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
- MLX
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
- Pi
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
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
staticbuild — 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.
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Model tree for mlx-community/LLaDA2.2-flash-OptiQ-2bit
Base model
inclusionAI/LLaDA2.2-flash