An Apple Silicon MLX-VLM MXFP4 release of the original
meta-models/Muse-Glimmer-30B model, developed by Meta
Superintelligence Lab. This conversion is pinned to source revision
97c77dff50b2797bcc558fa2d909761dbc575c59 and is intended for local text-and-image inference with MLX.
The original model, this conversion, and the DFlash assistant are Apache-2.0.
Read the upstream model card and USAGE_POLICY.md before
deployment.
Model details
Property
Value
Architecture
Dense causal transformer with a dedicated perception encoder
Parameters
Approximately 29.8B total, including an approximately 1.8B vision encoder
Modalities
Text and image input; text output
Context length
131,072+ architecture limit documented by the original model; not validated end-to-end here
Quantization
RTN mxfp4, 4-bit, group size 32
Quantization scope
Eligible language and bridge linear layers; vision_tower remains unquantized
Release size
Approximately 18.57 GB
Validated host
Apple M2 Max with 32 GB unified memory
Requirements
macOS on Apple Silicon.
Validated environment: macOS 26.5.1, Python 3.13.6, MLX 0.32.0; see
reports/runtime.json for the complete package set.
Approximately 19 GB for model files, plus memory for MLX, the KV cache,
prompt processing, and the operating system.
32 GB unified memory was validated for autoregressive text and vision use.
DFlash was not usable on the tested 32 GB host; see the experimental section
below.
The Hugging Face hf CLI and network access are needed only if downloading
the separate DFlash assistant with the command below.
This exact MLX-VLM revision is required because Muse Glimmer support is not yet
part of a stable release.
Usage
Text generation
bash
1mlx_vlm.generate \2 --model bsisduck/Muse-Glimmer-30B-MLX-MXFP4 \3 --prompt "Write a quicksort in Python."\4 --max-tokens 512 --temperature 1.0 --top-p 0.95 --top-k 64
Muse Glimmer's published recommended sampling is temperature 1.0, top-p 0.95,
and top-k 64. The benchmark below uses greedy decoding because that is the
published DFlash speed protocol and permits output equality checks.
Image understanding
bash
1mlx_vlm.generate \2 --model bsisduck/Muse-Glimmer-30B-MLX-MXFP4 \3 --image ./image.jpg \4 --prompt "Describe this image and identify any important details."\5 --max-tokens 512 --temperature 1.0 --top-p 0.95 --top-k 64
DFlash (experimental)
On the tested 32 GB M2 Max, the target and pinned
assistant loaded together, but the 8-token DFlash warmup did not complete after
43.6 minutes. The process footprint reached approximately
22.5 GB while free system
memory fell to 4%, with
heavy compression and swap. No local DFlash throughput or output-equality result
is claimed. DFlash likely requires more available memory, but no minimum-memory
requirement or higher-memory MLX configuration was validated here. The 32 GB
validation covers autoregressive generation only.
The upstream model card
reports 1.5x on M4 Max (23.7 to 37.8 tok/s) and 1.8x on M5 Max (26.6 to
50.2 tok/s). Those measurements used the upstream 17 GB K-Quant target with
ExecuTorch, batch size 1, and greedy decoding. They are not MLX measurements
and are not directly comparable to the local result below.
Passed; 64x64 red-square fixture, 79 prompt tokens, 32 generated tokens, identified as red
Quantization metadata
Passed; MXFP4, 4-bit, group size 32
Vision tower policy
Passed; the vision tower is unquantized
These checks establish artifact integrity, reproducibility, loadability, and
basic text/vision operation. They are not a task-quality comparison against
the BF16 source model. Exact agreement with another serialized MXFP4 release
does not prove quality equivalence to BF16.
The documented 131,072+ context limit was not exercised on the 32 GB validation
host. Long prompts and larger or more complex images may require more memory.
Local throughput
Measured on the host described in
reports/benchmark.json, batch size 1, four prompt categories, greedy decoding,
and an 8-token warmup:
Mode
Mean tok/s
Median tok/s
Peak MLX memory
Autoregressive
2.73
2.37
18.86 GB
DFlash
Not completed on 32 GB
Not completed on 32 GB
~22.5 GB process footprint
The baseline completed all four prompts. DFlash loaded but did not finish its
8-token warmup under severe unified-memory pressure, so speedup and greedy-output
equality were not measured locally.
The complete protocol, per-prompt results, environment, and package versions are
available in reports/benchmark.json.
DFlash numerical caveat
The MLX-VLM PR author reports that a real 4-bit Muse target can choose different
greedy tokens between token-at-a-time and block verification because quantized
matrix multiplication is shape-dependent. Target verification still occurs,
but do not assume bit-identical output on every prompt.
No local equality result is claimed because DFlash did not complete warmup on this 32 GB host.
Intended use
This release is intended for local research and application development on
Apple Silicon, including:
text generation and reasoning;
coding and tool-using agents with application-level safeguards;
image and document understanding;
evaluation of MLX-VLM integration and MXFP4 deployment.
Audio input/output is not supported. Uses that violate applicable law, the
Apache-2.0 license, or the included usage policy are out of scope.
Limitations and safety
The model may produce inaccurate, biased, unsafe, or objectionable output.
Quantization can change outputs and may reduce quality in edge cases. No full
BF16-versus-MXFP4 quality benchmark was run for this release.
Performance can vary substantially with prompt length, memory pressure, OS,
and MLX/runtime versions.
DFlash depends on an experimental runtime revision and was not validated as
usable on the tested 32 GB host.
Agentic deployments should validate tool calls, require confirmation for
irreversible actions, and apply safeguards appropriate to the use case.
The original model was trained on more than 100 languages, but performance
was not validated here across that language set.
The original model's safety evaluations and broader limitations are documented
in its model card
and methodology report.
Repository scope
This repository publishes one model variant: the MXFP4 target described above.
It is not a multi-variant repository and intentionally does not duplicate
Meta's 5.1 GB BF16 assistant. Download the pinned official assistant separately
with the hf download command shown above before using DFlash.
