---
title: MiniMax-M3.1-Flash-oQ4e-mtp
canonical_url: "https://www.modelscope.cn/models/mlx-community/MiniMax-M3.1-Flash-oQ4e-mtp"
md_url: "https://www.modelscope.cn/models/mlx-community/MiniMax-M3.1-Flash-oQ4e-mtp.md"
repository: mlx-community/MiniMax-M3.1-Flash-oQ4e-mtp
last_updated: 2026-10-23
license: other
pipeline_tag: image-text-to-text
tasks:
  - image-text-to-text
model_type:
  - minimax_m3_vl
architectures:
  - MiniMaxM3SparseForConditionalGeneration
base_model:
  - MiniMaxAI/MiniMax-M3.1-Flash
base_model_relation: quantized
parameters: 81.6B
tensor_type:
  - F32
  - U8
  - U32
  - BF16
library_name:
  - mlx
  - safetensors
  - pytorch
frameworks:
  - pytorch
downloads: 24
stars: 0
tags:
  - mlx
  - oq
  - quantized
  - minimax_m3_vl
  - mtp
---

# MiniMax-M3.1-Flash-oQ4e-mtp

> MiniMax-M3.1-Flash-oQ4e-mtp - mlx-community 在 ModelScope 开源的模型。MiniMax-M3.1-Flash-oQ4e-mtp

mlx-community/MiniMax-M3.1-Flash-oQ4e-mtp 是 ModelScope 魔搭社区上的 81.6B 参数image-text-to-text模型，采用 other 许可，基于 MiniMaxAI/MiniMax-M3.1-Flash 构建。

- **Repository**: mlx-community/MiniMax-M3.1-Flash-oQ4e-mtp
- **License**: other
- **Tasks**: image-text-to-text
- **Parameters**: 81.6B
- **Base model**: MiniMaxAI/MiniMax-M3.1-Flash
- **Tags**: mlx, oq, quantized, minimax_m3_vl, mtp
- **Downloads**: 24
- **Stars**: 0
- **Last updated**: 2026-10-23

Source: https://www.modelscope.cn/models/mlx-community/MiniMax-M3.1-Flash-oQ4e-mtp

---

# MiniMax-M3.1-Flash-oQ4e-mtp

This model was quantized using [oQ](https://github.com/jundot/omlx) (oMLX v0.7.3.dev1) mixed-precision quantization.

A mixed-precision MLX checkpoint of [MiniMaxAI/MiniMax-M3.1-Flash](https://huggingface.co/MiniMaxAI/MiniMax-M3.1-Flash), a native multimodal mixture-of-experts model for coding, agentic workflows, computer use and cybersecurity with about 428B total and 23B activated parameters. It takes text, image and video inputs with a context window of up to 1M tokens, and uses MiniMax Sparse Attention throughout the model. It includes the language model, the vision encoder and the bundled DSpark draft in `dspark/`, which oMLX Lightning MTP uses for speculative decoding.

The effective average is 4.617 bits/weight for the language model and vision encoder, or 4.638 bits/weight including the DSpark draft.

## Quantization and Bit Distribution

oQ4e keeps the routed experts in their source NVFP4 format. It keeps MXFP8 for the shared experts, the sparse attention indexer and more than half of the attention projections, and stores the remaining projections, the embeddings and the vocabulary head as affine 4- to 8-bit. The router and the DSpark draft keep their source precision.

| Storage format | Logical weights | Share of total | Tensor storage | Effective bits/weight |
|---|---|---|---|---|
| Affine 4-bit, group 64 | 0.189B | 0.044% | 0.099 GiB | 4.50 |
| NVFP4, group 16 | 413.122B | 96.738% | 216.422 GiB | 4.50 |
| Affine 5-bit, group 64 | 2.378B | 0.557% | 1.523 GiB | 5.50 |
| Affine 6-bit, group 64 | 1.085B | 0.254% | 0.821 GiB | 6.50 |
| Affine 8-bit, group 64 | 2.458B | 0.576% | 2.433 GiB | 8.50 |
| MXFP8, group 32 | 7.097B | 1.662% | 6.816 GiB | 8.25 |
| BF16 | 0.678B | 0.159% | 1.262 GiB | 16.00 |
| FP32 | 0.045B | 0.011% | 0.167 GiB | 32.00 |
| **Total** | **427.052B** | **100%** | **229.542 GiB** | **4.617** |

## Storage Breakdown

| Component | Logical weights | Storage |
|---|---|---|
| Language model | 426.375B | 245.116 GB / 228.282 GiB |
| Vision | 0.677B | 1.354 GB / 1.261 GiB |
| DSpark draft (MTP) | 2.011B | 2.302 GB / 2.144 GiB |
| **Total** | **429.063B** | **248.771 GB / 231.686 GiB** |

## License

MiniMax Community License, following the source model. See `LICENSE`. Commercial use has additional conditions in the license, including displaying "Built with MiniMax M3.1-Flash" and obtaining prior written authorization from MiniMax.