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

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	---
	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
	---

	<p align="center">
	<a href="https://apps.apple.com/tr/app/vanta-local-ai-llm-chat/id6758898098">
	<img src="banner.png" alt="Vanta - Local AI LLM Chat" width="100%" />
	</a>
	</p>

	<h1 align="center">Qwen3-VL-2B-Instruct-4bit</h1>

	<p align="center">
	A verbatim mirror of
	<a href="https://huggingface.co/mlx-community/Qwen3-VL-2B-Instruct-4bit">mlx-community/Qwen3-VL-2B-Instruct-4bit</a>,
	kept here so the <b>Vanta</b> iOS app always has a stable lower-RAM model to download from.
	</p>

	## 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 ->](https://apps.apple.com/tr/app/vanta-local-ai-llm-chat/id6758898098)

	---

	> This is a copy. Every model file in this repository is an exact copy of
	> [`mlx-community/Qwen3-VL-2B-Instruct-4bit`](https://huggingface.co/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](https://huggingface.co/mlx-community), [Qwen](https://huggingface.co/Qwen),
	> and the original authors.

	---

	## Model Details

	- Original Model: [Qwen/Qwen3-VL-2B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-2B-Instruct)
	- Upstream MLX Repo: [mlx-community/Qwen3-VL-2B-Instruct-4bit](https://huggingface.co/mlx-community/Qwen3-VL-2B-Instruct-4bit)
	- Quantization: 4-bit
	- Format: MLX SafeTensors
	- Framework: [mlx-vlm](https://github.com/Blaizzy/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`](https://huggingface.co/Qwen/Qwen3-VL-2B-Instruct)
	using `mlx-vlm` version 0.3.4.

	## Related Models

	- Default Vanta pick: [TerminatorPower/Qwen3-VL-4B-Thinking-4bit](https://huggingface.co/TerminatorPower/Qwen3-VL-4B-Thinking-4bit)
	- Upstream MLX repo: [mlx-community/Qwen3-VL-2B-Instruct-4bit](https://huggingface.co/mlx-community/Qwen3-VL-2B-Instruct-4bit)
	- Original: [Qwen/Qwen3-VL-2B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-2B-Instruct)

	## Usage

	```python
	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:

	```bash
	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](https://huggingface.co/Qwen/Qwen3-VL-2B-Instruct)
	from the original Qwen model. The mirror does not add any restrictions.