Instructions to use batiai/Qwen3-Reranker-8B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use batiai/Qwen3-Reranker-8B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="batiai/Qwen3-Reranker-8B-GGUF",
	filename="Qwen3-Reranker-8B-Q6_K.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use batiai/Qwen3-Reranker-8B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf batiai/Qwen3-Reranker-8B-GGUF:Q6_K
# Run inference directly in the terminal:
llama-cli -hf batiai/Qwen3-Reranker-8B-GGUF:Q6_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf batiai/Qwen3-Reranker-8B-GGUF:Q6_K
# Run inference directly in the terminal:
llama-cli -hf batiai/Qwen3-Reranker-8B-GGUF:Q6_K

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf batiai/Qwen3-Reranker-8B-GGUF:Q6_K
# Run inference directly in the terminal:
./llama-cli -hf batiai/Qwen3-Reranker-8B-GGUF:Q6_K

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf batiai/Qwen3-Reranker-8B-GGUF:Q6_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf batiai/Qwen3-Reranker-8B-GGUF:Q6_K

Use Docker

docker model run hf.co/batiai/Qwen3-Reranker-8B-GGUF:Q6_K

LM Studio
Jan
Ollama
How to use batiai/Qwen3-Reranker-8B-GGUF with Ollama:
```
ollama run hf.co/batiai/Qwen3-Reranker-8B-GGUF:Q6_K
```

Unsloth Studio new

How to use batiai/Qwen3-Reranker-8B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for batiai/Qwen3-Reranker-8B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for batiai/Qwen3-Reranker-8B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for batiai/Qwen3-Reranker-8B-GGUF to start chatting

Pi new

How to use batiai/Qwen3-Reranker-8B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf batiai/Qwen3-Reranker-8B-GGUF:Q6_K

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "batiai/Qwen3-Reranker-8B-GGUF:Q6_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use batiai/Qwen3-Reranker-8B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf batiai/Qwen3-Reranker-8B-GGUF:Q6_K

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default batiai/Qwen3-Reranker-8B-GGUF:Q6_K

Run Hermes

hermes

Docker Model Runner
How to use batiai/Qwen3-Reranker-8B-GGUF with Docker Model Runner:
```
docker model run hf.co/batiai/Qwen3-Reranker-8B-GGUF:Q6_K
```

Lemonade

How to use batiai/Qwen3-Reranker-8B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull batiai/Qwen3-Reranker-8B-GGUF:Q6_K

Run and chat with the model

lemonade run user.Qwen3-Reranker-8B-GGUF-Q6_K

List all available models

lemonade list

Qwen3-Reranker-8B GGUF — Quantized by BatiAI

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?

Use case	Pick
Desktop workstation / plenty of RAM	8B — best ranking accuracy, clearest margin on adversarial/ambiguous negatives
Typical laptop / 32 GB Mac	4B — close to 8B quality at half the size
Edge / small Mac / batch rerank at scale	0.6B — 13× smaller than 8B, still hits 100 % pairwise accuracy on our test

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

File	Quant	Size	Recommended
`Qwen3-Reranker-8B-Q6_K.gguf`	Q6_K	5.8 GB	balanced (recommended default)
`Qwen3-Reranker-8B-Q8_0.gguf`	Q8_0	7.5 GB	near-lossless

Quality Verification (measured)

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

Quant	Accuracy	Margin (pos-neg)
Q6_K	100 %	0.819
Q8_0	100 %	0.825

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

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

Model	Hard margin	Drift (Q6↔Q8)
0.6B	0.723–0.751	r = 0.996
4B	0.650–0.672	r = 0.998
8B	0.819–0.825	r = 0.999

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

Role	Model	HF
Reranker (0.6 B)	Qwen3-Reranker-0.6B	batiai/Qwen3-Reranker-0.6B-GGUF
Reranker (4 B)	Qwen3-Reranker-4B	batiai/Qwen3-Reranker-4B-GGUF
Reranker (8 B)	Qwen3-Reranker-8B	this repo
VL Embedding (2 B)	Qwen3-VL-Embedding-2B	batiai/Qwen3-VL-Embedding-2B-GGUF
Chat LLM (35 B-A3B)	Qwen3.6-35B-A3B	batiai/Qwen3.6-35B-A3B-GGUF