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

This model was quantized using oQ (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, 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), 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

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

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 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: the 3-bit oQ quant.
• mlx-community/Muse-Spark-Open-816B-A42B-3bit: the standard mlx-lm 3-bit quant.
• 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.