Instructions to use giladgd/Qwen3-Reranker-8B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use giladgd/Qwen3-Reranker-8B-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="giladgd/Qwen3-Reranker-8B-GGUF", filename="Qwen3-Reranker-8B.F16.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 giladgd/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 giladgd/Qwen3-Reranker-8B-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf giladgd/Qwen3-Reranker-8B-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 giladgd/Qwen3-Reranker-8B-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf giladgd/Qwen3-Reranker-8B-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 giladgd/Qwen3-Reranker-8B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf giladgd/Qwen3-Reranker-8B-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 giladgd/Qwen3-Reranker-8B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf giladgd/Qwen3-Reranker-8B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/giladgd/Qwen3-Reranker-8B-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use giladgd/Qwen3-Reranker-8B-GGUF with Ollama:
ollama run hf.co/giladgd/Qwen3-Reranker-8B-GGUF:Q4_K_M
- Unsloth Studio new
How to use giladgd/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 giladgd/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 giladgd/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 giladgd/Qwen3-Reranker-8B-GGUF to start chatting
- Pi new
How to use giladgd/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 giladgd/Qwen3-Reranker-8B-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": "giladgd/Qwen3-Reranker-8B-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use giladgd/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 giladgd/Qwen3-Reranker-8B-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 giladgd/Qwen3-Reranker-8B-GGUF:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use giladgd/Qwen3-Reranker-8B-GGUF with Docker Model Runner:
docker model run hf.co/giladgd/Qwen3-Reranker-8B-GGUF:Q4_K_M
- Lemonade
How to use giladgd/Qwen3-Reranker-8B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull giladgd/Qwen3-Reranker-8B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Qwen3-Reranker-8B-GGUF-Q4_K_M
List all available models
lemonade list
llm.create_chat_completion(
messages = "No input example has been defined for this model task."
)Qwen3-Reranker-8B-GGUF
Static quants of Qwen/Qwen3-Reranker-8B.
Quants
| Link | URI | Quant | Size |
|---|---|---|---|
| GGUF | hf:giladgd/Qwen3-Reranker-8B-GGUF:Q2_K |
Q2_K | 3.1GB |
| GGUF | hf:giladgd/Qwen3-Reranker-8B-GGUF:Q3_K_S |
Q3_K_S | 3.5GB |
| GGUF | hf:giladgd/Qwen3-Reranker-8B-GGUF:Q3_K_M |
Q3_K_M | 3.9GB |
| GGUF | hf:giladgd/Qwen3-Reranker-8B-GGUF:Q3_K_L |
Q3_K_L | 4.2GB |
| GGUF | hf:giladgd/Qwen3-Reranker-8B-GGUF:Q4_0 |
Q4_0 | 4.4GB |
| GGUF | hf:giladgd/Qwen3-Reranker-8B-GGUF:Q4_K_S |
Q4_K_S | 4.5GB |
| GGUF | hf:giladgd/Qwen3-Reranker-8B-GGUF:Q4_K_M |
Q4_K_M | 4.7GB |
| GGUF | hf:giladgd/Qwen3-Reranker-8B-GGUF:Q5_0 |
Q5_0 | 5.3GB |
| GGUF | hf:giladgd/Qwen3-Reranker-8B-GGUF:Q5_K_S |
Q5_K_S | 5.3GB |
| GGUF | hf:giladgd/Qwen3-Reranker-8B-GGUF:Q5_K_M |
Q5_K_M | 5.4GB |
| GGUF | hf:giladgd/Qwen3-Reranker-8B-GGUF:Q6_K |
Q6_K | 6.2GB |
| GGUF | hf:giladgd/Qwen3-Reranker-8B-GGUF:Q8_0 |
Q8_0 | 8.0GB |
| GGUF | hf:giladgd/Qwen3-Reranker-8B-GGUF:F16 |
F16 | 15.1GB |
Download a quant using
node-llama-cpp(more info):npx -y node-llama-cpp pull <URI>
Usage
Use with node-llama-cpp (recommended)
Ensure you have node.js installed:
brew install nodejs
CLI
Chat with the model:
npx -y node-llama-cpp chat hf:giladgd/Qwen3-Reranker-8B-GGUF:Q4_K_M
Code
Use it in your project:
npm install node-llama-cpp
import {getLlama, resolveModelFile, LlamaChatSession} from "node-llama-cpp";
const modelUri = "hf:giladgd/Qwen3-Reranker-8B-GGUF:Q4_K_M";
const llama = await getLlama();
const model = await llama.loadModel({
modelPath: await resolveModelFile(modelUri)
});
const context = await model.createContext();
const session = new LlamaChatSession({
contextSequence: context.getSequence()
});
const q1 = "Hi there, how are you?";
console.log("User: " + q1);
const a1 = await session.prompt(q1);
console.log("AI: " + a1);
Read the getting started guide to quickly scaffold a new
node-llama-cppproject
Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
brew install llama.cpp
CLI
llama-cli -hf giladgd/Qwen3-Reranker-8B-GGUF:Q4_K_M -p "The meaning to life and the universe is"
Server
llama-server -hf giladgd/Qwen3-Reranker-8B-GGUF:Q4_K_M -c 2048
- Downloads last month
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# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="giladgd/Qwen3-Reranker-8B-GGUF", filename="", )