6-v2"}'

Credits

• Original model: cross-encoder/ms-marco-MiniLM-L-6-v2
• Inference engine: CrispEmbed (ggml-based)
• Conversion: convert-bert-embed-to-gguf.py