Instructions to use Accio-Lab/occamy-1.0-MLX-nvfp4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Accio-Lab/occamy-1.0-MLX-nvfp4 with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("Accio-Lab/occamy-1.0-MLX-nvfp4")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use Accio-Lab/occamy-1.0-MLX-nvfp4 with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Accio-Lab/occamy-1.0-MLX-nvfp4"

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": "Accio-Lab/occamy-1.0-MLX-nvfp4"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use Accio-Lab/occamy-1.0-MLX-nvfp4 with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "Accio-Lab/occamy-1.0-MLX-nvfp4"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "Accio-Lab/occamy-1.0-MLX-nvfp4"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "Accio-Lab/occamy-1.0-MLX-nvfp4",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use Accio-Lab/occamy-1.0-MLX-nvfp4 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 "Accio-Lab/occamy-1.0-MLX-nvfp4"

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 Accio-Lab/occamy-1.0-MLX-nvfp4

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Accio-Lab/occamy-1.0-MLX-nvfp4 with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Accio-Lab/occamy-1.0-MLX-nvfp4"

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 "Accio-Lab/occamy-1.0-MLX-nvfp4" \
  --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"

occamy-1.0-MLX-nvfp4 / conversion.json

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{
  "key": "nvfp4",
  "mode": "nvfp4",
  "bits": 4,
  "group_size": 16,
  "source_repo": "Accio-Lab/occamy-1.0",
  "source_revision": "8f8e0e58a3c9df042be1a3fa2c191fd8047acfb8",
  "source_payload_sha256_rechecked": true,
  "requantized": false,
  "conversion_backend": "Linux native MLX CUDA 12",
  "router_shared_gate_mode": "affine",
  "router_shared_gate_bits": 8,
  "router_shared_gate_group_size": 64,
  "versions": {
    "mlx": "0.32.2",
    "mlx-lm": "0.31.3",
    "transformers": "5.8.1",
    "numpy": "2.5.3"
  },
  "layout_preflight": {
    "passed": true,
    "source_index_tensors": 31666,
    "layers": 40,
    "experts_per_layer": 256,
    "groups": 120,
    "independent_expert_tensors": 30720,
    "all_shapes_checked": true,
    "each_group_dtype_uniform": true,
    "weight_payload_read": false,
    "model_instantiated": false
  },
  "layout_adapter": {
    "calls": [
      {
        "adapted": true,
        "groups": 120,
        "source_expert_tensors": 30720,
        "expert_order": "numeric 0..num_experts-1",
        "value_transform": "stack only; no cast, quantization, norm or conv transform"
      }
    ],
    "native_sanitize_calls": 1,
    "restored": true
  },
  "seconds": 42.45400047302246,
  "candidate_only": true,
  "mac_acceptance": "pending",
  "vision_included": false,
  "mtp_included": false
}

	{
	"key": "nvfp4",
	"mode": "nvfp4",
	"bits": 4,
	"group_size": 16,
	"source_repo": "Accio-Lab/occamy-1.0",
	"source_revision": "8f8e0e58a3c9df042be1a3fa2c191fd8047acfb8",
	"source_payload_sha256_rechecked": true,
	"requantized": false,
	"conversion_backend": "Linux native MLX CUDA 12",
	"router_shared_gate_mode": "affine",
	"router_shared_gate_bits": 8,
	"router_shared_gate_group_size": 64,
	"versions": {
	"mlx": "0.32.2",
	"mlx-lm": "0.31.3",
	"transformers": "5.8.1",
	"numpy": "2.5.3"
	},
	"layout_preflight": {
	"passed": true,
	"source_index_tensors": 31666,
	"layers": 40,
	"experts_per_layer": 256,
	"groups": 120,
	"independent_expert_tensors": 30720,
	"all_shapes_checked": true,
	"each_group_dtype_uniform": true,
	"weight_payload_read": false,
	"model_instantiated": false
	},
	"layout_adapter": {
	"calls": [
	{
	"adapted": true,
	"groups": 120,
	"source_expert_tensors": 30720,
	"expert_order": "numeric 0..num_experts-1",
	"value_transform": "stack only; no cast, quantization, norm or conv transform"
	}
	],
	"native_sanitize_calls": 1,
	"restored": true
	},
	"seconds": 42.45400047302246,
	"candidate_only": true,
	"mac_acceptance": "pending",
	"vision_included": false,
	"mtp_included": false
	}