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README.md

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+---
+license: other
+base_model: MiniMaxAI/MiniMax-M2.5
+tags:
+  - gguf
+  - quantized
+  - apex
+  - moe
+  - mixture-of-experts
+  - minimax
+---
+
+# MiniMax-M2.5 APEX GGUF
+
+**APEX (Adaptive Precision for EXpert Models)** quantizations of [MiniMax-M2.5](https://huggingface.co/MiniMaxAI/MiniMax-M2.5).
+
+**Brought to you by the [LocalAI](https://github.com/mudler/LocalAI) team** | [APEX Project](https://github.com/mudler/apex-quant) | [Technical Report](https://github.com/mudler/apex-quant/blob/main/paper/APEX_Technical_Report.pdf)
+
+## Benchmark Results
+
+Benchmarks coming soon. For reference APEX benchmarks on the Qwen3.5-35B-A3B architecture, see [mudler/Qwen3.5-35B-A3B-APEX-GGUF](https://huggingface.co/mudler/Qwen3.5-35B-A3B-APEX-GGUF).
+
+## Available Files
+
+| File | Profile | Size | Best For |
+|------|---------|------|----------|
+| MiniMax-M2.5-APEX-I-Balanced.gguf | I-Balanced | 155 GB | Best overall quality/size ratio |
+| MiniMax-M2.5-APEX-I-Quality.gguf | I-Quality | 130 GB | Highest quality with imatrix |
+| MiniMax-M2.5-APEX-Quality.gguf | Quality | 130 GB | Highest quality standard |
+| MiniMax-M2.5-APEX-Balanced.gguf | Balanced | 155 GB | General purpose |
+| MiniMax-M2.5-APEX-I-Compact.gguf | I-Compact | 100 GB | Multi-GPU setups, best quality/size |
+| MiniMax-M2.5-APEX-Compact.gguf | Compact | 100 GB | Multi-GPU setups |
+| MiniMax-M2.5-APEX-I-Mini.gguf | I-Mini | 81 GB | Smallest 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](https://github.com/mudler/apex-quant) 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](https://github.com/mudler/LocalAI) team. Developed through human-driven, AI-assisted research. Built on [llama.cpp](https://github.com/ggerganov/llama.cpp).