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mudler/MiniMax-M2.5-APEX-GGUF

sourceHugging Faceotherupdated 1mo agoView on Hugging Face
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MiniMax-M2.5 APEX GGUF

APEX (Adaptive Precision for EXpert Models) quantizations of MiniMax-M2.5.

Brought to you by the [LocalAI](https://github.com/mudler/LocalAI) team | APEX Project | Technical Report

Benchmark Results

Benchmarks coming soon. For reference APEX benchmarks on the Qwen3.5-35B-A3B architecture, see mudler/Qwen3.5-35B-A3B-APEX-GGUF.

Available Files

FileProfileSizeBest For
MiniMax-M2.5-APEX-I-Balanced.ggufI-Balanced155 GBBest overall quality/size ratio
MiniMax-M2.5-APEX-I-Quality.ggufI-Quality130 GBHighest quality with imatrix
MiniMax-M2.5-APEX-Quality.ggufQuality130 GBHighest quality standard
MiniMax-M2.5-APEX-Balanced.ggufBalanced155 GBGeneral purpose
MiniMax-M2.5-APEX-I-Compact.ggufI-Compact100 GBMulti-GPU setups, best quality/size
MiniMax-M2.5-APEX-Compact.ggufCompact100 GBMulti-GPU setups
MiniMax-M2.5-APEX-I-Mini.ggufI-Mini81 GBSmallest viable

What is APEX?

APEX is a quantization strategy for Mixture-of-Experts (MoE) models. It classifies tensors by role (routed expert, shared expert, attention) and applies a layer-wise precision gradient -- edge layers get higher precision, middle layers get more aggressive compression. I-variants use diverse imatrix calibration (chat, code, reasoning, tool-calling, agentic traces, Wikipedia).

See the APEX project for full details, technical report, and scripts.

Architecture

  • —Model: MiniMax-M2.5 (MiniMaxM2)
  • —Layers: 62
  • —Experts: 256 routed + 1 shared (8 active per token)
  • —Total Parameters: 228.7B
  • —Active Parameters: ~45B per token
  • —APEX Config: 5+5 symmetric edge gradient across 62 layers
  • —Calibration: v1.3 diverse dataset (chat, code, reasoning, multilingual, tool-calling, Wikipedia)

Run with LocalAI

bash
local-ai run mudler/MiniMax-M2.5-APEX-GGUF@MiniMax-M2.5-APEX-I-Balanced.gguf

Credits

APEX is brought to you by the LocalAI team. Developed through human-driven, AI-assisted research. Built on llama.cpp.