Instructions to use nativ-community/clef-MLX-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use nativ-community/clef-MLX-8bit 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("nativ-community/clef-MLX-8bit") config = load_config("nativ-community/clef-MLX-8bit") # 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
- Pi
How to use nativ-community/clef-MLX-8bit with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "nativ-community/clef-MLX-8bit"
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": "nativ-community/clef-MLX-8bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent
How to use nativ-community/clef-MLX-8bit 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 "nativ-community/clef-MLX-8bit"
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 nativ-community/clef-MLX-8bit
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use nativ-community/clef-MLX-8bit with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "nativ-community/clef-MLX-8bit"
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 "nativ-community/clef-MLX-8bit" \ --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-8bit
MLX conversion of Cloudflare/clef for mlx-vlm. Clef is a decision model: it returns a probability for every option of every question in one forward pass and does not generate text.
Clef support is not yet in an mlx-vlm release:
pip install "git+https://github.com/Lazarus-931/mlx-vlm.git@feat/clef"
from mlx_vlm import load, predict
model, processor = load("nativ-community/clef-MLX-8bit")
result = predict(model, processor, "Please refund my duplicate charge", {
"department": {
"type": "choice",
"instructions": "Which team should handle this ticket?",
"criteria": ["billing", "technical", "sales"],
},
})
print(result["answers"]["department"]["value"])
| Field | Value |
|---|---|
| Source | Cloudflare/clef |
| Source revision | ed3eed331870db2eff4b0db01237128ede8a00ce |
| Quantization | affine 8-bit, group size 64 |
| mlx-vlm | Lazarus-931/mlx-vlm@c16f81aa |
| Verification | Identical token ids on 11/11 reference records (text, image, video, image+video, two images, max_pixels, fps, num_frames); same answer as Cloudflare's torch reference (bf16) on 25/25 questions; largest probability gap 0.0349 |
- Downloads last month
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Model size
27B params
Tensor type
BF16
·
U32 ·
Hardware compatibility
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8-bit