Mirai-L 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/Qwen3.8-27B-L
Currently only Apple silicon inference is supported. If you want to build things from source, read this overview.
Method
Mirai Large uses 8-bit symmetric integer quantization with bfloat16 scales and group size 64. Block-diagonal Random Hadamard Transforms with block size 32 are used to reduce activation and weight outliers. The checkpoint was prepared with post-training quantization.
In quantization and model-size tier, this checkpoint is comparable to Unsloth's UD-Q8_K_XL GGUF.
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 Qwen/Qwen3.8-27B. For architecture details, intended use, evaluations, and limitations, see the original model card.