| --- |
| license: other |
| base_model: MiniMaxAI/MiniMax-M2.7 |
| tags: |
| - gguf |
| - quantized |
| - apex |
| - moe |
| - mixture-of-experts |
| - minimax |
| - minimax-m2 |
| --- |
| |
|
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|
|
| <!-- apex-banner-v2 --> |
| <div style="background-color: #f59e0b; color: white; padding: 20px; border-radius: 10px; text-align: center; margin: 20px 0;"> |
| <h2 style="color: white; margin: 0 0 10px 0;">⚡ Each donation = another big MoE quantized</h2> |
| <p style="font-size: 18px; margin: 0 0 15px 0;">I host <b>25+ free APEX MoE quantizations</b> as independent research. My only local hardware is an <b>NVIDIA DGX Spark</b> (122 GB unified memory) — enough for ~30-50B-class MoEs, but <b>bigger ones (200B+) require rented compute</b> on H100/H200/Blackwell, typically $20-100 per quant.<br>If APEX quants are useful to you, your support directly funds those bigger runs.</p> |
| <p style="font-size: 20px; margin: 0;"> |
| <a href="https://www.patreon.com/cw/mudler" style="color: white; text-decoration: underline;">🎉 Patreon (Monthly)</a> | |
| <a href="https://www.buymeacoffee.com/mudler" style="color: white; text-decoration: underline;">☕ Buy Me a Coffee</a> | |
| <a href="https://github.com/sponsors/mudler" style="color: white; text-decoration: underline;">⭐ GitHub Sponsors</a> |
| </p> |
| <p style="font-size: 14px; margin: 10px 0 0 0; opacity: 0.9;">💚 Big thanks to Hugging Face for generously donating additional storage — much appreciated.</p> |
| </div> |
|
|
| # 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) |
|
|
| > **Note**: MiniMax M2 architecture support in llama.cpp is still maturing. If you encounter inference issues, ensure you're using a recent llama.cpp build and report issues upstream. |
|
|
| ## Available Files |
|
|
| | File | Profile | Size | Best For | |
| |------|---------|------|----------| |
| | MiniMax-M2.7-APEX-I-Balanced.gguf | I-Balanced | 155 GB | Best overall quality/size ratio | |
| | MiniMax-M2.7-APEX-Balanced.gguf | Balanced | 155 GB | General purpose | |
| | MiniMax-M2.7-APEX-I-Quality.gguf | I-Quality | 129 GB | Highest quality with imatrix | |
| | MiniMax-M2.7-APEX-Quality.gguf | Quality | 129 GB | Highest quality standard | |
| | MiniMax-M2.7-APEX-I-Compact.gguf | I-Compact | 100 GB | Multi-GPU setups, best quality/size | |
| | MiniMax-M2.7-APEX-Compact.gguf | Compact | 100 GB | Multi-GPU setups | |
| | MiniMax-M2.7-APEX-I-Mini.gguf | I-Mini | 80 GB | Smallest "safe" tier | |
| | MiniMax-M2.7-APEX-I-Nano.gguf | **I-Nano** (new) | 64 GB | Experimental — IQ2_XXS mid-layer experts | |
| | MiniMax-M2.7-APEX-F16-*.gguf | F16 reference | 426 GB (10 shards) | Full-precision BF16 for imatrix/further research | |
| |
| ## 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). |
| |
| The key insight: in MoE models, expert FFN tensors make up the bulk of model weight but only ~8/256 experts activate per token. APEX compresses middle-layer experts more aggressively while preserving edge layers (first/last 5) and keeping attention and shared-expert tensors at higher precision. |
| |
| See the [APEX project](https://github.com/mudler/apex-quant) for full details, technical report, and scripts. |
| |
| ### Nano (new experimental tier) |
| |
| APEX M2.7 debuts the **Nano** tier, which pushes mid-layer routed experts to **IQ2_XXS (2.06 bpw)**, near-edge to IQ2_S, edges to Q3_K, and keeps shared experts at Q5_K. About 20% smaller than Mini with modest quality cost, viable only on MoE thanks to sparse per-token activation. Requires imatrix. |
|
|
| Benchmarks for Nano are pending. Feedback welcome. |
|
|
| ## Architecture |
|
|
| - **Model**: MiniMax-M2.7 (MiniMaxM2) |
| - **Layers**: 62 |
| - **Experts**: 256 routed (8 active per token) |
| - **Total Parameters**: ~228 B |
| - **Active Parameters**: ~10 B per token |
| - **Source Format**: FP8 (float8_e4m3fn, block-quantized 128×128) |
| - **Intermediate Format**: BF16 (via unsloth's pre-converted BF16 GGUF) |
| - **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-M2.7-APEX-GGUF@MiniMax-M2.7-APEX-I-Balanced.gguf |
| ``` |
| |
| ## Credits |
| |
| - **Base model**: [MiniMaxAI](https://huggingface.co/MiniMaxAI) |
| - **BF16 GGUF source**: [unsloth/MiniMax-M2.7-GGUF](https://huggingface.co/unsloth/MiniMax-M2.7-GGUF) |
| - **APEX quantization**: [LocalAI](https://github.com/mudler/LocalAI) team |
| - Built on [llama.cpp](https://github.com/ggerganov/llama.cpp) |
| |
