Instructions to use sinjab/Qwen3-Reranker-8B-Q4_K_M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sinjab/Qwen3-Reranker-8B-Q4_K_M-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="sinjab/Qwen3-Reranker-8B-Q4_K_M-GGUF",
	filename="Qwen3-Reranker-8B-Q4_K_M.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 sinjab/Qwen3-Reranker-8B-Q4_K_M-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf sinjab/Qwen3-Reranker-8B-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf sinjab/Qwen3-Reranker-8B-Q4_K_M-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf sinjab/Qwen3-Reranker-8B-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf sinjab/Qwen3-Reranker-8B-Q4_K_M-GGUF:Q4_K_M

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 sinjab/Qwen3-Reranker-8B-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf sinjab/Qwen3-Reranker-8B-Q4_K_M-GGUF:Q4_K_M

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 sinjab/Qwen3-Reranker-8B-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sinjab/Qwen3-Reranker-8B-Q4_K_M-GGUF:Q4_K_M

Use Docker

docker model run hf.co/sinjab/Qwen3-Reranker-8B-Q4_K_M-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use sinjab/Qwen3-Reranker-8B-Q4_K_M-GGUF with Ollama:
```
ollama run hf.co/sinjab/Qwen3-Reranker-8B-Q4_K_M-GGUF:Q4_K_M
```

Unsloth Studio new

How to use sinjab/Qwen3-Reranker-8B-Q4_K_M-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 sinjab/Qwen3-Reranker-8B-Q4_K_M-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 sinjab/Qwen3-Reranker-8B-Q4_K_M-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for sinjab/Qwen3-Reranker-8B-Q4_K_M-GGUF to start chatting

Pi new

How to use sinjab/Qwen3-Reranker-8B-Q4_K_M-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf sinjab/Qwen3-Reranker-8B-Q4_K_M-GGUF:Q4_K_M

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": "sinjab/Qwen3-Reranker-8B-Q4_K_M-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use sinjab/Qwen3-Reranker-8B-Q4_K_M-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 sinjab/Qwen3-Reranker-8B-Q4_K_M-GGUF:Q4_K_M

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 sinjab/Qwen3-Reranker-8B-Q4_K_M-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use sinjab/Qwen3-Reranker-8B-Q4_K_M-GGUF with Docker Model Runner:
```
docker model run hf.co/sinjab/Qwen3-Reranker-8B-Q4_K_M-GGUF:Q4_K_M
```

Lemonade

How to use sinjab/Qwen3-Reranker-8B-Q4_K_M-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sinjab/Qwen3-Reranker-8B-Q4_K_M-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3-Reranker-8B-Q4_K_M-GGUF-Q4_K_M

List all available models

lemonade list

Qwen3-Reranker-8B-Q4_K_M-GGUF

This model was converted to GGUF format from Qwen/Qwen3-Reranker-8B using llama.cpp via the ggml.ai's GGUF-my-repo space.

Refer to the original model card for more details on the model.

Model Information

Base Model: Qwen/Qwen3-Reranker-8B
Quantization: Q4_K_M
Format: GGUF (GPT-Generated Unified Format)
Converted with: llama.cpp

Quantization Details

This is a Q4_K_M quantization of the original model:

F16: Full 16-bit floating point - highest quality, largest size
Q8_0: 8-bit quantization - high quality, good balance
Q4_K_M: 4-bit quantization with medium quality - smaller size, faster inference

Usage

This model can be used with llama.cpp and other GGUF-compatible inference engines.

# Example using llama.cpp
./llama-rerank -m Qwen3-Reranker-8B-Q4_K_M.gguf

Model Files

Quantization	Use Case
F16	Maximum quality, largest size
Q8_0	High quality, good balance of size/performance
Q4_K_M	Good quality, smallest size, fastest inference

Citation

If you use this model, please cite the original model:

# See original model card for citation information

License

This model inherits the license from the original model. Please refer to the original model card for license details.

Acknowledgements

Original model by the authors of Qwen/Qwen3-Reranker-8B
GGUF conversion via llama.cpp by ggml.ai
Converted and uploaded by sinjab

Downloads last month: 10

GGUF

Model size

8B params

Architecture

qwen3

Hardware compatibility

4-bit

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