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

Libraries

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

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Voodisss/Qwen3-Reranker-0.6B-GGUF-llama_cpp",
	filename="Qwen3-Reranker-0.6B-Q2_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 Voodisss/Qwen3-Reranker-0.6B-GGUF-llama_cpp with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Voodisss/Qwen3-Reranker-0.6B-GGUF-llama_cpp:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Voodisss/Qwen3-Reranker-0.6B-GGUF-llama_cpp:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Voodisss/Qwen3-Reranker-0.6B-GGUF-llama_cpp:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Voodisss/Qwen3-Reranker-0.6B-GGUF-llama_cpp: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 Voodisss/Qwen3-Reranker-0.6B-GGUF-llama_cpp:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Voodisss/Qwen3-Reranker-0.6B-GGUF-llama_cpp: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 Voodisss/Qwen3-Reranker-0.6B-GGUF-llama_cpp:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Voodisss/Qwen3-Reranker-0.6B-GGUF-llama_cpp:Q4_K_M

Use Docker

docker model run hf.co/Voodisss/Qwen3-Reranker-0.6B-GGUF-llama_cpp:Q4_K_M

LM Studio
Jan
Ollama
How to use Voodisss/Qwen3-Reranker-0.6B-GGUF-llama_cpp with Ollama:
```
ollama run hf.co/Voodisss/Qwen3-Reranker-0.6B-GGUF-llama_cpp:Q4_K_M
```

Unsloth Studio new

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

Using HuggingFace Spaces for Unsloth

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

Pi new

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

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Voodisss/Qwen3-Reranker-0.6B-GGUF-llama_cpp: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": "Voodisss/Qwen3-Reranker-0.6B-GGUF-llama_cpp:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Voodisss/Qwen3-Reranker-0.6B-GGUF-llama_cpp with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Voodisss/Qwen3-Reranker-0.6B-GGUF-llama_cpp: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 Voodisss/Qwen3-Reranker-0.6B-GGUF-llama_cpp:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use Voodisss/Qwen3-Reranker-0.6B-GGUF-llama_cpp with Docker Model Runner:
```
docker model run hf.co/Voodisss/Qwen3-Reranker-0.6B-GGUF-llama_cpp:Q4_K_M
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Voodisss/Qwen3-Reranker-0.6B-GGUF-llama_cpp:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3-Reranker-0.6B-GGUF-llama_cpp-Q4_K_M

List all available models

lemonade list

Qwen3-Reranker-0.6B — GGUF (llama.cpp)

Working GGUF of Qwen/Qwen3-Reranker-0.6B for llama.cpp. Converted 2025-03-09 with the official convert_hf_to_gguf.py.

Other sizes: 0.6B (this) · 4B · 8B

Quantization quality comparison (Qwen3-Reranker-0.6B)

Benchmarked on MTEB AskUbuntuDupQuestions (361 queries) via llama-server /v1/rerank on RTX 3090. All quants produced from the same F16 source using llama-quantize.

Quant	Size	NDCG@10	MAP@10	MRR@10	Δ NDCG@10
F16	1.12 GB	0.6688	0.5143	0.7317	baseline
Q8_0	0.60 GB	0.6677	0.5143	0.7329	-0.2%
Q6_K	0.46 GB	0.6691	0.5156	0.7353	+0.0%
Q5_K_M	0.41 GB	0.6671	0.5138	0.7377	-0.3%
Q5_0	0.41 GB	0.6678	0.5118	0.7423	-0.2%
Q4_K_M	0.37 GB	0.6669	0.5120	0.7345	-0.3%
Q4_0	0.36 GB	0.6556	0.5010	0.7211	-2.0%
Q3_K_M	0.32 GB	0.6551	0.5004	0.7354	-2.1%
Q2_K	0.28 GB	0.4770	0.3104	0.5668	-28.7%

Takeaway: Q4_K_M (0.37 GB) is the sweet spot for 0.6B — 3x smaller than F16 with only 0.3% quality loss. Below Q4_K_M, quality starts to degrade: Q4_0 and Q3_K_M drop ~2%, and Q2_K is unusable (-28.7%). Smaller models are more sensitive to quantization than larger ones.

Does it work?

Yes. Most community GGUFs of Qwen3-Reranker produce garbage scores (4.5e-23) because they're missing reranker-specific tensors. See llama.cpp #16407. This one works:

Doc 0 (relevant):   relevance_score = 0.98XX
Doc 1 (irrelevant): relevance_score = 0.00XX

Quick start

llama-server -m Qwen3-Reranker-0.6B-f16.gguf --reranking --pooling rank --embedding --port 8081

curl http://localhost:8081/v1/rerank \
  -H "Content-Type: application/json" \
  -d '{
    "query": "employment termination notice period",
    "documents": [
      "The Labour Code requires 30 calendar days written notice.",
      "Corporate tax rates for small enterprises."
    ]
  }'

Use /v1/rerank, not /v1/embeddings. The embeddings endpoint returns zeros for reranker models.

What's different about this GGUF?

The official convert_hf_to_gguf.py detects Qwen3-Reranker and does things naive converters skip:

Extracts cls.output.weight (the yes/no classifier) from lm_head
Sets pooling_type = RANK metadata
Bakes in the rerank chat template
Sets classifier.output_labels = ["yes", "no"]

Without these, llama-server has nothing to compute scores from.

models.ini example

[Qwen3-Reranker-0.6B-f16]
model = /path/to/Qwen3-Reranker-0.6B-f16.gguf
reranking = true
pooling = rank
embedding = true
ctx-size = 32768

For a full multi-model setup guide (embedding + reranking + chat on one server), see the llama-server Qwen3 guide.

Convert it yourself

pip install huggingface_hub gguf torch safetensors sentencepiece
python -c "from huggingface_hub import snapshot_download; snapshot_download('Qwen/Qwen3-Reranker-0.6B', local_dir='Qwen3-Reranker-0.6B-src')"
python convert_hf_to_gguf.py --outtype f16 --outfile Qwen3-Reranker-0.6B-f16.gguf Qwen3-Reranker-0.6B-src/

License

Apache 2.0 — same as the original model.

Downloads last month: 974

GGUF

Model size

0.6B params

Architecture

qwen3

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Inference Providers NEW

Text Ranking

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Model tree for Voodisss/Qwen3-Reranker-0.6B-GGUF-llama_cpp

Base model

Qwen/Qwen3-0.6B-Base

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Collection including Voodisss/Qwen3-Reranker-0.6B-GGUF-llama_cpp

Qwen3-Reranker GGUF for llama.cpp

Collection

Working Qwen3-Reranker GGUFs (0.6B, 4B, 8B) converted with the official convert_hf_to_gguf.py. Most community conversions are broken — missing cls.out • 3 items • Updated Mar 9