Instructions to use vmlinux/Muse-Glimmer-30B-ROCmFPX-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 vmlinux/Muse-Glimmer-30B-ROCmFPX-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 vmlinux/Muse-Glimmer-30B-ROCmFPX-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf vmlinux/Muse-Glimmer-30B-ROCmFPX-GGUF:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf vmlinux/Muse-Glimmer-30B-ROCmFPX-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf vmlinux/Muse-Glimmer-30B-ROCmFPX-GGUF:BF16

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 vmlinux/Muse-Glimmer-30B-ROCmFPX-GGUF:BF16
# Run inference directly in the terminal:
./llama-cli -hf vmlinux/Muse-Glimmer-30B-ROCmFPX-GGUF:BF16

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 vmlinux/Muse-Glimmer-30B-ROCmFPX-GGUF:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf vmlinux/Muse-Glimmer-30B-ROCmFPX-GGUF:BF16

Use Docker

docker model run hf.co/vmlinux/Muse-Glimmer-30B-ROCmFPX-GGUF:BF16

LM Studio
Jan

vLLM

How to use vmlinux/Muse-Glimmer-30B-ROCmFPX-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vmlinux/Muse-Glimmer-30B-ROCmFPX-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": "vmlinux/Muse-Glimmer-30B-ROCmFPX-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/vmlinux/Muse-Glimmer-30B-ROCmFPX-GGUF:BF16

Ollama
How to use vmlinux/Muse-Glimmer-30B-ROCmFPX-GGUF with Ollama:
```
ollama run hf.co/vmlinux/Muse-Glimmer-30B-ROCmFPX-GGUF:BF16
```

Unsloth Studio

How to use vmlinux/Muse-Glimmer-30B-ROCmFPX-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for vmlinux/Muse-Glimmer-30B-ROCmFPX-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for vmlinux/Muse-Glimmer-30B-ROCmFPX-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for vmlinux/Muse-Glimmer-30B-ROCmFPX-GGUF to start chatting

How to use vmlinux/Muse-Glimmer-30B-ROCmFPX-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vmlinux/Muse-Glimmer-30B-ROCmFPX-GGUF:BF16

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "vmlinux/Muse-Glimmer-30B-ROCmFPX-GGUF:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use vmlinux/Muse-Glimmer-30B-ROCmFPX-GGUF with Docker Model Runner:
```
docker model run hf.co/vmlinux/Muse-Glimmer-30B-ROCmFPX-GGUF:BF16
```

Lemonade

How to use vmlinux/Muse-Glimmer-30B-ROCmFPX-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull vmlinux/Muse-Glimmer-30B-ROCmFPX-GGUF:BF16

Run and chat with the model

lemonade run user.Muse-Glimmer-30B-ROCmFPX-GGUF-BF16

List all available models

lemonade list

Hermes Agent

How to use vmlinux/Muse-Glimmer-30B-ROCmFPX-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 vmlinux/Muse-Glimmer-30B-ROCmFPX-GGUF:BF16

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 vmlinux/Muse-Glimmer-30B-ROCmFPX-GGUF:BF16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use vmlinux/Muse-Glimmer-30B-ROCmFPX-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vmlinux/Muse-Glimmer-30B-ROCmFPX-GGUF:BF16

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 "vmlinux/Muse-Glimmer-30B-ROCmFPX-GGUF:BF16" \
  --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"

Muse-Glimmer-30B-ROCmFPX-GGUF / BUILD_RESULTS.md

vmlinux

Add prompt and output throughput to DFlash results

2bd83a8 verified 7 days ago

preview code

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Build results

Outputs

File	Preset / type	Bytes	Size	BPW
`artifacts/Muse-Glimmer-30B-ROCmFP4.gguf`	`Q4_0_ROCMFP4_STRIX`, ftype 105	15,210,123,424	14.17 GiB	4.36
`artifacts/Muse-Glimmer-30B-ROCmFP8.gguf`	`Q8_0_ROCMFPX`, ftype 111	28,742,539,776	26.77 GiB	8.25
`artifacts/Muse-Glimmer-30B-ROCmFP4-Q6-QUALITY.gguf`	`Q4_0_ROCMFP4_COHERENT`, ftype 102	16,032,978,080	14.94 GiB	4.60
`intermediate/mmproj-Muse-Glimmer-30B-BF16.gguf`	BF16 vision projector	3,849,173,920	3.59 GiB	—
`artifacts/Muse-Glimmer-30B-DFlash-ROCmFP4.gguf`	`Q4_0_ROCMFP4_STRIX` drafter	1,491,030,176	1.39 GiB	4.63
`artifacts/Muse-Glimmer-30B-DFlash-ROCmFP8.gguf`	`Q8_0_ROCMFPX` drafter	2,649,174,176	2.47 GiB	8.25

The text GGUFs each contain 731 tensors and report the muse-glimmer architecture. The projector contains 809 tensors, 50 vision blocks, and the muse-glimmer projector type.

