---
license: other
base_model: MiniMaxAI/MiniMax-M2.7
tags:
  - gguf
  - quantized
  - apex
  - moe
  - mixture-of-experts
  - minimax
---

# MiniMax-M2.7 APEX GGUF

**APEX (Adaptive Precision for EXpert Models)** quantizations of [MiniMax-M2.7](https://huggingface.co/MiniMaxAI/MiniMax-M2.7).

**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)

> **Status: Re-quantization in progress.** The previous quants had a conversion bug (our direct FP8→BF16 path produced broken logits). We've identified the issue — using unsloth's pre-converted BF16 GGUF as the source instead — and are re-quantizing. Working quants will be back shortly.

## About 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.

See the [APEX project](https://github.com/mudler/apex-quant) for full details, technical report, and scripts.

## Architecture

- **Model**: MiniMax-M2.7 (MiniMaxM2)
- **Layers**: 62
- **Experts**: 256 routed (8 active per token)
- **Total Parameters**: ~228B
- **Active Parameters**: ~10B per token

## Credits

APEX is brought to you by the [LocalAI](https://github.com/mudler/LocalAI) team.