Instructions to use kingjones777/Muse-Glimmer-30B-Uncensored-ROCmFP4-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use kingjones777/Muse-Glimmer-30B-Uncensored-ROCmFP4-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf kingjones777/Muse-Glimmer-30B-Uncensored-ROCmFP4-GGUF
# Run inference directly in the terminal:
llama cli -hf kingjones777/Muse-Glimmer-30B-Uncensored-ROCmFP4-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf kingjones777/Muse-Glimmer-30B-Uncensored-ROCmFP4-GGUF
# Run inference directly in the terminal:
llama cli -hf kingjones777/Muse-Glimmer-30B-Uncensored-ROCmFP4-GGUF

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf kingjones777/Muse-Glimmer-30B-Uncensored-ROCmFP4-GGUF
# Run inference directly in the terminal:
./llama-cli -hf kingjones777/Muse-Glimmer-30B-Uncensored-ROCmFP4-GGUF

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf kingjones777/Muse-Glimmer-30B-Uncensored-ROCmFP4-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf kingjones777/Muse-Glimmer-30B-Uncensored-ROCmFP4-GGUF

Use Docker

docker model run hf.co/kingjones777/Muse-Glimmer-30B-Uncensored-ROCmFP4-GGUF

LM Studio
Jan

vLLM

How to use kingjones777/Muse-Glimmer-30B-Uncensored-ROCmFP4-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kingjones777/Muse-Glimmer-30B-Uncensored-ROCmFP4-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kingjones777/Muse-Glimmer-30B-Uncensored-ROCmFP4-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/kingjones777/Muse-Glimmer-30B-Uncensored-ROCmFP4-GGUF

Ollama
How to use kingjones777/Muse-Glimmer-30B-Uncensored-ROCmFP4-GGUF with Ollama:
```
ollama run hf.co/kingjones777/Muse-Glimmer-30B-Uncensored-ROCmFP4-GGUF
```
Unsloth Desktop

How to use kingjones777/Muse-Glimmer-30B-Uncensored-ROCmFP4-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kingjones777/Muse-Glimmer-30B-Uncensored-ROCmFP4-GGUF

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "kingjones777/Muse-Glimmer-30B-Uncensored-ROCmFP4-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use kingjones777/Muse-Glimmer-30B-Uncensored-ROCmFP4-GGUF with Docker Model Runner:
```
docker model run hf.co/kingjones777/Muse-Glimmer-30B-Uncensored-ROCmFP4-GGUF
```

Lemonade

How to use kingjones777/Muse-Glimmer-30B-Uncensored-ROCmFP4-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull kingjones777/Muse-Glimmer-30B-Uncensored-ROCmFP4-GGUF

Run and chat with the model

lemonade run user.Muse-Glimmer-30B-Uncensored-ROCmFP4-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use kingjones777/Muse-Glimmer-30B-Uncensored-ROCmFP4-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kingjones777/Muse-Glimmer-30B-Uncensored-ROCmFP4-GGUF

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default kingjones777/Muse-Glimmer-30B-Uncensored-ROCmFP4-GGUF

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use kingjones777/Muse-Glimmer-30B-Uncensored-ROCmFP4-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kingjones777/Muse-Glimmer-30B-Uncensored-ROCmFP4-GGUF

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "kingjones777/Muse-Glimmer-30B-Uncensored-ROCmFP4-GGUF" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

🔧 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.
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 <MODEL_DIR> --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)

Vulkan/CUDA/CPU cannot load these files — ROCmFP4 is ROCm-only.
Vision requires -fa off.
Small max_tokens returns empty content — budget goes to reasoning_content.
--reasoning-budget is not enforced; use reasoning_strength.
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.

Downloads last month: 649

GGUF

Model size

28B params

Architecture

muse-glimmer

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Model tree for kingjones777/Muse-Glimmer-30B-Uncensored-ROCmFP4-GGUF

Base model

meta-models/Muse-Glimmer-30B

Quantized

(176)

this model