cstr/jina-reranker-v2-base-multilingual-GGUF

jina-reranker-v2-base-multilingual GGUF

GGUF format of jinaai/jina-reranker-v2-base-multilingual for use with CrispEmbed.

Jina Reranker v2 Base Multilingual. Cross-encoder reranker for 100+ languages. Post-LN, NomicBERT-like layout (mixer.Wqkv + GELU FFN). Use with crispembed_rerank().

Files

Parity vs HuggingFace reference

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

Quick Start

# Download
huggingface-cli download cstr/jina-reranker-v2-base-multilingual-GGUF jina-reranker-v2-base-multilingual.gguf --local-dir .

# Run with CrispEmbed
./crispembed -m jina-reranker-v2-base-multilingual.gguf "Hello world"

# Or with auto-download
./crispembed -m jina-reranker-v2-base-multilingual "Hello world"

Model Details

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 jina-reranker-v2-base-multilingual.gguf "query text"

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

Credits

• —Original model: jinaai/jina-reranker-v2-base-multilingual
• —Inference engine: CrispEmbed (ggml-based)
• —Conversion: convert-bert-embed-to-gguf.py

Provenance and EU AI Act Art. 53 note

• —Upstream model: jinaai/jina-reranker-v2-base-multilingual — published by jinaai.
• —Upstream licence: cc-by-nc-4.0. 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.