Instructions to use mlx-community/LLaDA2.2-flash-OptiQ-2bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlx-community/LLaDA2.2-flash-OptiQ-2bit 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("mlx-community/LLaDA2.2-flash-OptiQ-2bit")

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 mlx-community/LLaDA2.2-flash-OptiQ-2bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "mlx-community/LLaDA2.2-flash-OptiQ-2bit"

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": "mlx-community/LLaDA2.2-flash-OptiQ-2bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use mlx-community/LLaDA2.2-flash-OptiQ-2bit 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 "mlx-community/LLaDA2.2-flash-OptiQ-2bit"

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 mlx-community/LLaDA2.2-flash-OptiQ-2bit

Run Hermes

hermes

OpenClaw new

How to use mlx-community/LLaDA2.2-flash-OptiQ-2bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "mlx-community/LLaDA2.2-flash-OptiQ-2bit"

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 "mlx-community/LLaDA2.2-flash-OptiQ-2bit" \
  --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 mlx-community/LLaDA2.2-flash-OptiQ-2bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "mlx-community/LLaDA2.2-flash-OptiQ-2bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "mlx-community/LLaDA2.2-flash-OptiQ-2bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "mlx-community/LLaDA2.2-flash-OptiQ-2bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

LLaDA2.2-flash-OptiQ-2bit / README.md

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metadata

license: apache-2.0
language:
  - en
tags:
  - mlx
  - optiq
  - diffusion
library_name: mlx
pipeline_tag: text-generation
base_model: inclusionAI/LLaDA2.2-flash

LLaDA2.2-flash-OptiQ-2bit

Built with mlx-optiq, the MLX-native toolkit to quantize, fine-tune, and serve LLMs locally on Apple Silicon (no PyTorch, no cloud). Try the Lab · All OptiQ quants · Docs

An OptiQ mixed-precision MLX quant of LLaDA2.2-flash, a ~100B diffusion language model with a 256-routed-expert sparse MoE. This is an extreme 2-bit build.

Mixed 2/4-bit static build — per-layer bit-widths assigned to a 2.5 target bits-per-weight.
192 GB bf16 to 36 GB on disk (5.3x).

Requirements

Needs optiq >= 0.4.4, which ships the vendored llada2_moe decoder (the 256-expert diffusion MoE) and the block-diffusion decode loop. Stock mlx-lm has no llada2_moe arch and cannot load or generate from this repo.

pip install -U optiq

Running it

LLaDA2 is a masked-diffusion model, not autoregressive — it denoises a canvas block by block. optiq serve detects the arch and routes it through OptiQ's vendored decoder and the block-diffusion decode loop automatically:

optiq serve --model mlx-community/LLaDA2.2-flash-OptiQ-2bit

Then call the OpenAI-compatible endpoint at http://localhost:8000/v1, or use it from the OptiQ Lab and optiq code.