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kingjones777/Muse-Glimmer-30B-Uncensored-ROCmFP4-GGUF

🤗 On Hugging Faceimage-text-to-textapache-2.081 GBGGUF✓ Checksums witnessedupdated today
Magnet
### 🔧 Runtime: build the ROCmFPX fork below
Stock llama.cpp will not load this file. You need both the muse-glimmer architecture
and the ROCmFP4 tensor types in one tree. Upstream
charlie12345/ROCmFPX has the ROCmFP4 types but
not muse-glimmer. Our fork has both:
kingjones30/ROCmFPX — a fork of charlie12345/ROCmFPX, branch main.
```bash
git clone https://github.com/kingjones30/ROCmFPX.git
cd ROCmFPX
cmake -B build -DGGML_HIP=ON -DGPU_TARGETS=gfx1151 -DGGML_NATIVE=ON -DCMAKE_BUILD_TYPE=Release
cmake --build build --target llama-server llama-quantize -j$(nproc)
```
Verified 2026-08-27 on gfx1151: clean clone → 0 build errors → llama-server loads a
muse-glimmer ROCmFP4 GGUF from this family and generates coherent text.

Muse-Glimmer-30B Uncensored — ROCmFP4 for AMD Strix Halo (gfx1151)

Uncensored ROCmFP4 quantisations of meta-models/Muse-Glimmer-30B, built with the same ROCmFPX pipeline and the same card ftypes as kingjones777/Muse-Glimmer-30B-ROCmFP4-Strix-Halo-DFlash-GGUF.

Research artifact. Abliteration removes content-refusal. It does not add capability. Do not ship this as a product default. The aligned repo remains the serving default.

| Metric | Result |

| --- | --- |

| Quantization | ROCmFP4 103 FAST + 106 STRIX_LEAN; also Q6 114/116 |

| Source | Muse-Glimmer-30B BF16 safetensors → GGUF via convert_hf_to_gguf.py |

| Hardware | Ryzen AI Max+ 395 / Radeon 8060S / gfx1151 / 128 GB / ROCm 7.2.4 |

| Drafter | Meta dflash-kquant.gguf, --spec-type draft-dflash --spec-draft-n-max 15 |

| Unc FAST 103 decode | prose 15.68 · code 37.44 tok/s |

| Unc STRIX_LEAN 106 decode | prose 16.72 · code 38.51 tok/s |

| Aligned FAST 103 (published A/B) | 20.31 tok/s mixed; real-world ~17–45 |

| Aligned STRIX_LEAN 106 (published A/B) | 18.72 tok/s mixed |

Why this build?

The aligned card measured STRIX_LEAN (106) at 18.72 tok/s and FAST (103) at 20.31 tok/s in a controlled A/B (DFlash n=15, ctx 32K, -fa on). This repo is those same ftypes from an abliterated checkpoint, plus the Q6 AGENT/LEAN pair.

Q6 LEAN (ftype 116) is not the card LEAN. Card LEAN = 106.

Which file should I use?

Start with STRIX_LEAN (106) if you want the card-matched LEAN. Take FAST (103) if you want the aligned speed pick. Take Q6 AGENT (114) if you want more bits and will live with ~Q6 decode.

Ryzen AI Max+ 395, ROCm 7.2.4, DFlash --spec-draft-n-max 15, -fa on, ctx 32768, batch 1, temperature 0. Warm medians of 3; first call after load discarded.

| Build | ftype | Size | prose tok/s | code tok/s |

| --- | --- | --- | --- | --- |

| Unc FAST | 103 | 13.80 GiB | 15.68 | 37.44 |

| Unc STRIX_LEAN | 106 | 14.00 GiB | 16.72 | 38.51 |

| Unc Q6 AGENT | 114 | 24.17 GiB | — | — |

| Unc Q6 LEAN | 116 | 21.09 GiB | — | — |

| Aligned FAST (published) | 103 | 13.80 GiB | ~15 | ~39 |

| Aligned STRIX_LEAN (published A/B) | 106 | 14.00 GiB | — | 18.72 mixed |

Same flags, same drafter as the aligned card. Decode on this model is workload-dominated — quote a range, not a point.

