base_model: Blackfrost-AI/Muse-Glimmer-30B-Abliterated-BF16 base_model_relation: quantized license: apache-2.0 pipeline_tag: image-text-to-text tags:

• gguf
• quantized
• llama.cpp
• muse-glimmer
• muse_glimmer
• abliterated
• multimodal
• conversational

Muse Glimmer 30B Abliterated — Custom GGUF Quant

Custom GGUF quantization of Blackfrost-AI/Muse-Glimmer-30B-Abliterated-BF16.

This quant was produced with an experimental quantization workflow intended to improve the quality/size tradeoff relative to a conventional quant of the same source model.

Model

• Base model: Blackfrost-AI/Muse-Glimmer-30B-Abliterated-BF16
• Format: GGUF
• Approximate file size: 11.9 GB
• Quantization processing time: ~2.3 hours
• Target runtimes: llama.cpp and compatible GGUF frontends
• License: Apache-2.0, following the upstream model
• Recommended vram A 16 Gigabyte card should run this comfortably.

Local benchmark results

The following results are from local factual-summary testing and are not a standardized leaderboard evaluation.

In this local test, the custom quant matched the approximate Q8 factual score while being about 2.3 GB smaller than the tested standard IQ4_XS representation.

Results may vary with prompt, sampling settings, runtime, hardware, and benchmark methodology.

Usage

With a recent llama.cpp build:

llama-cli -m ./YOUR_MODEL_FILE.gguf --jinja -ngl 999

Or with the llama.cpp server:

llama-server -m ./YOUR_MODEL_FILE.gguf --jinja -ngl 999

Replace YOUR_MODEL_FILE.gguf with the actual filename in this repository.

Multimodal / vision support

Muse Glimmer is an image-text-to-text model.

If your llama.cpp build requires a separate multimodal projector, use a compatible mmproj*.gguf for this model lineage:

llama-server \
  -m ./YOUR_MODEL_FILE.gguf \
  --mmproj ./YOUR_MMPROJ_FILE.gguf \
  --jinja \
  -ngl 999

If this repository does not include an mmproj file, users will need to obtain or generate a compatible projector separately for image input.

Reproducibility

When comparing this quant against other variants, please report:

• exact GGUF filename/revision
• llama.cpp version or commit
• inference backend
• GPU/CPU and available memory
• context size
• temperature, top-p, top-k, and seed
• benchmark prompts
• number of generations/runs
• scoring methodology

Notes

• This repository contains a quantized derivative, not a retrained model.
• The source is the abliterated BF16 derivative linked above.
• Quantization can affect factual recall, reasoning, formatting, and multimodal performance differently.
• Local benchmark results should be independently reproduced before drawing broad conclusions.

Attribution

Source model:

Blackfrost-AI/Muse-Glimmer-30B-Abliterated-BF16

Please also review the upstream model card for its original Muse Glimmer lineage, intended use, limitations, and safety information.

License

Apache-2.0, following the source repository.