Instructions to use gorse-io/Qwen3-VL-Reranker-2B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use gorse-io/Qwen3-VL-Reranker-2B-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf gorse-io/Qwen3-VL-Reranker-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf gorse-io/Qwen3-VL-Reranker-2B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf gorse-io/Qwen3-VL-Reranker-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf gorse-io/Qwen3-VL-Reranker-2B-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 gorse-io/Qwen3-VL-Reranker-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf gorse-io/Qwen3-VL-Reranker-2B-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 gorse-io/Qwen3-VL-Reranker-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf gorse-io/Qwen3-VL-Reranker-2B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/gorse-io/Qwen3-VL-Reranker-2B-GGUF:Q4_K_M

Llama new
LM Studio
Jan
Ollama
How to use gorse-io/Qwen3-VL-Reranker-2B-GGUF with Ollama:
```
ollama run hf.co/gorse-io/Qwen3-VL-Reranker-2B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use gorse-io/Qwen3-VL-Reranker-2B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf gorse-io/Qwen3-VL-Reranker-2B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "gorse-io/Qwen3-VL-Reranker-2B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use gorse-io/Qwen3-VL-Reranker-2B-GGUF with Docker Model Runner:
```
docker model run hf.co/gorse-io/Qwen3-VL-Reranker-2B-GGUF:Q4_K_M
```

Lemonade

How to use gorse-io/Qwen3-VL-Reranker-2B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull gorse-io/Qwen3-VL-Reranker-2B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3-VL-Reranker-2B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use gorse-io/Qwen3-VL-Reranker-2B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf gorse-io/Qwen3-VL-Reranker-2B-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 gorse-io/Qwen3-VL-Reranker-2B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use gorse-io/Qwen3-VL-Reranker-2B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf gorse-io/Qwen3-VL-Reranker-2B-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "gorse-io/Qwen3-VL-Reranker-2B-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Qwen3-VL-Reranker-2B

GGUF conversion of Qwen/Qwen3-VL-Reranker-2B, a query/document reranker, not a general-purpose text-generation or embedding model.

Files

Qwen3-VL-Reranker-2B-BF16.gguf
Qwen3-VL-Reranker-2B-Q8_0.gguf
Qwen3-VL-Reranker-2B-Q4_K_M.gguf
mmproj-Qwen3-VL-Reranker-2B-F16.gguf

The language GGUF contains the yes/no classifier; the separate F16 projector preserves the vision encoder and projection weights. Keep both for an eventual multimodal integration; do not quantize the projector with the language quantizer.

Text reranking with llama.cpp

Use llama.cpp with Qwen3-VL reranker conversion and RANK pooling support (the pipeline was inspected against commit abee0c8476b585b0d081d580b5ce21d0addc22c4):

llama-server -hf gorse-io/Qwen3-VL-Reranker-2B-GGUF:Q8_0 \
    --embedding --reranking --pooling rank --host 127.0.0.1 --port 8080

For the 4-bit variant, replace :Q8_0 with :Q4_K_M. For a local file, replace -hf ... with -m Qwen3-VL-Reranker-2B-Q8_0.gguf. The pipeline builds llama-quantize; install or build llama-server separately.

curl http://127.0.0.1:8080/v1/rerank \
    -H 'Content-Type: application/json' \
    -d '{"query":"What is the capital of China?","documents":["Beijing is the capital of China.","Paris is the capital of France."],"top_n":2}'

Multimodal limitation: the inspected llama.cpp /v1/rerank API accepts text queries and text documents only. Image URLs are treated as text; adding --mmproj does not enable image/video reranking through that endpoint. The projector is provided as an artifact, not as a claim of end-to-end multimodal reranking support. Use the upstream Transformers implementation for multimodal inputs until a compatible GGUF runtime integration is available and tested. The converter's fixed text rerank prompt is not the official VL processor; custom instructions and upstream score parity are not guaranteed.

Conversion

Preserve the original source README.md beside the weights: llama.cpp uses README/name hints to recognize the reranker and emit RANK pooling, yes/no classifier labels, and cls.output.weight instead of the LM head. Reserve space for source weights, conversion temporaries, and all four outputs; 8B needs substantially more RAM and disk than 2B. Offline script tests validate commands and manifests, not actual GGUF contents or inference quality.