cvgro/Muse-Glimmer-30B-Abliterated-GGUF overview

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license: apache-2.0

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

tags:

- muse-glimmer

- gguf

- abliterated

- quantized

- agentic

- multimodal

- llama.cpp

- dflash

- experimental

pipeline_tag: image-text-to-text

library_name: gguf

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!UoOQg

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<h1>MUSE-GLIMMER-30B-ABLITERATED-GGUF</h1>

<h3>GGUF quant ladder of the abliterated Muse Glimmer 30B · runs local on one GPU or CPU</h3>

<p><strong>Built by <a href="https://x.com/Blackfrost_AI">Blackfrost</a> · Las Vegas, NV</strong></p>

<p>

<img src="https://img.shields.io/badge/GGUF_full_ladder-047857?style=for-the-badge" />

<img src="https://img.shields.io/badge/0%2F450_refusals-047857?style=for-the-badge" />

<img src="https://img.shields.io/badge/Abliterated-1f2937?style=for-the-badge" />

<img src="https://img.shields.io/badge/EXPERIMENTAL-b45309?style=for-the-badge" />

<img src="https://img.shields.io/badge/llama.cpp_+_DFlash-1f2937?style=for-the-badge" />

</p>

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Refusal benchmark

Measured on the abliterated parent (GGUF quants inherit this behavior):

| Metric | Result |

|---|--:|

| True refusal (harmful, n=300) | 0 / 300 = 0.0% |

| True refusal (full 450) | 0 / 450 = 0.0% |

| Substring-harmful | 0 / 300 |

| Substring-all | 2 / 450 (XSTest false positives) |

| Errors | 0 |

The in-place weight change removes the refusal direction cleanly with no measured true refusals across the full 450-prompt suite.

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Why this model exists

Muse Glimmer is Meta Superintelligence Labs' 30B agentic, on-device model. This is the abliterated build — the refusal direction removed via an in-place residual-write weight change — packaged as GGUF for llama.cpp, so it runs on a single consumer GPU or CPU, fully offline. The local footprint is the product.

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Specifications

| | |

|---|---|

| Architecture | muse_glimmer — dense, 52 layers, hidden 6656, GQA (32 q / 2 kv), sliding-window attention, + vision tower |

| Base | meta-models/Muse-Glimmer-30B — Meta, Apache-2.0 |

| Transform | Abliteration only — in-place residual-write weight change (attn o_proj + mlp.down_proj), α=1.5 × 3 iterative passes. Vision / gates / norms untouched. |

| Formats | GGUF — Q2_K, Q3_K_S, Q3_K_M, Q4_K_S, Q4_K_M, Q5_K_S, Q5_K_M, Q6_K, Q8_0 |

| Context | 131,072 |

| Spec-decode | DFlash drafter — --spec-type draft-dflash --spec-draft-n-max 15 |

| Default persona | Ships with the "AI assistant" system template baked in |

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Quant ladder

| quant | size | recommended for |

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

| Q2_K | 10.0 GB | smallest, quality trade-off |

| Q3_K_S | 11.7 GB | very tight VRAM |

| Q3_K_M | 12.7 GB | tight VRAM |

| Q4_K_S | 15.0 GB | 16 GB cards |

| Q4_K_M | 15.8 GB | default — balanced, fits 24 GB |

| Q5_K_S | 18.0 GB | higher quality |

| Q5_K_M | 18.5 GB | strong quality/size balance |

| Q6_K | 21.3 GB | near-lossless |

| Q8_0 | 27.6 GB | max fidelity |

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Vision & speculative-decode files

Load a text quant plus an mmproj projector for image input:

| file | size | purpose |

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

| mmproj-Muse-Glimmer-30B-Abliterated-F16.gguf | 3.6 GB | vision projector — full precision |

| mmproj-Muse-Glimmer-30B-Abliterated-Q8_0.gguf | 1.9 GB | vision projector — compact |

| dflash-Muse-Glimmer-30B-Abliterated-F16.gguf | 4.8 GB | DFlash drafter — speculative decoding |

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Serving (llama.cpp) — confirmed settings

Requires a recent llama.cpp (master) with llama-server. DFlash runs under llama-server only — it shares the target model's context, so it does not work in llama-cli.

Recommended — with DFlash speculative decoding (~1.6× faster, identical output):

llama-server \
  -m  Muse-Glimmer-30B-Abliterated-Q8_0.gguf \
  -md dflash-Muse-Glimmer-30B-Abliterated-F16.gguf \
  --spec-type draft-dflash --spec-draft-n-max 15 \
  -ngl 999 -ngld 999 -fa on --jinja \
  --host 0.0.0.0 --port 8080 -c 16384 \
  --temp 1.0 --top-p 0.95 --top-k 64

• Plain (no drafter): drop -md, --spec-type, --spec-draft-n-max, and -ngld.
• Multimodal (image input): add --mmproj mmproj-Muse-Glimmer-30B-Abliterated-F16.gguf.
• One-command kit: deploy/serve.sh auto-downloads + serves; full guide in deploy/DEPLOYMENT.md.

Confirmed settings

• Sampling: temperature 1.0, top_p 0.95, top_k 64 (Meta). Steer depth with a Reasoning strength: low/medium/high/xhigh system line.
• max_tokens ≥ 1024 — heavy thinker; small budgets return empty content because the reasoning channel consumes them. Reasoning arrives in reasoning_content, the answer in content.
• --spec-draft-n-max 15 — DFlash block size (trained 16, clamped).
• Flash attention: -fa on for peak speed; switch to -fa off if the load hangs on a brand-new GPU paired with an older CUDA toolkit.

Measured performance

1× NVIDIA RTX PRO 6000 (Blackwell), Q8_0, -fa off:

| config | decode tok/s | speedup |

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

| baseline | ~46 | 1.0× |

| + DFlash | ~73 | 1.6× |

Speedup rises with -fa on and structured/code output (Meta reports up to 3.1× on an RTX 5090).

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<p>Built by <a href="https://x.com/Blackfrost_AI">Blackfrost</a> · Las Vegas, NV. Not affiliated with Meta.</p>

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