Instructions to use shoemoney/Muse-Glimmer-30B-Abliterated-MLX-q6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use shoemoney/Muse-Glimmer-30B-Abliterated-MLX-q6 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Muse-Glimmer-30B-Abliterated-MLX-q6 shoemoney/Muse-Glimmer-30B-Abliterated-MLX-q6
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Muse-Glimmer-30B-Abliterated — MLX 6-bit
MLX 6-bit quantisation of Blackfrost-AI/Muse-Glimmer-30B-Abliterated-BF16.
Changes: weights quantised to 6-bit from the BF16 source with
mlx_vlm.convert. No fine-tuning, no merging, no re-alignment.
Measured
Converted and measured on one machine — Apple M3 Ultra, 96 GB unified memory, macOS 27 — as part of a full ladder. Every rung in this family came from the same BF16 source with the same group size, so bit width is the only variable between them.
| Size on disk | 26.42 GB |
| Perplexity | 7.284 |
| Relative to best rung in family | 1.01× |
| Throughput (1 req / 8 concurrent) | 25.3 / 78.4 tok/s |
Perplexity measured on allenai/tulu-3-sft-mixture, 192 samples of 512 tokens,
seed 123 — identical for every rung.
Perplexity is only comparable within this family. Tokenizers differ between model families, so a number here should never be compared against a different base model's. The
×column above is the meaningful one.
Usage
pip install mlx-vlm
mlx_vlm.generate --model shoemoney/Muse-Glimmer-30B-Abliterated-MLX-q6 --prompt "Hello" --max-tokens 256
Load with mlx-vlm, not mlx-lm — this architecture is registered in mlx-vlm.
Provenance
mlx_vlm.convert --hf-path Blackfrost-AI/Muse-Glimmer-30B-Abliterated-BF16 \
--mlx-path Muse-Glimmer-30B-Abliterated-q6 -q --q-bits 6 --q-group-size 64
License
apache-2.0, inherited from the base model. Attribution above.
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Model tree for shoemoney/Muse-Glimmer-30B-Abliterated-MLX-q6
Base model
meta-models/Muse-Glimmer-30B