license: apache-2.0
base_model: meta-models/Muse-Glimmer-30B
pipeline_tag: image-text-to-text
tags:
- gguf
- abliterated
- vision
- muse-glimmer
amoral-muse-glimmer-30b-abliterated — GGUF
Quantized GGUFs for the abliterated Muse Glimmer 30B,
a multimodal (text + image) agentic VLM. Abliteration removes the refusal direction from the
model's activation space using Heretic; it edits the language
model's residual stream only, so the vision tower is untouched.
| quant | file | size |
|---|---|---|
| Q4_K_M | amoral-muse-glimmer-30b-abliterated-Q4_K_M.gguf |
16.9 GB |
| Q3_K_M | amoral-muse-glimmer-30b-abliterated-Q3_K_M.gguf |
13.7 GB |
| Q8_0 | amoral-muse-glimmer-30b-abliterated-Q8_0.gguf |
29.6 GB |
Easiest path: pull the published tag
These quants are already published and ready to run:
ollama pull saracen9/amoral-muse-glimmer-30b-abliterated # :q4_k_m, also :latest
ollama pull saracen9/amoral-muse-glimmer-30b-abliterated:q3_k_m
ollama pull saracen9/amoral-muse-glimmer-30b-abliterated:q8_0
Building it yourself from these files
The included Modelfile.* reproduce the published tags exactly — same renderer, parser, system
prompt and parameters. Two FROM lines are required: this is a multimodal model, and a
single-FROM build silently loses vision.
The vision projector ships here too, so no second source is needed. Abliteration edits only the
language model's residual stream, so the stock projector is this model's projector — but note
it is the BF16 one (mmproj-Muse-Glimmer-30B-bf16.gguf, 3.85 GB), byte-identical to the
projector inside the published Ollama tags. The Q4_K_M projector published upstream is a
different, quantized vision tower; building with it gives you a model that is not the one
these tags serve.
hf download saracen9/muse-glimmer-30b-abliterated-GGUF \
amoral-muse-glimmer-30b-abliterated-Q4_K_M.gguf \
mmproj-Muse-Glimmer-30B-bf16.gguf Modelfile.q4_k_m --local-dir .
ollama create amoral-muse -f Modelfile.q4_k_m
ollama show amoral-muse should report vision among its capabilities and a clip projector.
If it does not, the second FROM line did not resolve.
Serving notes
RENDERER glimmer / PARSER glimmer are Ollama's built-in Muse handlers — they are what make
tool-calling and reasoning work, so do not replace them with a hand-written TEMPLATE.
This is a reasoning model: it spends roughly a thousand tokens on its internal to=self channel
before answering, which the parser separates into the response's thinking field. A small
context truncates the answer to empty — which reads like a refusal and is not one. num_ctx is
baked at 32768 for that reason, and because a baked value cannot be raised per-request on an
OpenAI-compatible endpoint.