Duplicate from dealignai/Muse-Glimmer-30B-CRACK-GGUF

Co-authored-by: dealign.ai <dealignai@users.noreply.huggingface.co>

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README.md

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+---
+license: apache-2.0
+language:
+- en
+- zh
+library_name: gguf
+pipeline_tag: image-text-to-text
+base_model: meta-models/Muse-Glimmer-30B
+base_model_relation: quantized
+tags:
+- gguf
+- llama.cpp
+- muse-glimmer
+- abliterated
+- uncensored
+- crack
+- reasoning
+- tool-use
+- atem
+---
+
+<p align="center">
+  <img src="dealign_logo.png" alt="Dealign.ai" width="180"/>
+  <br/><strong><a href="https://dealign.ai">Dealign.ai</a></strong>
+</p>
+
+# 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
+
+| 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:
+```json
+{"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

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