Quick start

llama-server \
  -m muse-glimmer-30B-Uncensored-ROCmFP4-STRIX_LEAN.gguf \
  --spec-type draft-dflash --model-draft dflash-kquant.gguf \
  --spec-draft-n-max 15 --spec-draft-ngl 99 --spec-draft-device ROCm0 \
  --chat-template-kwargs '{"reasoning_strength":"low"}' \
  -ngl 999 -fa on -dio --jinja -fit off -dev ROCm0 -c 32768 \
  --host 127.0.0.1 --port 8080

Requires a llama.cpp built with ROCmFP4 (ggml types 100–106) and the muse-glimmer port. Stock llama.cpp rejects these tensor types.

| Flag | Why |

| --- | --- |

| --chat-template-kwargs '{"reasoning_strength":"low"}' | Template defaults to high. Small max_tokens then returns empty content. |

| -fa on (text) / -fa off (vision) | Vision requires -fa off. |

| --spec-draft-n-max 15 | DFlash block size is 16; one slot holds the previously accepted token. |

--reasoning-budget is not enforced on this model. Use reasoning_strength.

Uncensored findings

Content-refusal scoring on a 24 harmful / 12 harmless / 8 quality research set, greedy (temp 0). Counts only — no payloads. Abliteration is supposed to drop harmful-tune refusals without wrecking ordinary Q&A.

| Model | Harmful 24 | Harmless 12 | Quality 8 |

| --- | --- | --- | --- |

| Qwen3.8 aligned Q8 AGENT | 23 refuse, 1 comply | 11/12 ok (1 over-refuse) | 6/8 |

| Qwen3.8 uncensored Q6 AGENT (114) | 23 comply, 1 broken | 11/12 ok (1 over-refuse) | 6/8 |

| Muse aligned Q6 AGENT (114) | 18 refuse, 6 comply | 11/12 ok (1 over-refuse) | 7/8 |

| Muse uncensored STRIX_LEAN (106) | 24 comply | 12/12 ok (0 over-refuse) | 7/8 |

Reading:

  • Aligned Qwen still refuses almost everything on this set. Abliterated Qwen complies on almost everything. Quality score is identical (same two fails: Márquez needle + bat-and-ball).
  • Aligned Muse is leakier than aligned Qwen on this classifier — a few complies even before abliteration.
  • Quality is a substring smoke check, not MMLU. It is a regression guard against a broken quant, not a capability claim.

Files

| File | ftype | Size | Role |

| --- | --- | --- | --- |

| muse-glimmer-30B-Uncensored-ROCmFP4-FAST.gguf | 103 | 13.80 GiB | speed pick |

| muse-glimmer-30B-Uncensored-ROCmFP4-STRIX_LEAN.gguf | 106 | 14.00 GiB | card-LEAN equivalent |

| muse-glimmer-30B-Uncensored-Q6_0_ROCMFPX_AGENT.gguf | 114 | 24.17 GiB | 6-bit, Q8 head/attn |

| muse-glimmer-30B-Uncensored-Q6_0_ROCMFPX_LEAN.gguf | 116 | 21.09 GiB | 6-bit throughout |

| dflash-kquant.gguf | — | 1.52 GiB | DFlash drafter (Meta's, unmodified) — use this |

| mmproj-kquant.gguf | — | 1.30 GiB | vision projector (unmodified; vision tensors were not abliterated) |

Six files. llama.cpp loads them via --model, --model-draft and --mmproj. This repo is the uncensored family only — aligned builds are a separate repo.

Quantization

PYTHONPATH=gguf-py python convert_hf_to_gguf.py  --outtype bf16 --outfile unc-BF16.gguf
llama-quantize unc-BF16.gguf …-FAST.gguf Q4_0_ROCMFP4_FAST 16
llama-quantize unc-BF16.gguf …-STRIX_LEAN.gguf Q4_0_ROCMFP4_STRIX_LEAN 16

No extra --tensor flags — matches the published aligned card. ROCmFPX llama-quantize only.

Known issues (same as aligned)

1. Vulkan/CUDA/CPU cannot load these files — ROCmFP4 is ROCm-only.

2. Vision requires -fa off.

3. Small max_tokens returns empty content — budget goes to reasoning_content.

4. --reasoning-budget is not enforced; use reasoning_strength.

5. This is an uncensored research build. Do not deploy it as the public default.

Not yet measured

| Test | Status |

| --- | --- |

| Perplexity / KL vs BF16 | ❓ not measured |

| MMLU-Pro, GPQA, GSM8K | ❓ not run |

| Tool-calling 7-case suite on the unc weights | ❓ not re-run (aligned scored 6/7, model-level) |

| Vision spatial 3/3 on the unc projector | ❓ projector reused, not re-scored |

| Independent reproduction | ❓ none yet |

License and attribution

Base model: Meta Muse-Glimmer-30B (Apache 2.0). ROCmFP4 types: ROCmFPX. This repository is quantisation and measurement of an abliterated Muse-Glimmer-30B checkpoint.

See the aligned card for the muse-glimmer architecture port, DFlash notes, and tool-calling suite.