Qwen3-Reranker-8B-GGUF

Qwen3-Reranker-8B GGUF — Quantized by BatiAI

<p align="center"> <a href="https://flow.bati.ai"><img src="https://img.shields.io/badge/BatiFlow-RAG%20on%20Mac-blue?style=for-the-badge&logo=apple" alt="BatiFlow"></a> <a href="https://huggingface.co/Qwen/Qwen3-Reranker-8B"><img src="https://img.shields.io/badge/Upstream-Qwen3--Reranker--8B-orange?style=for-the-badge" alt="Upstream"></a> </p>

GGUF quantizations of Qwen/Qwen3-Reranker-8B — the top-tier of the Qwen3 reranker family for maximum ranking precision. Part of BatiAI's on-device RAG stack for BatiFlow.

What is a reranker?

RAG pipeline: embedding (coarse retrieve) → reranker (precise scoring) → LLM (answer).

A reranker takes (query, candidate_document) and returns a relevance score. It's the "second pass" after vector search — turns "probably relevant" candidates into an ordered top-K that the LLM can use confidently.

When to pick 8B over 0.6B / 4B?

All three from the same Qwen3-Reranker family, different sizes. 8B is the quality ceiling.

Quick Start (llama.cpp)

./llama-server -m Qwen3-Reranker-8B-Q8_0.gguf \
  --rerank --pooling rank -c 4096 \
  --host 127.0.0.1 --port 8090

curl http://127.0.0.1:8090/rerank -d '{
  "query": "What is RAG?",
  "documents": ["RAG ...", "Paris ..."]
}'

Note: Ollama doesn't have a native reranker endpoint yet, so this GGUF is intended for direct llama.cpp integration or tools like LangChain / LlamaIndex.

Available Quantizations

Quality Verification (measured)

Ran 40 (query, positive, negative) triples — 20 EN + 20 KO — on hard test (topically-close negatives):

Pearson correlation Q6_K ↔ Q8_0: r = 0.9986 → quantization drift essentially zero.

8B vs smaller variants (same testset, same script):

The 8B's larger margin on adversarial negatives is its key differentiator — the score separation between "right answer" and "close-but-wrong" is visibly wider, which helps in high-stakes retrieval where you can't afford the top-1 to be wrong.

Why Qwen3-Reranker?

• SOTA among open rerankers — top of MTEB reranking benchmarks
• Multilingual — en / ko / ja / zh
• Apache 2.0 — commercial-friendly

Why BatiAI?

• Quantized directly from Alibaba's BF16 safetensors
• BatiAI-signed — general.author: BatiAI, general.url: https://flow.bati.ai
• Part of a full on-device RAG stack

Technical Details

• Original Model: Qwen/Qwen3-Reranker-8B
• Architecture: Qwen3 Causal LM (cross-encoder scorer)
• Parameters: 8 B
• Context: 32 K
• License: Apache 2.0
• Quantized with: llama.cpp build bafae2765

BatiAI's RAG Stack

License

Mirrors upstream Qwen Apache 2.0. Commercial use permitted.