Instructions to use darthcrawl/artifex-rp-orpheus-llama-3.1-8b-mlx-4bit-dwq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use darthcrawl/artifex-rp-orpheus-llama-3.1-8b-mlx-4bit-dwq 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("darthcrawl/artifex-rp-orpheus-llama-3.1-8b-mlx-4bit-dwq")

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 darthcrawl/artifex-rp-orpheus-llama-3.1-8b-mlx-4bit-dwq with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "darthcrawl/artifex-rp-orpheus-llama-3.1-8b-mlx-4bit-dwq"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "darthcrawl/artifex-rp-orpheus-llama-3.1-8b-mlx-4bit-dwq"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use darthcrawl/artifex-rp-orpheus-llama-3.1-8b-mlx-4bit-dwq with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "darthcrawl/artifex-rp-orpheus-llama-3.1-8b-mlx-4bit-dwq"

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 "darthcrawl/artifex-rp-orpheus-llama-3.1-8b-mlx-4bit-dwq" \
  --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"

MLX LM

How to use darthcrawl/artifex-rp-orpheus-llama-3.1-8b-mlx-4bit-dwq with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "darthcrawl/artifex-rp-orpheus-llama-3.1-8b-mlx-4bit-dwq"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "darthcrawl/artifex-rp-orpheus-llama-3.1-8b-mlx-4bit-dwq"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "darthcrawl/artifex-rp-orpheus-llama-3.1-8b-mlx-4bit-dwq",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use darthcrawl/artifex-rp-orpheus-llama-3.1-8b-mlx-4bit-dwq 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 "darthcrawl/artifex-rp-orpheus-llama-3.1-8b-mlx-4bit-dwq"

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 darthcrawl/artifex-rp-orpheus-llama-3.1-8b-mlx-4bit-dwq

Run Hermes

hermes

Atomic Chat

artifex-rp-orpheus-llama-3.1-8b-mlx-4bit-dwq

Part of the Artifex RP series.

MLX 4-bit DWQ (group-size 32) quantization of artifex-rp-orpheus-llama-3.1-8b for Apple Silicon.

For full model details, sample outputs, and training notes → see the main model card.

Quickstart

pip install mlx-lm
mlx_lm.generate --model darthcrawl/artifex-rp-orpheus-llama-3.1-8b-mlx-4bit-dwq \
  --system-prompt "Write in vivid, restrained, intelligent prose. Concrete sensory detail. Never pretentious. Tension carries more weight than description. Match the user's energy and length." \
  --prompt "She hadn't seen him in three years." \
  --temp 0.85 --top-p 0.95 --max-tokens 400

Quantization

Parameter	Value
Bits	4
Group size	32
Effective bits/weight	5.0
Size	4.7GB

Available variants

Repo	Size	Fits on
artifex-rp-orpheus-llama-3.1-8b	~16GB	24GB+ Mac (BF16)
artifex-rp-orpheus-llama-3.1-8b-mlx-8bit	8.4GB	16GB Mac
artifex-rp-orpheus-llama-3.1-8b-mlx-6bit	6.6GB	16GB Mac
artifex-rp-orpheus-llama-3.1-8b-mlx-4bit-dwq	4.7GB	8GB Mac