MiMo-V2.5-coder-Q2 Supercharges Flawless Coding and Tool Calls on Macs

MiMo-V2.5-coder-Q2 is a text-only GGUF build of the MiMo-V2.5 model, specifically quantized and tested for English-language coding and tool-calling tasks. This Q2_K_S quant was iteratively calibrated to preserve syntax precision, exact exported names, and structured tool-call formatting that lower-bit models often lose. The target system is a 128 GB Apple Silicon machine, where the model can run with a 100,000-token context while fitting as much as possible onto Metal acceleration.

Developer Jedisct1 created this build after discovering that standard low-bit quants of very large MoE models frequently produce malformed tool calls, broken API details, and repetitive reasoning loops. The project focuses the limited Q2-class quality budget directly on developer workflows rather than generic chat breadth. The GGUF does not include vision or audio encoders, and the multi-token prediction blocks were omitted because current llama.cpp inference does not execute them.

Built for real-world coding and agent workflows

This quantized model suits developers and power users with high-memory Apple Silicon workstations who need reliable local coding assistance and agent tool calling. The build was validated across Swift, TypeScript, Rust, C, C++, Zig, Python, Perl, Go, JavaScript, and static HTML/CSS tasks using real compilers and test runners. Frontend testing also covered React, Vue, and Solid components with props, filtering behavior, and accessible form markup.

How the calibration process improved reliability

The v2 build emerged from an iterative process that identified and fixed specific real-world failures. An initial Q2 candidate missed conditional tools, malformed some tool-call arguments, and produced brittle JavaScript parsing. Those failures were fed back into an expanded coding and tool-use prompt mix, which rebuilt the importance matrix to tell the quantizer which activations mattered most. Key tensors received higher precision protection: embeddings and output tensors kept higher precision for token identity, attention tensors were protected for structure-heavy tool-calling, and MoE down-expert tensors used Q3_K instead of pushing them lower. The current v2 scores improved from 18/22 to a perfect 22/22 on tool selection and achieved 11/11 on the coding harness.

"If you have 128 Gb, this is an excellent alternative to Qwen3.6 and DS4, especially for coding. Fast, and with reliable tool calling." — jedisct1 on Reddit