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
Provenance and EU AI Act Art. 53 note
- Upstream model: cross-encoder/ms-marco-MiniLM-L-6-v2 — published by
cross-encoder. - Upstream licence:
apache-2.0. 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.
2026-08-05: corrected scoring head (-g7c files)
The original .gguf files in this repo were converted without the
BertPooler stage: HF's BertForSequenceClassification scores
classifier(tanh(pooler(CLS))), but these files carried only the 1-layer
classifier, so scores came out mis-calibrated (≈ ±0.2 instead of ≈ ±11)
and the ranking tail could reorder. The -g7c files fold the pooler into a
dense→tanh→out_proj head and match the reference ONNX export
(Xenova/ms-marco-MiniLM-L-6-v2) to ≤1e-3 at f16. Prefer the -g7c files;
the originals are kept only so older CrispEmbed releases keep their pinned
downloads.