CRACK-abliterated Muse Glimmer 30B — GGUF quants for llama.cpp. Three quantizations (Q8_0 / Q4_K_M / Q2_K) in one repository. Refusal behavior removed while preserving the model's knowledge, reasoning, multi-strength thinking, and ATEM tool-calling.

Research artifact with reduced safety guardrails. Use responsibly and lawfully.

Quantizations

File	Size	Notes
`Q8_0`	29.6 GB	near-lossless reference
`Q4_K_M`	16.9 GB	balanced (recommended)
`Q2_K`	10.7 GB	smallest

Pick one text file plus the vision projector mmproj-Muse-Glimmer-30B-f16.gguf (3.8 GB) for image input. Q4_K_M is the recommended balance; Q8_0 is near-lossless; Q2_K is smallest.

Benchmarks

Evaluated through llama.cpp at greedy decoding. MMLU is logit-mode accuracy (base vs. CRACK at the same quant — measures knowledge retention). HarmBench is answer-channel compliance on harm behaviors, counting only coherent responses.

Quant	MMLU (base)	MMLU (CRACK)	ΔMMLU	HarmBench compliance
`Q8_0`	80.0%	79.0%	-1.05 pp	99.6%
`Q4_K_M`	80.0%	78.6%	-1.40 pp	100.0%
`Q2_K`	77.5%	77.9%	+0.35 pp	99.6%

MMLU is retained within noise of the base model at every quant. HarmBench compliance is reported for the CRACK model.

HarmBench compliance by topic (CRACK)

Topic	Compliance
chemical biological	100.0%
cybercrime intrusion	100.0%
harassment bullying	100.0%
harmful	100.0%
illegal	100.0%
misinformation disinformation	100.0%

Usage (llama.cpp)

bash

llama-cli -m Muse-Glimmer-30B-CRACK-Q4_K_M.gguf -cnv \
  --temp 1.0 --top-p 0.95 --top-k 64
# or serve:
llama-server -m Muse-Glimmer-30B-CRACK-Q4_K_M.gguf --jinja \
  --temp 1.0 --top-p 0.95 --top-k 64 -c 8192

Recommended sampling (baked into the GGUF): temperature=1.0, top_p=0.95, top_k=64. Token IDs: BOS 200000, EOS 200001/<|eot|>, pad 200018.

Reasoning strength

Muse Glimmer supports controllable reasoning. Set it via the chat template:

{"chat_template_kwargs": {"reasoning_strength": "low"}} // low | medium | high | xhigh

The reasoning trace is emitted on a separate channel (reasoning_content); the final answer is the assistant content.

Tool calling (ATEM)

The model emits ATEM-format tool calls, parsed natively by llama.cpp's --jinja server into standard tool_calls. Pass OpenAI-style tools to the chat endpoint.

Vision (image + text)

This is a multimodal model. Download a text quant and mmproj-Muse-Glimmer-30B-f16.gguf:

bash

llama-mtmd-cli -m Muse-Glimmer-30B-CRACK-Q4_K_M.gguf \
  --mmproj mmproj-Muse-Glimmer-30B-f16.gguf --jinja \
  --image photo.jpg -p "Describe this image."
# or serve with vision:
llama-server -m Muse-Glimmer-30B-CRACK-Q4_K_M.gguf \
  --mmproj mmproj-Muse-Glimmer-30B-f16.gguf --jinja -c 8192

The same mmproj works with all three text quants.

License

Apache 2.0. The upstream Muse Glimmer Usage Policy applies.

Contact

eric@dealign.ai

Justbackup/Muse-Glimmer-30B-CRACK-GGUF

Muse-Glimmer-30B-CRACK-GGUF