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

Libraries

How to use abenzerps/Clef-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("abenzerps/Clef-MLX")
config = load_config("abenzerps/Clef-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 abenzerps/Clef-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 "abenzerps/Clef-MLX"

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": "abenzerps/Clef-MLX"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use abenzerps/Clef-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 "abenzerps/Clef-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 abenzerps/Clef-MLX

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use abenzerps/Clef-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 "abenzerps/Clef-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 "abenzerps/Clef-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"

Clef-MLX / Clef-MLX-4bit /README.md

abenzerps

Initial Commit: Add MLX quantizations for Cloudflare/clef

d81b074 25 days ago

preview code

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815 Bytes

metadata

base_model: Cloudflare/clef
library_name: mlx
pipeline_tag: image-text-to-text
tags:
  - mlx
  - multimodal
  - decision-model
  - 4bit
license: apache-2.0

clef-mlx-4bit

This model is a 4-bit quantized version of Cloudflare/clef using the MLX framework.

Model Details

Base Model: Cloudflare/clef
Architecture: + Joint Schema Head
Quantization: 4-bit affine (group size: 64)
Effective Bits Per Weight: 4.695 bpw
Model Size: ~16 GB
License: Apache-2.0

Files Included

Quantized backbone weights (, )
Joint Schema Head weights (, )
Model execution code ()
Processor & Tokenizer configs (, , , )
Model Configuration (, )

abenzerps
/

Clef-MLX

clef-mlx-4bit

Model Details

Files Included

Usage with MLX