---

license: apache-2.0

base_model: meta-models/Muse-Glimmer-30B

library_name: llama.cpp

pipeline_tag: image-text-to-text

tags:

• gguf
• quantization
• llama.cpp
• multimodal
• vision
• agentic
• tool-use

---

Muse-Glimmer-30B - GGUF Quants

This repository contains GGUF quants for meta-models/Muse-Glimmer-30B, created using llama.cpp.

Muse Glimmer is a 30-billion-parameter multimodal model distilled from Muse Spark, purpose-built for autonomous agentic workflows, long-horizon multi-step reasoning, SWE-bench coding tasks, and reliable function calling on consumer hardware.

---

File Availability & Recommended Hardware

To run vision inputs, download one core model file (.gguf) along with one multimodal vision projector (mmproj-*.gguf).

Core Model Files

| File Name | Size | Quantization | Rec. Memory / VRAM | Description |

| :--- | :---: | :---: | :---: | :--- |

| Muse-Glimmer-30B-Q8_0.gguf | 29.6 GB | Q8_0 | 32 GB - 48 GB | Maximum precision. Virtually lossless retention compared to BF16. |

| Muse-Glimmer-30B-Q6_K.gguf | 22.9 GB | Q6_K | 28 GB - 32 GB | Near-lossless output precision. Great for 32GB system/VRAM setup. |

| Muse-Glimmer-30B-Q5_K_M.gguf | 19.8 GB | Q5_K_M | 24 GB | High Quality balance. Ideal fit for GPUs with 24GB VRAM (e.g., RTX 3090/4090/5090). |

| Muse-Glimmer-30B-Q4_K_M.gguf | 16.9 GB | Q4_K_M | 20 GB - 24 GB | Recommended Sweet Spot. Optimal trade-off between speed, memory, and reasoning capacity. |

| Muse-Glimmer-30B-IQ4_NL.gguf | 16.1 GB | IQ4_NL | 20 GB | Non-Linear 4-bit importance matrix quantization. Strong performance under 17GB. |

| Muse-Glimmer-30B-IQ4_XS.gguf | 15.3 GB | IQ4_XS | 18 GB - 20 GB | Extra-small 4-bit iQuant for constrained VRAM environments. |

| Muse-Glimmer-30B-Q3_K_M.gguf | 14.0 GB | Q3_K_M | 16 GB - 18 GB | Standard 3-bit K-quant. Good option for 16GB VRAM cards. |

| Muse-Glimmer-30B-IQ3_M.gguf | 13.1 GB | IQ3_M | 16 GB | 3-bit medium importance quant with better reasoning recovery than baseline Q3. |

| Muse-Glimmer-30B-IQ3_XS.gguf | 12.3 GB | IQ3_XS | 14 GB - 16 GB | 3-bit extra-small iQuant for lower memory targets. |

| Muse-Glimmer-30B-IQ3_XXS.gguf | 11.5 GB | IQ3_XXS | 12 GB - 16 GB | Highly compressed 3-bit iQuant. Fits tight memory budgets. |

Vision Projector Files (mmproj)

| File Name | Size | Precision | Usage |

| :--- | :---: | :---: | :--- |

| mmproj-Muse-Glimmer-30B-BF16.gguf | 3.85 GB | BF16 | Full-precision ~1.8B ViT perception projector for maximum image fidelity. |

| mmproj-Muse-Glimmer-30B-Q8_0.gguf | 2.05 GB | Q8_0 | Recommended. 8-bit quantized vision projector preserving high image understanding at nearly half the RAM. |

---

Quickstart & Usage

1. llama.cpp CLI (With Vision Support)

To run the model with multimodal vision capability:

# Start server with vision support
llama-server \
  -m Muse-Glimmer-30B-Q4_K_M.gguf \
  --mmproj mmproj-Muse-Glimmer-30B-Q8_0.gguf \
  -c 131072 \
  --port 8080