The vendor-recipe quantized checkpoint of Muse Glimmer, packed for serving with SGLang. This is the model vendor's own fp_awq mixed-precision recipe (MIXED_PRECISION): NVFP4 (E2M1, group 16) on most projections, MXFP8 (E4M3, group 32) on the higher-sensitivity set (v_proj, down_proj, lm_head), norms in bf16 — 18.3 GiB versus 52 GiB for the bf16 export. Grab it and serve it directly by repo id; no conversion step needed.

Model Specifications

Base model: meta-models/Muse-Glimmer-30B (Muse Glimmer bf16 export)
Format: Safetensors, 5 shards, 18.34 GiB
Recipe: vendor fp_awq hand-off packed with the SGLang fork's convert_fp_awq_to_hf.py (default flags — the recipe as shipped); quantization map in hf_quant_config.json (quant_algo: MIXED_PRECISION)
Language model: 52 layers, hidden 6656, 32 Q / 2 KV heads, vocab 202,048
Text-only serving is recommended (--language-model-only); the vision tower ships in the base export, not in this quantized checkpoint

Evaluation Results

Measured on 1× DGX Spark (GB10), SGLang, 1024 in / 1024 out, greedy:

BS	output tok/s	vs bf16 target	+ DFlash draft (sim acc=5)
1	12.1	2.7×	36.4
4	47.7	2.7×	156.2
8	92.2	2.6×	300.7

Notes: decode is memory-bound, so the speedup tracks the 52→18 GiB weight reduction. On GB10, quantized prefill is slower than bf16 at batch ≥ 4 (~650 vs ~1500 input tok/s) — decode-heavy workloads win, prefill-heavy workloads should measure. Correctness smoke-verified (greedy arithmetic and generation with correct stop tokens); full accuracy suite pending.

Serving with SGLang

Requires the SGLang fork with Muse Glimmer support (sgl-project/sglang#34262, model support PR #3) until merged upstream.

bash

sglang serve \
  --model-path RadixArk/Muse-Glimmer-NVFP4 \
  --reasoning-parser muse \
  --tool-call-parser muse \
  --language-model-only \
  --tp-size 1 \
  --mem-fraction-static 0.85 \
  --host 0.0.0.0 --port 30000

With speculative decoding (pairs with meta-models/Muse-Glimmer-30B-assistant):

bash

sglang serve \
  --model-path RadixArk/Muse-Glimmer-NVFP4 \
  --reasoning-parser muse \
  --tool-call-parser muse \
  --language-model-only \
  --speculative-algorithm DFLASH \
  --speculative-draft-model-path meta-models/Muse-Glimmer-30B-assistant \
  --speculative-dflash-block-size 5 \
  --tp-size 1 \
  --mem-fraction-static 0.85 \
  --host 0.0.0.0 --port 30000

Hardware notes:

Fits a 32 GB RTX 5090 (the bf16 export does not).
DGX Spark / GB10 (unified memory): use --mem-fraction-static 0.40 (0.38 with DFlash). Higher fractions let the KV pool consume the shared CPU/GPU pool and can OOM the machine during load.
Sampling: temperature 0.95, top_p 1.0 (greedy only for determinism checks). Do not add <|eom|> (200007) as an EOS token — it breaks parallel tool calling; generation_config.json already carries the correct stops.