Instructions to use Accio-Lab/occamy-1.0-MLX-5bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Accio-Lab/occamy-1.0-MLX-5bit 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-5bit")

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-5bit 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-5bit"

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-5bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use Accio-Lab/occamy-1.0-MLX-5bit 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-5bit"

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-5bit"
# 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-5bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use Accio-Lab/occamy-1.0-MLX-5bit 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-5bit"

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-5bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Accio-Lab/occamy-1.0-MLX-5bit 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-5bit"

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-5bit" \
  --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-5bit / quality /batch.json

Eang

Release native MLX 5bit with artifact, HTTP and paired BF16 quality checks

b3a65ec verified 26 days ago

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711 Bytes

{
  "source_revision": "8f8e0e58a3c9df042be1a3fa2c191fd8047acfb8",
  "llama_cpp_revision": "972d2313bc0bf0a45f634f77d95c9fb03aeab12c",
  "apex_revision": "636cec7e3f8d308e4162ab2bd5ec4b56807dd806",
  "mlx": [
    {
      "key": "5bit",
      "mode": "affine",
      "bits": 5,
      "group_size": 64
    },
    {
      "key": "mxfp4",
      "mode": "mxfp4",
      "bits": 4,
      "group_size": 32
    },
    {
      "key": "mxfp8",
      "mode": "mxfp8",
      "bits": 8,
      "group_size": 32
    },
    {
      "key": "nvfp4",
      "mode": "nvfp4",
      "bits": 4,
      "group_size": 16
    }
  ],
  "recipes": [
    "APEX-I-Compact",
    "APEX-I-Quality",
    "APEX-I-Balanced",
    "APEX-I-Mini"
  ]
}

	{
	"source_revision": "8f8e0e58a3c9df042be1a3fa2c191fd8047acfb8",
	"llama_cpp_revision": "972d2313bc0bf0a45f634f77d95c9fb03aeab12c",
	"apex_revision": "636cec7e3f8d308e4162ab2bd5ec4b56807dd806",
	"mlx": [
	{
	"key": "5bit",
	"mode": "affine",
	"bits": 5,
	"group_size": 64
	},
	{
	"key": "mxfp4",
	"mode": "mxfp4",
	"bits": 4,
	"group_size": 32
	},
	{
	"key": "mxfp8",
	"mode": "mxfp8",
	"bits": 8,
	"group_size": 32
	},
	{
	"key": "nvfp4",
	"mode": "nvfp4",
	"bits": 4,
	"group_size": 16
	}
	],
	"recipes": [
	"APEX-I-Compact",
	"APEX-I-Quality",
	"APEX-I-Balanced",
	"APEX-I-Mini"
	]
	}