Large agent models usually need cloud GPUs and complex infra; this GGUF release compresses Muse Glimmer-30B into quantized artifacts so you can run an agent-focused multimodal model locally on 24–32 GB class machines with only minor quality loss. It bundles text-only quantized builds plus a required perception encoder for image understanding and an optional small drafter for much faster speculative decoding.
Key Capabilities
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End-to-end agentic workflows: supports multi-step planning, reliable schema-based tool calls, failure recovery and long-horizon task execution — so what: you can run agent orchestration and tool-heavy pipelines locally without frequent cloud round trips.
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Multimodal input via a perception encoder: a separate ~1.8B ViT-G/14 encoder handles images and interleaved text — so what: interprets screenshots, charts and documents together with chat context, enabling visual grounding for agent decisions.
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Local-first optimizations and compact quantization: two 4-bit k-quant variants (≈17 GB and ≈19.7 GB text builds) plus quantized drafter reduce memory footprint with ~0.2–1.0% measured degradation — so what: fits typical 24–32 GB GPUs and leaves headroom for KV cache and companions for real-time interaction.
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Speculative decoding via a DFlash drafter: block-diffusion drafter proposes blocks of tokens (block size 16) that the main model verifies — so what: throughput improves multiple-fold on supported hardware while preserving output quality.
Who it fits & tradeoffs
Great fit if you need a local, multimodal agent-capable model that: runs on a single workstation or small server (24–32 GB VRAM), handles long contexts (131k+), and prioritizes reliable tool use and multi-step reasoning. It’s useful for local AI agents, coding assistants, document/screenshot understanding, and on-device evaluation workloads.
Look elsewhere if you need the absolute top-tier accuracy or research-grade full-precision weights for fine-tuning (full BF16 weights are available in the base repo), if you require audio/video inputs (audio not supported), or if you cannot upgrade to llama.cpp build b10353+ (older builds will not recognize the architecture).
Practical notes
- Image input requires the mmproj perception encoder; text-only GGUFs will not understand images by themselves. The DFlash drafter is optional but recommended for higher token throughput.
- The GGUFs embed a chat template and a permanent reasoning channel; clients must pass the
--jinjaflag and can control thinking viareasoning_strength/reasoning_budgetrather than disabling it entirely. - Target users should perform task-specific safety testing and deploy additional guardrails for agentic integrations.
