How to use from
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"}
     ]
   }'
Quick Links

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.

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