Muse-Glimmer-30B-CRACK-GGUF

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Muse-Glimmer-30B-CRACK-GGUF

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

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.

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)

Usage (llama.cpp)

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:

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

[email protected]