Model overview
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. - **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 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.
Key features
特点摘要待依据来源补齐，不凭模型名称推断能力。
Limitations & considerations
Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.
Source prices & cost analysis
Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.
| Source / channel | Input | Output | Billing unit | Collected at |
|---|---|---|---|---|
| No verified prices yet. Check the source channel. | ||||
Estimate monthly base token costs
Estimates exclude cache discounts, tiered pricing, multimodal surcharges, web search, taxes and additional reasoning output.
Public benchmarks & run configuration
暂无已采集的可核验公开评分。模型未上榜不代表能力较低。
View full indexed rankings →Frequently asked questions
Do open weights permit free commercial use?
Not necessarily. Check the version license, commercial conditions and usage policies. Self-hosting incurs compute and maintenance costs.
Are these real-time billing prices?
These are timestamped source snapshots, not live billing prices. Check the channel for current versions, regions, discounts and billing methods.
Sources & traceability
许可证：来源未披露，须进一步核验。模型介绍与第三方评测均以原来源的具体版本和方法为准。