Instructions to use mlx-community/Inkling-Small-mxfp4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlx-community/Inkling-Small-mxfp4 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("mlx-community/Inkling-Small-mxfp4")
config = load_config("mlx-community/Inkling-Small-mxfp4")

# 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 mlx-community/Inkling-Small-mxfp4 with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "mlx-community/Inkling-Small-mxfp4"

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": "mlx-community/Inkling-Small-mxfp4"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use mlx-community/Inkling-Small-mxfp4 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 "mlx-community/Inkling-Small-mxfp4"

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 mlx-community/Inkling-Small-mxfp4

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use mlx-community/Inkling-Small-mxfp4 with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "mlx-community/Inkling-Small-mxfp4"

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 "mlx-community/Inkling-Small-mxfp4" \
  --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"

Inkling-Small-mxfp4 / README.md

pcuenq HF Staff

Update README.md (#1)

8bd08ce about 2 months ago

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metadata

language: en
library_name: mlx
pipeline_tag: image-text-to-text
base_model: thinkingmachines/Inkling-Small
tags:
  - mlx

Inkling Small MXFP4

MXFP4 version of thinkingmachines/Inkling-Small.

This checkpoint uses less memory than thinkingmachines/Inkling-Small-NVFP4, but quantizes more tensors to MXFP4. The official NVFP4 checkpoint, on the other hand, only applies 4-bit quantization to routed experts, so quality should be better.

For the best quality, please run the official NVFP4 checkpoint directly with MLX, there's no need to convert:

mlx_vlm.generate --prompt "who are you?" --model thinkingmachines/Inkling-Small-NVFP4

Or, if you want to try the version in this repo (lower memory consumption, faster):

mlx_vlm.generate --prompt "who are you?" --model mlx-community/Inkling-Small-mxfp4

Note: please make sure you use the Inkling Small mlx-vlm PR.