cstr/all-mpnet-base-v2-GGUF

all-mpnet-base-v2 GGUF

GGUF format of sentence-transformers/all-mpnet-base-v2 for use with CrispEmbed.

All-MPNet-Base-v2. Highest quality sentence-transformers model, 768-dimensional mean-pooled.

Files

File	Quantization	Size
all-mpnet-base-v2-q4_k.gguf	Q4_K	71 MB
all-mpnet-base-v2-q8_0.gguf	Q8_0	112 MB
all-mpnet-base-v2.gguf	F32	418 MB

Quick Start

# Download
huggingface-cli download cstr/all-mpnet-base-v2-GGUF all-mpnet-base-v2-q4_k.gguf --local-dir .

# Run with CrispEmbed
./crispembed -m all-mpnet-base-v2-q4_k.gguf "Hello world"

# Or with auto-download
./crispembed -m all-mpnet-base-v2 "Hello world"

Model Details

Property	Value
Architecture	BERT
Parameters	109M
Embedding Dimension	768
Layers	12
Pooling	mean
Tokenizer	WordPiece
Base Model	sentence-transformers/all-mpnet-base-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 all-mpnet-base-v2-q4_k.gguf "query text"

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

Credits

• Original model: sentence-transformers/all-mpnet-base-v2
• Inference engine: CrispEmbed (ggml-based)
• Conversion: convert-bert-embed-to-gguf.py

Provenance and EU AI Act Art. 53 note

• Upstream model: sentence-transformers/all-mpnet-base-v2 — published by sentence-transformers.
• 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.