---
title: Muse-Spark-Open-816B-A42B-oQ4e
canonical_url: "https://www.modelscope.cn/models/mlx-community/Muse-Spark-Open-816B-A42B-oQ4e"
md_url: "https://www.modelscope.cn/models/mlx-community/Muse-Spark-Open-816B-A42B-oQ4e.md"
repository: mlx-community/Muse-Spark-Open-816B-A42B-oQ4e
last_updated: 2026-10-16
license: other
pipeline_tag: image-text-to-text
tasks:
  - image-text-to-text
model_type:
  - muse_spark
architectures:
  - MuseSparkForConditionalGeneration
base_model:
  - meta-models/Muse-Spark-Open-816B-A42B
base_model_relation: quantized
parameters: 105.9B
tensor_type:
  - BF16
  - F32
  - U8
  - U32
library_name:
  - mlx
  - safetensors
  - pytorch
frameworks:
  - pytorch
downloads: 132
stars: 0
tags:
  - mlx
  - oq
  - quantized
  - muse
  - muse-spark
---

# Muse-Spark-Open-816B-A42B-oQ4e

> Muse-Spark-Open-816B-A42B-oQ4e - mlx-community 在 ModelScope 开源的模型。Muse-Spark-Open-816B-A42B-oQ4e

mlx-community/Muse-Spark-Open-816B-A42B-oQ4e 是 ModelScope 魔搭社区上的 105.9B 参数image-text-to-text模型，采用 other 许可，基于 meta-models/Muse-Spark-Open-816B-A42B 构建。

- **Repository**: mlx-community/Muse-Spark-Open-816B-A42B-oQ4e
- **License**: other
- **Tasks**: image-text-to-text
- **Parameters**: 105.9B
- **Base model**: meta-models/Muse-Spark-Open-816B-A42B
- **Tags**: mlx, oq, quantized, muse, muse-spark
- **Downloads**: 132
- **Stars**: 0
- **Last updated**: 2026-10-16

Source: https://www.modelscope.cn/models/mlx-community/Muse-Spark-Open-816B-A42B-oQ4e

---

# Muse-Spark-Open-816B-A42B-oQ4e

This model was quantized using [oQ](https://github.com/jundot/omlx) (oMLX v0.7.1) mixed-precision quantization with oQe importance-matrix quantization.

Re-uploaded on 2026-10-16 at 08:41 UTC in the layout of mlx-lm 0.32.2, so it loads in both mlx-lm 0.32.2 and oMLX. This version keeps the lm_head in BF16, which mlx-lm 0.32.2 needs for this model, and gives q, k, v and the attention output gate of each layer one bit width, as mlx-lm stores them as one projection.

A mixed-precision MLX checkpoint of [meta-models/Muse-Spark-Open-816B-A42B](https://huggingface.co/meta-models/Muse-Spark-Open-816B-A42B), Meta's 816B total / 42B active Mixture-of-Experts model with a 1M-token context window, built for coding and agentic work. It includes the language model and the vision encoder. The audio encoder is not included.

The routed experts of layers 1-60 are passed through unchanged from the official NVFP4 release ([meta-models/Muse-Spark-Open-816B-A42B-NVFP4](https://huggingface.co/meta-models/Muse-Spark-Open-816B-A42B-NVFP4)), with their per-expert tensor scales.

The effective average is 4.649 bits/weight for the complete checkpoint.

## Quantization and Bit Distribution

oQ4e allocates bits by measured layer sensitivity, then rounds each affine group with an importance matrix (128 samples x 512 tokens) and a weighted least-squares refit of its scale and bias. The NVFP4 experts pass through unchanged.

| Storage format | Logical weights | Share of total | Tensor storage | Effective bits/weight |
|---|---|---|---|---|
| NVFP4, group 16 | 773.094B | 94.060% | 405.000 GiB | 4.50 |
| Affine 4-bit, group 64 | 25.770B | 3.135% | 13.500 GiB | 4.50 |
| Affine 8-bit, group 64 | 19.338B | 2.353% | 19.136 GiB | 8.50 |
| BF16 | 3.585B | 0.436% | 6.678 GiB | 16.00 |
| FP32 | 0.132B | 0.016% | 0.492 GiB | 32.00 |
| **Total** | **821.920B** | **100%** | **444.806 GiB** | **4.649** |

## Storage Breakdown

| Component | Logical weights | Storage |
|---|---|---|
| Language backbone | 819.992B | 473.751 GB / 441.215 GiB |
| Vision encoder | 1.928B | 3.856 GB / 3.591 GiB |
| **Total** | **821.920B** | **477.607 GB / 444.806 GiB** |

> [!WARNING]
> This checkpoint takes about 478 GB, which is more than a single 512 GB Mac can run reliably. For one 512 GB Mac, use the oQ3e quant instead.

## Use with mlx-lm

```bash
pip install -U "mlx-lm>=0.32.2"
mlx_lm.generate --model mlx-community/Muse-Spark-Open-816B-A42B-oQ4e \
  --prompt "Explain convolutional networks like I am 5" \
  --max-tokens 5000
```

## Use with oMLX

Download the model in [oMLX](https://github.com/jundot/omlx) and chat with it in the built-in chat UI. You can add images and set the reasoning effort from low to xhigh. Reasoning cannot be turned off, and the default (Auto) uses xhigh.

## You can also use

- [mlx-community/Muse-Spark-Open-816B-A42B-oQ3e](https://huggingface.co/mlx-community/Muse-Spark-Open-816B-A42B-oQ3e): the 3-bit oQ quant.
- [mlx-community/Muse-Spark-Open-816B-A42B-3bit](https://huggingface.co/mlx-community/Muse-Spark-Open-816B-A42B-3bit): the standard mlx-lm 3-bit quant.
- [mlx-community/Muse-Spark-Open-816B-A42B-4bit](https://huggingface.co/mlx-community/Muse-Spark-Open-816B-A42B-4bit): the standard mlx-lm 4-bit quant.

## License

Muse Model License, following the source model. See `LICENSE.md` and `USE_POLICY.md`.