Instructions to use spicyneuron/Qwen3.5-4B-MLX-6.8bit-vision with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use spicyneuron/Qwen3.5-4B-MLX-6.8bit-vision 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("spicyneuron/Qwen3.5-4B-MLX-6.8bit-vision")
config = load_config("spicyneuron/Qwen3.5-4B-MLX-6.8bit-vision")

# 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 spicyneuron/Qwen3.5-4B-MLX-6.8bit-vision with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "spicyneuron/Qwen3.5-4B-MLX-6.8bit-vision"

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": "spicyneuron/Qwen3.5-4B-MLX-6.8bit-vision"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use spicyneuron/Qwen3.5-4B-MLX-6.8bit-vision 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 "spicyneuron/Qwen3.5-4B-MLX-6.8bit-vision"

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 spicyneuron/Qwen3.5-4B-MLX-6.8bit-vision

Run Hermes

hermes

OpenClaw new

How to use spicyneuron/Qwen3.5-4B-MLX-6.8bit-vision with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "spicyneuron/Qwen3.5-4B-MLX-6.8bit-vision"

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 "spicyneuron/Qwen3.5-4B-MLX-6.8bit-vision" \
  --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"

Qwen/Qwen3.5-4B optimized for MLX.

A mixed-precision quant that balances speed, memory, and accuracy.
4-bit baseline with important layers at higher precision.
This quant supports image input and requires a vision-capable server.

Usage

# Start server at http://localhost:8080/v1/chat/completions
uvx --from mlx-vlm mlx_vlm.server \ 
  --host 127.0.0.1 \
  --port 8080 \
  --model spicyneuron/Qwen3.5-4B-MLX-6.8bit-vision

Benchmarks

metric	mlx-community/Qwen3.5-4B-MLX-4bit	mlx-community/Qwen3.5-4B-OptiQ-4bit	6.8 bit (this model)
bpw	4.503	6.219	6.750
base memory	2.205	3.045	3.305
peak memory (1024/512)	3.625	4.541	4.814
prompt tok/s (1024)	2674.115 ± 3.462	2661.445 ± 1.504	2589.364 ± 13.109
gen tok/s (512)	166.004 ± 0.312	135.779 ± 0.031	127.137 ± 0.121
kl mean	0.083 ± 0.004	0.041 ± 0.002	0.007 ± 0.001
kl p95	0.180 ± 0.004	0.100 ± 0.002	0.020 ± 0.001
perplexity	5.053 ± 0.035	4.932 ± 0.034	4.921 ± 0.034

Tested on a Mac Studio M3 Ultra with:

mlx_lm.convert --hf-path ... --mlx-path ./mlx && mlx_lm.kld --baseline-model ./mlx
mlx_lm.perplexity --sequence-length 512 --seed 123
mlx_lm.benchmark --prompt-tokens 1024 --generation-tokens 512 --num-trials 5

Required PRs:

Methodology

Quantized with a mlx-vlm fork. MLX quantization options differ than llama.cpp, but the principles are the same:

Sensitive layers like MoE routing, attention, and output embeddings get higher precision
More tolerant layers like MoE experts get lower precision

Downloads last month: 93

Safetensors

Model size

1B params

Tensor type

BF16

U32

F32

MLX

Hardware compatibility

4-bit

Model tree for spicyneuron/Qwen3.5-4B-MLX-6.8bit-vision

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