| 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. | |