What are the hardware requirements for Muse Glimmer 30B Abliterated MLX 4bit AWQ?

Muse Glimmer 30B Abliterated MLX 4bit AWQ requires approximately 19.4 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Muse Glimmer 30B Abliterated MLX 4bit AWQ and how large is it?

Muse Glimmer 30B Abliterated MLX 4bit AWQ is developed by Blackfrost-AI, a model with 30b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate Muse Glimmer 30B Abliterated MLX 4bit AWQ?

Muse Glimmer 30B Abliterated MLX 4bit AWQ is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Muse Glimmer 30B Abliterated MLX 4bit AWQ by Blackfrost-AI — VRAM 19.4GB

Name: Muse Glimmer 30B Abliterated MLX 4bit AWQ
Author: Blackfrost-AI

Muse Glimmer 30B Abliterated MLX 4bit AWQ is an open-source language model by Blackfrost-AI. Features: 30b LLM, VRAM: 19.4GB, License: apache-2.0, Quantized, LLM Explorer Score: 0.27.

4-bit 4bit Abliterated Apple-silicon Awq Base model:blackfrost-ai/muse-... Base model:quantized:blackfros... Conversational Image-text-to-text Mlx Muse-glimmer Muse glimmer Quantized Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/Blackfrost-AI/Muse-Glimmer-30B-Abliterated-MLX-4bit-AWQ

Muse Glimmer 30B Abliterated MLX 4bit AWQ Parameters and Internals

LLM Name	Muse Glimmer 30B Abliterated MLX 4bit AWQ
Repository 🤗	https://huggingface.co/Blackfrost-AI/Muse-Glimmer-30B-Abliterated-MLX-4bit-AWQ
Base Model(s)	Blackfrost-Research/Muse-Glimmer-30B-Abliterated-BF16 Blackfrost-Research/Muse-Glimmer-30B-Abliterated-BF16
Model Size	30b
Required VRAM	19.4 GB
Updated	2026-08-27
Maintainer	Blackfrost-AI
Model Type	muse_glimmer
Model Files	5.3 GB: 1-of-4 5.4 GB: 2-of-4 5.3 GB: 3-of-4 3.4 GB: 4-of-4
AWQ Quantization	Yes
Quantization Type	awq\|4bit
Model Architecture	MuseGlimmerForConditionalGeneration
License	apache-2.0
Model Max Length	131072
Transformers Version	5.15.0.dev0
Tokenizer Class	TokenizersBackend
Padding Token	<\|finetune_right_pad\|>

Rank the Muse Glimmer 30B Abliterated MLX 4bit AWQ capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

Best Alternatives to Muse Glimmer 30B Abliterated MLX 4bit AWQ

Best Alternatives	Context / RAM	Downloads	Likes
Muse Glimmer 30B AWQ INT4	0K / 24.1 GB	985	8
Muse Glimmer 30B AWQ FP8	0K / 34.5 GB	45	1
Muse Glimmer Q4 MLX	128K / 15.7 GB	5949	6
Muse Glimmer 30B 8bit	0K / 11.6 GB	2935	2
Muse Glimmer 30B OptiQ 4bit	0K / 19.9 GB	360	1
Muse Glimmer 30B MLX 8bit	0K / 33.4 GB	107	1
...use Glimmer 30B Hermes Agentic	0K / 59.6 GB	37	2
Muse Glimmer 30B 4bit	0K / 19.4 GB	0	11
...e Glimmer 30B Unsloth Bnb 4bit	0K / 22.2 GB	0	7
...immer 30B Abliterated MLX 6bit	0K / 26.4 GB	1010	1

Note: green Score (e.g. "73.2") means that the model is better than Blackfrost-AI/Muse-Glimmer-30B-Abliterated-MLX-4bit-AWQ.