Instructions to use DreamFoundries/Qwen3-VL-4B-Instruct-3bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DreamFoundries/Qwen3-VL-4B-Instruct-3bit 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("DreamFoundries/Qwen3-VL-4B-Instruct-3bit")
config = load_config("DreamFoundries/Qwen3-VL-4B-Instruct-3bit")

# 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 DreamFoundries/Qwen3-VL-4B-Instruct-3bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "DreamFoundries/Qwen3-VL-4B-Instruct-3bit"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "DreamFoundries/Qwen3-VL-4B-Instruct-3bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use DreamFoundries/Qwen3-VL-4B-Instruct-3bit 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 "DreamFoundries/Qwen3-VL-4B-Instruct-3bit"

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 DreamFoundries/Qwen3-VL-4B-Instruct-3bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use DreamFoundries/Qwen3-VL-4B-Instruct-3bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "DreamFoundries/Qwen3-VL-4B-Instruct-3bit"

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 "DreamFoundries/Qwen3-VL-4B-Instruct-3bit" \
  --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-4B-Instruct-3bit / README.md

DreamFoundries

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	---
	license: apache-2.0
	base_model: mlx-community/Qwen3-VL-4B-Instruct-3bit
	pipeline_tag: image-text-to-text
	tags:
	- mlx
	- vision
	---

	# Qwen3-VL-4B-Instruct-3bit (repaired)

	[![Open in MLXHub](https://mlxhub.app/assets/badge-open-in-mlxhub.svg)](https://mlxhub.app/open-model?repo=DreamFoundries/Qwen3-VL-4B-Instruct-3bit)

	Re-upload of [mlx-community/Qwen3-VL-4B-Instruct-3bit](https://huggingface.co/mlx-community/Qwen3-VL-4B-Instruct-3bit) with one fix: the upstream repo ships a stale `model.safetensors.index.json` describing a 2-shard bf16 checkpoint (`model-00001-of-00002.safetensors` / `model-00002-of-00002.safetensors`) that does not exist in that repo — only a single consolidated `model.safetensors` (3-bit, MLX format, 1219 tensors) is actually present. Any loader that trusts the index blindly (mlx-swift-lm's `safetensorWeightURLs`, among others) fails trying to open a shard that was never there, and no re-download fixes it since the missing files simply don't exist upstream.

	This repo removes the stale index. Weights are otherwise byte-identical to upstream — same single `model.safetensors`, same digest.

	All credit for the conversion to MLX and the 3-bit quantization goes to [mlx-community](https://huggingface.co/mlx-community). All credit for the base model goes to the [Qwen team](https://huggingface.co/Qwen) (Apache-2.0).

	## Usage

	```bash
	pip install -U mlx-vlm
	python -m mlx_vlm.generate --model DreamFoundries/Qwen3-VL-4B-Instruct-3bit --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image>
	```

	Or open directly in [MLXHub](https://mlxhub.app) on iOS/iPadOS via the badge above.

	---

	[![Download MLXHub](https://mlxhub.app/assets/badge-download-mlxhub.svg)](https://apps.apple.com/app/apple-store/id6766485144?pt=121945436&ct=HuggingFace&mt=8)