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

OpenClaw new

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"

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

Muse-Glimmer-30B ROCmFPX GGUF

ROCmFP4 and ROCmFP8 builds of meta-models/Muse-Glimmer-30B, targeted and tested on AMD Strix Halo (gfx1151). Rawr. 🦖

At publication time, a Hub search returned no other Muse Glimmer ROCmFPX repositories. These are custom ROCmFPX formats, not ordinary llama.cpp Q4/Q8 files; read the compatibility section before downloading.

Experimental runtime required: these GGUFs need a patched build of charlie12345/ROCmFPX. Stock llama.cpp does not implement the ROCmFP4/ROCmFP8 tensor layouts, while the pinned ROCmFPX base predates Muse Glimmer support. Apply the included ROCmFPX-Muse-Glimmer.patch to ROCmFPX commit 00d54526e…, then build that checkout. The patch adds the upstream Muse text, vision, and DFlash support, ports it to ROCmFPX's older APIs, supplies the FP16 sparse-attention mask required by its multimodal flash-attention path, and backports the DFlash injected-cache rotation fix required when using quantized KV caches.

Files

File	ROCmFPX preset	Size	BPW	iMatrix	Prompt t/s	Output t/s
`Muse-Glimmer-30B-ROCmFP4.gguf`	`Q4_0_ROCMFP4_STRIX`	14.17 GiB	4.36	Yes	113.7	14.9
`Muse-Glimmer-30B-ROCmFP4-Q6-QUALITY.gguf`	`Q4_0_ROCMFP4_COHERENT`	14.94 GiB	4.60	Yes	39.0	14.0
`Muse-Glimmer-30B-ROCmFP8.gguf`	`Q8_0_ROCMFPX`	26.77 GiB	8.25	No	96.7	7.8
`mmproj-Muse-Glimmer-30B-BF16.gguf`	BF16 vision projector	3.59 GiB	—	—	81.7	14.9
`Muse-Glimmer-30B-DFlash-ROCmFP4.gguf`	`Q4_0_ROCMFP4_STRIX` drafter	1.39 GiB	4.63	No	65.5¹	28.3¹
`Muse-Glimmer-30B-DFlash-ROCmFP8.gguf`	`Q8_0_ROCMFPX` drafter	2.47 GiB	8.25	No	65.1¹	27.2¹

¹ End-to-end target measurement with Muse-Glimmer-30B-ROCmFP4.gguf, DFlash enabled, and a six-token draft window. These are three-run means; the main-model and projector rows are the earlier short smoke tests described under Validation.

Suggested choices:

ROCmFP4: default Strix Halo speed/quality build. Fast FP4 body, dual-scale FP4 attention K/V, and Q6_K token embeddings.
ROCmFP4-Q6-QUALITY: coherence-biased build. Dual-scale FP4 throughout the body with Q6_K token embeddings.
ROCmFP8: high-fidelity 8.25-bpw reference build.
DFlash ROCmFP4: recommended drafter on Strix Halo. It is smaller and was slightly faster than the FP8 drafter in the measured six-token configuration.
DFlash ROCmFP8: higher-precision drafter reference; useful for comparing acceptance behavior and tuning on other hardware.

The BF16 projector works with all three text models.

iMatrix

Both FP4 models use the same GGUF importance matrix:

500 chunks × 512 tokens (approximately 256k calibration tokens)
416 tensor importance entries consumed by each quantizer
varied narrative/general-language calibration corpus
checkpoints saved every 100 chunks

Q8_0_ROCMFPX does not consume importance weights, so the FP8 reference was intentionally built without an iMatrix.

The two DFlash drafters were quantized directly from Meta's official assistant checkpoint without an iMatrix. Their role is proposal generation: every draft is verified by the main model, so drafter quantization changes acceptance and speed rather than bypassing the target model's output decision.

Compatibility

These files use experimental ROCmFPX tensor types and will not load in stock llama.cpp.

The validated runtime was built from:

ROCmFPX base commit 00d54526e24e3aba4c76474e3147cbf9c7cc034c
upstream llama.cpp Muse support commit 62bf73d25c53b8161f8a22894d4f90c4aebbd7d0
small compatibility adaptations for the older ROCmFPX chat, model, and multimodal APIs
the upstream DFlash quantized-cache rotation fix, adapted to this older graph API; without it, Q4_0 draft KV caches load but produce near-zero acceptance

ROCmFPX-Muse-Glimmer.patch contains the complete patch against the pinned ROCmFPX base. The runtime was built with ROCm and Vulkan backends; the reported generation tests used ROCm0 on gfx1151.

