Instructions to use mlx-community/KAT-Coder-V2.5-Dev-OptiQ-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlx-community/KAT-Coder-V2.5-Dev-OptiQ-4bit 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/KAT-Coder-V2.5-Dev-OptiQ-4bit")

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/KAT-Coder-V2.5-Dev-OptiQ-4bit 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/KAT-Coder-V2.5-Dev-OptiQ-4bit"

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/KAT-Coder-V2.5-Dev-OptiQ-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use mlx-community/KAT-Coder-V2.5-Dev-OptiQ-4bit 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/KAT-Coder-V2.5-Dev-OptiQ-4bit"

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/KAT-Coder-V2.5-Dev-OptiQ-4bit

Run Hermes

hermes

OpenClaw new

How to use mlx-community/KAT-Coder-V2.5-Dev-OptiQ-4bit 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/KAT-Coder-V2.5-Dev-OptiQ-4bit"

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/KAT-Coder-V2.5-Dev-OptiQ-4bit" \
  --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/KAT-Coder-V2.5-Dev-OptiQ-4bit 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/KAT-Coder-V2.5-Dev-OptiQ-4bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "mlx-community/KAT-Coder-V2.5-Dev-OptiQ-4bit"
# 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/KAT-Coder-V2.5-Dev-OptiQ-4bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

KAT-Coder-V2.5-Dev-OptiQ-4bit

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 KAT-Coder-V2.5-Dev, a qwen3_5_moe coding model (256-routed-expert sparse MoE with hybrid linear + full attention).

Mixed 4/8-bit static build — per-layer bit-widths assigned to a 4.5 target bits-per-weight: 400 projections at 4-bit, 111 at 8-bit.
~4.51 bits per weight, 20 GB on disk.

Requirements

pip install -U optiq

qwen3_5_moe loads under stock mlx-lm too, but optiq serve adds mixed- precision loading, KV-cache quantization, and the OptiQ Lab.

Running it

optiq serve --model mlx-community/KAT-Coder-V2.5-Dev-OptiQ-4bit

Then use the OpenAI-compatible endpoint at http://localhost:8000/v1, the OptiQ Lab, or point optiq code at it. This is a reasoning coder — it thinks before it answers.

Downloads last month: 1,535

Safetensors

Model size

35B params

Tensor type

BF16

U32

MLX

Hardware compatibility

4-bit

Model tree for mlx-community/KAT-Coder-V2.5-Dev-OptiQ-4bit

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Kwaipilot/KAT-Coder-V2.5-Dev

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