The DFlash GGUFs each contain 58 tensors and report the dflash architecture. Their source is Meta's official five-layer MuseGlimmerAssistantModel at revision 2c86316d689027b91123638739743fef1d425233. The modular ROCmFPX conversion and current upstream conversion produced byte-identical BF16 GGUFs (dc4ecbb…ad891) before quantization.

Importance matrix

File: calibration/Muse-Glimmer-30B-narrative-general.imatrix
Format: GGUF importance matrix
Entries consumed by quantizer: 416
Calibration: 500 chunks × 512 tokens (approximately 256k tokens)
Input corpus: local narrative-general-imatrix-sample.txt
Recovery files: .at_100, .at_200, .at_300, .at_400, .at_500

Both FP4 quantizers parsed all 416 entries and reported that the matrix was computed from 500 chunks. The standalone --show-statistics mode exits with SIGFPE in this ROCmFPX base; this does not affect matrix generation or the two successful quantizer consumers.

Neither DFlash drafter uses an iMatrix. They were quantized directly from the official BF16 assistant; the custom FP4 precision floor retains fc.weight as Q8_0 while routing the remaining eligible weights through the Strix FP4 preset.

Runtime validation

All checks used the experimental Muse-capable ROCmFPX build on ROCm0 with all layers offloaded and a 1,024-token context unless noted.

Model	Result	Prompt rate	Decode rate
ROCmFP4	clean 16-token single-turn generation	113.7 tok/s	14.9 tok/s
ROCmFP8	clean 16-token single-turn generation	96.7 tok/s	7.8 tok/s
ROCmFP4-Q6-QUALITY	clean 16-token single-turn generation	39.0 tok/s	14.0 tok/s
ROCmFP4 + BF16 mmproj	loaded `text, vision`, decoded a PNG as a folder icon	81.7 tok/s	14.9 tok/s

DFlash throughput

Controlled DFlash runs used the ROCmFP4 target, three prompts, 256 generated tokens per prompt, context 2,048, batch size 1, greedy decoding, flash attention, full ROCm0 offload, and Q4_0 KV caches for both contexts.

Mode	Draft window	Mean prompt t/s	Mean output t/s	Speedup	Output range	Weighted acceptance
Baseline	—	73.7	13.7	1.00×	13.7–13.7	—
DFlash ROCmFP4	6	65.5	28.3	2.07×	24.3–31.8	519/1,448 (35.8%)
DFlash ROCmFP8	6	65.1	27.2	1.99×	24.1–33.0	519/1,454 (35.7%)
DFlash ROCmFP4	15	65.6	24.6	1.80×	18.5–30.7	543/3,171 (17.1%)
DFlash ROCmFP8	15	65.3	26.3	1.92×	19.2–34.6	563/2,891 (19.5%)

The six-token window is the recommended starting point on this host. The runtime patch includes the upstream DFlash injected-cache rotation fix; before that fix, quantized KV caches produced near-zero acceptance despite valid target verification.

The vision smoke test used a 2,048-token context and a local system icon. The older ROCmFPX flash-attention ABI requires the Muse sparse attention mask in FP16; the experiment toolchain converts the upstream FP32 mask values to FP16 at upload time.

Toolchain verification

test-llama-archs: passed, including Muse Glimmer architecture loading
test-quantize-fns: passed
Vulkan ROCmFP4 copy/conversion backend tests: 34/34 passed
llama-quantize, llama-imatrix, llama-cli, and llama-server: built
ROCmFPX build version: 209 (00d54526e)
DFlash ROCmFP4 and ROCmFP8 both completed verified speculative generation with Q4_0 KV caches after the injected-cache rotation backport

SHA-256 digests for sources, intermediates, and final outputs are recorded in manifests/SHA256SUMS.