A native Apple-silicon conversion of meta-models/Muse-Glimmer-30B, quantized with oMLX's optimized mixed-precision oQ6 recipe for text-and-image inference with MLX-VLM.
This repository contains an oMLX-optimized mixed-precision oQ6 conversion of Muse Glimmer 30B, a dense agentic language model with a dedicated perception encoder. It preserves the upstream tokenizer, ATEM chat template, image processor configuration, generation configuration, licence, and usage policy.
The oQ recipe keeps the embedding, language-model head, and selected attention/MLP projections at 8-bit above the 6-bit base. Both quantization and quantization_config record every per-module override.
[!IMPORTANT]
Muse Glimmer support was validated against the official MLX-VLM source at commit 5262cb6, reporting package version 0.6.12. An older MLX-VLM or oMLX bundle may report Model type muse_glimmer not supported; update to a build containing the upstream Muse implementation before loading this checkpoint.
Apple-silicon performance
This checkpoint was load-tested, text-generation tested, and benchmarked on:
Hardware
Configuration
Host
Mac Studio
Chip
Apple M3 Ultra
CPU
32 cores (24 performance + 8 efficiency)
Unified memory
256 GB
Runtime
MLX-VLM 0.6.12 source revision 5262cb6 with the oMLX MLX runtime
A warmed local text-only test produced:
Measurement
Result
Decode (median)
26.29 tokens/s
Individual decode runs
26.32 / 26.28 / 26.29 tokens/s
Reported peak memory
29.13 GB
Timed runs
3 × 256 generated tokens
Warm-up
256 generated tokens
Prompt
83 tokens after chat templating
The decode figure is the median of three greedy 256-token runs after a full 256-token Metal-kernel warm-up. It is a practical local reference, not a controlled cross-platform benchmark. Prompt length, images, context growth, sampler settings, memory pressure, thermal state, and runtime versions can materially change performance. Image prefill is not included in this decode benchmark.
Runtime setup
Until Muse Glimmer support reaches the MLX-VLM build supplied by your application, install the exact official source revision used for validation:
Muse Glimmer supports low, medium, high, and xhigh reasoning strengths. Higher settings can spend more tokens reasoning before returning the final answer.
Image and text generation
The multimodal path was smoke-tested locally with a real image. Include an image content item so the ATEM template emits the required patch token:
Muse Glimmer combines a dense causal transformer with a dedicated perception encoder for interleaved text and image input.
Architecture detail
Upstream value
Total parameters
~29.6B
Transformer layers
52
Hidden size
6,656
Attention heads / KV heads
32 / 2
Head dimension
128
FFN intermediate size
19,968
Attention pattern
local / local / local / global
Sliding window
2,048 tokens
Vocabulary size
202,048
Perception encoder
~1.8B-parameter ViT-G/14, 50 layers
Context length
131,072 tokens
The model supports agentic task completion, tool use through the upstream ATEM protocol, controllable reasoning effort, multilingual input, failure recovery, and multimodal understanding. See the original model card for upstream benchmarks, training details, intended uses, limitations, and safety guidance.
Conversion and validation notes
Source weights: upstream BF16 checkpoint at revision a4e59da52a7bc87ae7251dd5545c0dd437c44b68.
Quantization mode: oQ6 affine mixed precision, group size 64, with 11 protected-module overrides at 8-bit.
The upstream tokenizer, ATEM chat template, processor configuration, generation configuration, licence, and usage policy are included.
All 2,272 converted tensors and all 6 indexed shards were checked locally.
The checkpoint was exercised through full text generation; the Muse architecture was also smoke-tested with image input.
Quantization can reduce output quality relative to BF16. Use a higher-precision variant when quality matters more than memory use.
This release does not include or claim support for the upstream speculative drafter.
This is a community conversion, not an official Meta release. Validate quality, safety, and numerical behaviour on representative workloads before production use.
Licence, usage policy, and attribution
The upstream model is released under the Apache License 2.0. The upstream LICENSE and USAGE_POLICY.md files are included in this repository; use is subject to both the licence and the upstream usage policy.
All model design, training, benchmark, and upstream documentation credit belongs to Meta and the original contributors. The MLX conversion, Apple-silicon validation, compatibility work, and model card are provided by Vontra.
