most-embed-de GGUF

GGUF format of malteos/most-embed-de for use with CrispEmbed.

MOST Embed DE — German customer-support retrieval model, with query: / passage: prompts and normalized 2048-d embeddings.

Files

File	Quantization	Size
most-embed-de-q4_k-attn-q8.gguf	Q4_K	875 MB
most-embed-de-q8_0.gguf	Q8_0	1163 MB

Parity vs HuggingFace reference

Cosine similarity vs the upstream sentence-transformers reference on a fixed test set (text):

Quant	Text
f16	1.0000
q8_0	0.9998
q4_k	0.9872

Note: below the 0.99 retrieval-quality bar — text: q4_k (0.987). Embeddings are still functionally usable (>0.9 = directionally correct for similarity ranking) but expect small differences in nearest-neighbor results vs the upstream f32 reference.

License and provenance

The fine-tune is distributed under CC-BY-NC-4.0; commercial use is not permitted without separate authorization from the fine-tune author. It is derived from NVIDIA's Nemotron-3-Embed-1B-BF16, whose Model Materials are distributed under OpenMDW-1.1. Redistribution must retain the OpenMDW agreement plus all applicable copyright and origin notices. Both sets of terms and the upstream model cards must be reviewed and preserved.

Quick Start

# Download
huggingface-cli download cstr/most-embed-de-GGUF most-embed-de-q4_k-attn-q8.gguf --local-dir .

# Run with CrispEmbed
./crispembed -m most-embed-de-q4_k-attn-q8.gguf "Hello world"

# Or with auto-download
./crispembed -m most-embed-de "Hello world"

Model Details

Property	Value
Architecture	Ministral3 bidirectional encoder
Parameters	1.14B
Embedding Dimension	2048
Layers	16
Pooling	mean
Tokenizer	Tekken ByteLevel BPE
Base Model	malteos/most-embed-de

Verification

Compared with the original Transformers implementation. F16 reaches cosine 1.000000 at every dumped transformer boundary and on the final embedding. Q8_0 reaches final cosine 0.999818. The compact Q4_K artifact keeps token embeddings and attention at Q8_0; over eight German query/document texts it has minimum cosine 0.987191, preserves every top-1 retrieval result, and reduces maximum similarity-score error to 0.02595.

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 most-embed-de-q4_k-attn-q8.gguf "query text"

# Server mode
./build/crispembed-server -m most-embed-de-q4_k-attn-q8.gguf --port 8080
curl -X POST http://localhost:8080/v1/embeddings \
    -d '{"input": ["Hello world"], "model": "most-embed-de"}'