MUSE-GLIMMER-30B-ABLITERATED-MLX-3bit

3-bit MLX for Apple Silicon — smallest footprint

Built by Blackfrost · Las Vegas, NV

Refusal benchmark — R1-HARMFUL-BENCH-450

Measured on the abliterated model — quantization holds it, no refusal snapback:

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

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Why this model exists

Muse Glimmer is Meta Superintelligence Labs' 30B agentic, on-device model. This is the abliterated build — refusal behavior removed via a Blackfrost weight-change process — packaged as MLX 3-bit — ~15 GB for Apple-silicon Macs. 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	Abliterated — refusal behavior removed via a Blackfrost weight-change process; multimodal capability intact
Format	MLX 3-bit — ~15 GB
Context	131,072

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Serving (Apple Silicon / MLX)

pip install mlx-lm
# one-off generate:
mlx_lm.generate --model Blackfrost-Research/Muse-Glimmer-30B-Abliterated-MLX-3bit --prompt "Write a binary search in Python." --max-tokens 1024
# OpenAI-compatible server:
mlx_lm.server --model Blackfrost-Research/Muse-Glimmer-30B-Abliterated-MLX-3bit --port 8080

Or open it directly in LM Studio (MLX runtime) on an Apple-silicon Mac.

Sampling (Meta): temperature 1.0, top_p 0.95, top_k 64. It's a heavy thinker — use a generous max_tokens (≥ 1024) and steer depth with a Reasoning strength: low/medium/high/xhigh system line. Reasoning is returned separately from the final answer.

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