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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4.62 kB

	# 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`.