---
license: apache-2.0
base_model:
- Blackfrost-Research/Muse-Glimmer-30B-Abliterated-BF16
tags:
- muse-glimmer
- abliterated
- mlx
- awq
- quantized
- apple-silicon
- 3-bit
pipeline_tag: image-text-to-text
library_name: mlx
---

<div align="center">

<h1>MUSE-GLIMMER-30B-ABLITERATED-MLX-3bit-AWQ</h1>

<h3>3-bit AWQ MLX for Apple Silicon — activation-aware, quality-recovered 3-bit</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/MLX_3bit_AWQ-1f2937?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/Apple_MLX-047857?style=for-the-badge" />
</p>

</div>

## 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 |

---

## 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 AWQ — ~15 GB** for Apple-silicon Macs. The local footprint is the product.

---

## 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`](https://huggingface.co/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 AWQ — ~15 GB |
| **Context** | 131,072 |

---

## Serving (Apple Silicon / MLX)

```bash
pip install mlx-lm
# one-off generate:
mlx_lm.generate --model Blackfrost-Research/Muse-Glimmer-30B-Abliterated-MLX-3bit-AWQ --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-AWQ --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.

---

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