Mirai's LFM2.5-1.2B-Thinking Medium Quantization

A medium-sized, high-quality quantization of LFM2.5-1.2B-Thinking prepared for efficient local inference on Apple silicon.

Mirai-M is on the size-KL Pareto frontier: we found no checkpoint that is smaller while also having lower KL divergence.

Evaluation data mixture: 45% public agentic, 30% public SFT/long-context, 25% private chat data.

Quickstart

If you are on macOS, the easiest way is to install the mirai Homebrew package and then run the CLI:

brew install mirai
mirai --model trymirai/LFM2.5-1.2B-Thinking-M

Currently only Apple silicon inference is supported. If you want to build things from source, read this overview.

API Docs

Method

Mirai Medium uses 4-bit asymmetric integer quantization with 4-bit zero points, bfloat16 scales, and group size 64. Block-diagonal Random Hadamard Transforms are used to reduce activation and weight outliers. The checkpoint was prepared with post-training quantization followed by quantization-aware distillation.

Citation

If you find our work helpful, feel free to give us a cite.

@misc{mirai-quant,
    title  = {{Mirai Quantization}: Redefining the speed-quality frontier for local LLMs on Apple silicon},
    author = {Artur Chakhvadze and Ryan Mathieu and Roman Knyazhitskiy and Nikolai Voinilenko and Chen-Chen Yeh and Artur Mullakhmetov and Eugene Bokhan and others},
    note   = {In collaboration with others at Mirai Labs},
    month  = {June},
    year   = {2026},
    url    = {https://trymirai.com/blog/quantization}
}

Original model

This is a quantized version of LiquidAI/LFM2.5-1.2B-Thinking. For architecture details, intended use, evaluations, and limitations, see the original model card.