MUSE-GLIMMER-30B-ABLITERATED-GGUF

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

Built by Blackfrost · Las Vegas, NV

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Refusal benchmark

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

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

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Quant ladder

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Vision & speculative-decode files

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

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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:

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

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Built by Blackfrost · Las Vegas, NV. Not affiliated with Meta.