Instructions to use jc-builds/qwen3vl-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 jc-builds/qwen3vl-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 jc-builds/qwen3vl-2b-gguf:Q8_0
# Run inference directly in the terminal:
llama cli -hf jc-builds/qwen3vl-2b-gguf:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf jc-builds/qwen3vl-2b-gguf:Q8_0
# Run inference directly in the terminal:
llama cli -hf jc-builds/qwen3vl-2b-gguf:Q8_0

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 jc-builds/qwen3vl-2b-gguf:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf jc-builds/qwen3vl-2b-gguf:Q8_0

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 jc-builds/qwen3vl-2b-gguf:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf jc-builds/qwen3vl-2b-gguf:Q8_0

Use Docker

docker model run hf.co/jc-builds/qwen3vl-2b-gguf:Q8_0

LM Studio
Jan

vLLM

How to use jc-builds/qwen3vl-2b-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jc-builds/qwen3vl-2b-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jc-builds/qwen3vl-2b-gguf",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/jc-builds/qwen3vl-2b-gguf:Q8_0

Ollama
How to use jc-builds/qwen3vl-2b-gguf with Ollama:
```
ollama run hf.co/jc-builds/qwen3vl-2b-gguf:Q8_0
```
Unsloth Desktop

How to use jc-builds/qwen3vl-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 jc-builds/qwen3vl-2b-gguf:Q8_0

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": "jc-builds/qwen3vl-2b-gguf:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use jc-builds/qwen3vl-2b-gguf with Docker Model Runner:
```
docker model run hf.co/jc-builds/qwen3vl-2b-gguf:Q8_0
```

Lemonade

How to use jc-builds/qwen3vl-2b-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jc-builds/qwen3vl-2b-gguf:Q8_0

Run and chat with the model

lemonade run user.qwen3vl-2b-gguf-Q8_0

List all available models

lemonade list

Hermes Agent

How to use jc-builds/qwen3vl-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 jc-builds/qwen3vl-2b-gguf:Q8_0

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 jc-builds/qwen3vl-2b-gguf:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use jc-builds/qwen3vl-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 jc-builds/qwen3vl-2b-gguf:Q8_0

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 "jc-builds/qwen3vl-2b-gguf:Q8_0" \
  --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-2B GGUF

This is a GGUF conversion of Qwen/Qwen3-VL-2B-Instruct - a Vision-Language Model optimized for on-device inference with llama.cpp.

Model Details

Property	Value
Original Model	Qwen3-VL-2B-Instruct
Parameters	2 billion
Quantization	Q8_0
Model Size	~1.7 GB
Vision Encoder Size	~424 MB (Q8_0)
Context Window	8,192 tokens
Architecture	Qwen3-VL with native vision encoder

Files

Qwen3VL-2B-Instruct-Q8_0.gguf - Main language model
mmproj-Qwen3VL-2B-Instruct-Q8_0.gguf - Vision encoder (mmproj)

Intended Use

This model is optimized for:

Mobile/Edge Deployment: Runs on iOS devices with 8GB+ RAM
llama.cpp Integration: Compatible with llama.cpp vision features
On-Device AI: Private, offline image understanding

Capabilities

Image Captioning: Describe images in detail
Visual Q&A: Answer questions about images
Document OCR: Extract text from documents and photos
Scene Understanding: Analyze complex visual scenes
Superior Quality: Best-in-class for 2B parameter VLMs

Usage with llama.cpp

./llama-llava-cli -m Qwen3VL-2B-Instruct-Q8_0.gguf \
  --mmproj mmproj-Qwen3VL-2B-Instruct-Q8_0.gguf \
  --image your_image.jpg \
  -p "Describe this image in detail"

Prompt Format

<|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
<|vision_start|><|vision_end|>
{prompt}<|im_end|>
<|im_start|>assistant

License

This model inherits the Apache 2.0 license from the original Qwen3-VL model.

Attribution

Original Model: Qwen3-VL-2B-Instruct by Qwen Team, Alibaba Cloud
GGUF Conversion: jc-builds

Downloads last month: 30

GGUF

Model size

2B params

Architecture

qwen3vl

Hardware compatibility

8-bit

Model tree for jc-builds/qwen3vl-2b-gguf

Base model

Qwen/Qwen3-VL-2B-Instruct

Quantized

(89)

this model