Instructions to use worndown/Qwen3-Reranker-0.6B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use worndown/Qwen3-Reranker-0.6B-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("worndown/Qwen3-Reranker-0.6B-GGUF", dtype="auto")

llama-cpp-python

How to use worndown/Qwen3-Reranker-0.6B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="worndown/Qwen3-Reranker-0.6B-GGUF",
	filename="Qwen3-Reranker-0.6B-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 worndown/Qwen3-Reranker-0.6B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf worndown/Qwen3-Reranker-0.6B-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf worndown/Qwen3-Reranker-0.6B-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf worndown/Qwen3-Reranker-0.6B-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf worndown/Qwen3-Reranker-0.6B-GGUF:F16

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 worndown/Qwen3-Reranker-0.6B-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf worndown/Qwen3-Reranker-0.6B-GGUF:F16

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 worndown/Qwen3-Reranker-0.6B-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf worndown/Qwen3-Reranker-0.6B-GGUF:F16

Use Docker

docker model run hf.co/worndown/Qwen3-Reranker-0.6B-GGUF:F16

LM Studio
Jan
Ollama
How to use worndown/Qwen3-Reranker-0.6B-GGUF with Ollama:
```
ollama run hf.co/worndown/Qwen3-Reranker-0.6B-GGUF:F16
```

Unsloth Studio new

How to use worndown/Qwen3-Reranker-0.6B-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 worndown/Qwen3-Reranker-0.6B-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 worndown/Qwen3-Reranker-0.6B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for worndown/Qwen3-Reranker-0.6B-GGUF to start chatting

Pi new

How to use worndown/Qwen3-Reranker-0.6B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf worndown/Qwen3-Reranker-0.6B-GGUF:F16

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": "worndown/Qwen3-Reranker-0.6B-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use worndown/Qwen3-Reranker-0.6B-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 worndown/Qwen3-Reranker-0.6B-GGUF:F16

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 worndown/Qwen3-Reranker-0.6B-GGUF:F16

Run Hermes

hermes

Docker Model Runner
How to use worndown/Qwen3-Reranker-0.6B-GGUF with Docker Model Runner:
```
docker model run hf.co/worndown/Qwen3-Reranker-0.6B-GGUF:F16
```

Lemonade

How to use worndown/Qwen3-Reranker-0.6B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull worndown/Qwen3-Reranker-0.6B-GGUF:F16

Run and chat with the model

lemonade run user.Qwen3-Reranker-0.6B-GGUF-F16

List all available models

lemonade list

Qwen3-Reranker-0.6B

Highlights

The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. Building upon the dense foundational models of the Qwen3 series, it provides a comprehensive range of text embeddings and reranking models in various sizes (0.6B, 4B, and 8B). This series inherits the exceptional multilingual capabilities, long-text understanding, and reasoning skills of its foundational model. The Qwen3 Embedding series represents significant advancements in multiple text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bitext mining.

Exceptional Versatility: The embedding model has achieved state-of-the-art performance across a wide range of downstream application evaluations. The 8B size embedding model ranks No.1 in the MTEB multilingual leaderboard (as of June 5, 2025, score 70.58), while the reranking model excels in various text retrieval scenarios.

Comprehensive Flexibility: The Qwen3 Embedding series offers a full spectrum of sizes (from 0.6B to 8B) for both embedding and reranking models, catering to diverse use cases that prioritize efficiency and effectiveness. Developers can seamlessly combine these two modules. Additionally, the embedding model allows for flexible vector definitions across all dimensions, and both embedding and reranking models support user-defined instructions to enhance performance for specific tasks, languages, or scenarios.

Multilingual Capability: The Qwen3 Embedding series offer support for over 100 languages, thanks to the multilingual capabilites of Qwen3 models. This includes various programming languages, and provides robust multilingual, cross-lingual, and code retrieval capabilities.

Model Overview

Qwen3-Reranker-0.6B has the following features:

Model Type: Text Reranking
Supported Languages: 100+ Languages
Number of Paramaters: 0.6B
Context Length: 32k

Downloads last month: 6

GGUF

Model size

0.6B params

Architecture

qwen3

Hardware compatibility

16-bit

Inference Providers NEW

Text Ranking

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