Model overview
GGUF builds of meta-models/Muse-Glimmer-30B — Meta Superintelligence Lab's ~30B multimodal agent model, distilled from Muse Spark and purpose-built for autonomous agentic work on consumer hardware. Every file here was cut from the same BF16 source weights; the repo holds full-precision copies, a vision projector, and 29 quants from Q8_0 down to Q1_0. Quantized 2026-09-07. **These are llama.cpp GGUF files.** They are not loaded by vLLM, SGLang or `transformers` the way the source repo is. Muse Glimmer is a 30B-class dense causal transformer with a dedicated perception encoder (image + text input, text output), trained for the capabilities an autonomous agent needs working together:
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：apache-2.0。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。