Qwen3-VL-2B-Instruct-4bit

Instructions to use TerminatorPower/Qwen3-VL-2B-Instruct-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TerminatorPower/Qwen3-VL-2B-Instruct-4bit with MLX:

# Make sure mlx-vlm is installed
# pip install --upgrade mlx-vlm

from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config

# Load the model
model, processor = load("TerminatorPower/Qwen3-VL-2B-Instruct-4bit")
config = load_config("TerminatorPower/Qwen3-VL-2B-Instruct-4bit")

# Prepare input
image = ["http://images.cocodataset.org/val2017/000000039769.jpg"]
prompt = "Describe this image."

# Apply chat template
formatted_prompt = apply_chat_template(
    processor, config, prompt, num_images=1
)

# Generate output
output = generate(model, processor, formatted_prompt, image)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use TerminatorPower/Qwen3-VL-2B-Instruct-4bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "TerminatorPower/Qwen3-VL-2B-Instruct-4bit"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "TerminatorPower/Qwen3-VL-2B-Instruct-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use TerminatorPower/Qwen3-VL-2B-Instruct-4bit with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "TerminatorPower/Qwen3-VL-2B-Instruct-4bit"

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 TerminatorPower/Qwen3-VL-2B-Instruct-4bit

Run Hermes

hermes

OpenClaw new

How to use TerminatorPower/Qwen3-VL-2B-Instruct-4bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "TerminatorPower/Qwen3-VL-2B-Instruct-4bit"

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 "TerminatorPower/Qwen3-VL-2B-Instruct-4bit" \
  --custom-provider-id mlx-lm \
  --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-Instruct-4bit / README.md

TerminatorPower

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metadata

base_model: Qwen/Qwen3-VL-2B-Instruct
library_name: mlx
pipeline_tag: image-text-to-text
tags:
  - mlx
  - qwen3-vl
  - vision-language-model
  - quantized
  - 4bit
license: apache-2.0

Qwen3-VL-2B-Instruct-4bit

A verbatim mirror of mlx-community/Qwen3-VL-2B-Instruct-4bit, kept here so the Vanta iOS app always has a stable lower-RAM model to download from.

Run it on your iPhone with Vanta

This is one of the built-in one-tap downloads in Vanta - Local AI LLM Chat, a local-first AI chat app for iPhone and iPad. Vanta runs models like this one fully on-device with Apple's MLX framework - no account and no cloud, your chats stay on your device. Because it's a vision-capable model, you can also chat about images.

Vanta recommends this smaller model on RAM-tight devices where the 4B Thinking model is likely too heavy.

Download Vanta on the App Store ->

This is a copy. Every model file in this repository is an exact copy of mlx-community/Qwen3-VL-2B-Instruct-4bit. We cloned it so that Vanta Client always has a reliable, always-available source to download this model from, independent of any upstream changes. All credit for the model weights and the MLX conversion goes to mlx-community, Qwen, and the original authors.

Model Details

Original Model: Qwen/Qwen3-VL-2B-Instruct
Upstream MLX Repo: mlx-community/Qwen3-VL-2B-Instruct-4bit
Quantization: 4-bit
Format: MLX SafeTensors
Framework: mlx-vlm
Model Type: qwen3_vl
Task: Image-text-to-text
Disk Size: ~1.78 GB

Conversion Details

The upstream model was converted to MLX format from Qwen/Qwen3-VL-2B-Instruct using mlx-vlm version 0.3.4.

Related Models

Default Vanta pick: TerminatorPower/Qwen3-VL-4B-Thinking-4bit
Upstream MLX repo: mlx-community/Qwen3-VL-2B-Instruct-4bit
Original: Qwen/Qwen3-VL-2B-Instruct

Usage

from mlx_vlm import load, generate

model, processor = load("TerminatorPower/Qwen3-VL-2B-Instruct-4bit")

output = generate(
    model,
    processor,
    prompt="Describe this image.",
    image="path/to/image.jpg",
    max_tokens=512
)
print(output)

CLI:

python3 -m mlx_vlm.generate \
  --model TerminatorPower/Qwen3-VL-2B-Instruct-4bit \
  --image path/to/image.jpg \
  --prompt "Describe this image."

License

This model inherits the Apache 2.0 license from the original Qwen model. The mirror does not add any restrictions.