LFM2.5-230M-OptiQ-4bit

mlx-community/LFM2.5-230M-OptiQ-4bit

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

An OptiQ mixed-precision quant of LiquidAI/LFM2.5-230M, the smallest model in the LFM2.5 line. 180 MB on disk, down from 459 MB at bf16, small enough to sit in memory on any Apple Silicon Mac alongside everything else you are running.

LFM2.5 is a hybrid architecture: convolutional blocks interleaved with full attention, so only a few blocks carry a KV cache. OptiQ measures each layer's sensitivity and assigns per-layer bit-widths.

What it is

Capability Score

Six-metric mean, the standard OptiQ eval.

At 230M parameters the strengths are instruction-following and maths for the size. Long-context multi-hop retrieval sits at the floor for a model this small, and that score is genuine rather than a harness artifact.

Run it

pip install mlx-optiq

from mlx_lm import load, generate

model, tok = load("mlx-community/LFM2.5-230M-OptiQ-4bit")
prompt = tok.apply_chat_template(
    [{"role": "user", "content": "Name three colours."}],
    tokenize=False, add_generation_prompt=True,
)
print(generate(model, tok, prompt=prompt, max_tokens=200))

Serve it with the bundled mixed-precision KV cache:

optiq serve --model mlx-community/LFM2.5-230M-OptiQ-4bit --kv-config kv_config.json

Tool calling

LFM2.5 writes Pythonic calls between special tokens:

<|tool_call_start|>[get_weather(city="Paris")]<|tool_call_end|>

Pass tools= to apply_chat_template and they are rendered into the system prompt.

Links

• Project website: mlx-optiq.com
• All OptiQ quants: mlx-optiq.com/models
• Base model: LiquidAI/LFM2.5-230M