Instructions to use gnukeith/Ocelot-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gnukeith/Ocelot-MLX 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("gnukeith/Ocelot-MLX")
config = load_config("gnukeith/Ocelot-MLX")

# 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 gnukeith/Ocelot-MLX with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "gnukeith/Ocelot-MLX"

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": "gnukeith/Ocelot-MLX"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use gnukeith/Ocelot-MLX with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "gnukeith/Ocelot-MLX"

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 "gnukeith/Ocelot-MLX" \
  --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"

Hermes Agent

How to use gnukeith/Ocelot-MLX 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 "gnukeith/Ocelot-MLX"

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 gnukeith/Ocelot-MLX

Run Hermes

hermes

Atomic Chat

Ocelot-MLX / README.md

gnukeith

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	---
	license: apache-2.0
	base_model: [bravesoftware/Ocelot-1-VL, Qwen/Qwen3-VL-4B-Instruct]
	library_name: mlx
	pipeline_tag: image-text-to-text
	tags: [mlx, qwen3-vl, vision, summarization, 4-bit]
	---

	# Ocelot-1-VL MLX 4-bit

	Recommended MLX 4-bit, group-size 64 conversion of [Ocelot-1-VL](https://huggingface.co/bravesoftware/Ocelot-1-VL), merged into its BF16 Qwen3-VL-4B-Instruct base. Effective quantization is 5.577 bits/weight because sensitive and unsupported tensors remain at higher precision.

	This model is specialized only for webpage summarization. Follow the strict prompt contract and limitations in the original model card.

	These are final MLX weights, not conversion inputs. Users can open a local browser interface directly after installing the MLX runtime:

	```bash
	pip install 'mlx-vlm @ git+https://github.com/Blaizzy/mlx-vlm.git'
	mlx_vlm.chat_ui --model gnukeith/Ocelot-MLX
	```

	The runtime downloads the model from Hugging Face automatically. No cloning, conversion, or Python code is required.

	Direct command-line inference is also available:

	```bash
	mlx_vlm.generate --model gnukeith/Ocelot-MLX --prompt 'The is the text of a webpage: <page>Page text here</page> Summarise the content between the <page> tags, or if no content is found use the screenshots provided, in the Brave Summary style.' --max-tokens 512
	```

	For screenshots, add `--image webpage.png` and begin the prompt with `The following is a screenshot of a webpage:`. Converted with MLX-VLM revision `0b1d25e334686bd36dda71b2307d186dbb3e7859`. Text and screenshot tests passed. An Apple M4 Pro test used 3.34 GB peak memory and measured 45 prompt tokens/s and 15 generation tokens/s.