Minimal runtime setup:

git clone https://github.com/charlie12345/ROCmFPX.git
cd ROCmFPX
git checkout 00d54526e24e3aba4c76474e3147cbf9c7cc034c

hf download vmlinux/Muse-Glimmer-30B-ROCmFPX-GGUF \
  ROCmFPX-Muse-Glimmer.patch \
  --local-dir /tmp/muse-glimmer-rocmfpx
git apply /tmp/muse-glimmer-rocmfpx/ROCmFPX-Muse-Glimmer.patch

BUILD_DIR=build-muse-rocmfpx \
JOBS=16 \
CMAKE_HIP_COMPILER=/opt/rocm-7.2.0/lib/llvm/bin/clang++ \
GGML_HIP_ROCWMMA_FATTN=OFF \
./scripts/build-strix-rocmfp4-mtp.sh

Adjust CMAKE_HIP_COMPILER for the installed ROCm version. The resulting runtime binaries are under build-muse-rocmfpx/bin/. The patch must be applied to the exact pinned commit; git apply --check was verified before publishing.

Download and run

hf download vmlinux/Muse-Glimmer-30B-ROCmFPX-GGUF \
  Muse-Glimmer-30B-ROCmFP4.gguf \
  Muse-Glimmer-30B-DFlash-ROCmFP4.gguf \
  mmproj-Muse-Glimmer-30B-BF16.gguf \
  --local-dir ./Muse-Glimmer-30B-ROCmFPX

Text:

./llama-cli \
  -m ./Muse-Glimmer-30B-ROCmFPX/Muse-Glimmer-30B-ROCmFP4.gguf \
  -dev ROCm0 -ngl all -c 8192 -cnv

Vision:

./llama-cli \
  -m ./Muse-Glimmer-30B-ROCmFPX/Muse-Glimmer-30B-ROCmFP4.gguf \
  -mm ./Muse-Glimmer-30B-ROCmFPX/mmproj-Muse-Glimmer-30B-BF16.gguf \
  --image ./image.png \
  -p "Describe this image." \
  -dev ROCm0 -ngl all -c 8192 -cnv -st

DFlash speculative decoding (recommended starting point):

./llama-cli \
  -m ./Muse-Glimmer-30B-ROCmFPX/Muse-Glimmer-30B-ROCmFP4.gguf \
  --model-draft ./Muse-Glimmer-30B-ROCmFPX/Muse-Glimmer-30B-DFlash-ROCmFP4.gguf \
  --spec-type draft-dflash \
  -dev ROCm0 -ngl all \
  --spec-draft-device ROCm0 --spec-draft-ngl all \
  -ctk q4_0 -ctv q4_0 \
  --spec-draft-type-k q4_0 --spec-draft-type-v q4_0 \
  --spec-draft-n-max 6 --spec-draft-n-min 0 \
  --spec-draft-p-min 0.0 --spec-draft-p-split 0.10 \
  --no-spec-draft-backend-sampling \
  -c 8192 -cnv

Validation

All three files completed clean, single-turn ROCm generation with every layer offloaded. The projector completed an end-to-end image test and correctly identified the test image as a folder icon.

Model	Prompt processing	Token generation
ROCmFP4	113.7 tok/s	14.9 tok/s
ROCmFP4-Q6-QUALITY	39.0 tok/s	14.0 tok/s
ROCmFP8	96.7 tok/s	7.8 tok/s
ROCmFP4 + BF16 projector	81.7 tok/s	14.9 tok/s

These are short smoke-test measurements, not a formal benchmark. Host: AMD Strix Halo gfx1151, 128 GiB unified memory, ROCm backend, 1,024-token text context (2,048 for vision).

DFlash benchmark

The additive DFlash benchmark used the default ROCmFP4 target, batch size 1, greedy decoding, three text prompts, 256 generated tokens per prompt, a 2,048-token context, flash attention, full ROCm0 offload, and Q4_0 target and draft KV caches. Values are arithmetic means of the three runs.

Mode	Draft window	Prompt t/s	Output t/s	Speedup	Output range	Weighted draft acceptance
No speculation	—	73.7	13.7	1.00×	13.7–13.7	—
DFlash ROCmFP4	6	65.5	28.3	2.07×	24.3–31.8	35.8% (519/1,448)
DFlash ROCmFP8	6	65.1	27.2	1.99×	24.1–33.0	35.7% (519/1,454)
DFlash ROCmFP4	15	65.6	24.6	1.80×	18.5–30.7	17.1% (543/3,171)
DFlash ROCmFP8	15	65.3	26.3	1.92×	19.2–34.6	19.5% (563/2,891)

Raw llama.cpp throughput lines for all 15 benchmark runs:

Mode	Window	Prompt 1 `[Prompt \| Output]`	Prompt 2 `[Prompt \| Output]`	Prompt 3 `[Prompt \| Output]`
No speculation	—	55.3 t/s \| 13.7 t/s	83.5 t/s \| 13.7 t/s	82.4 t/s \| 13.7 t/s
DFlash ROCmFP4	6	50.3 t/s \| 28.8 t/s	73.7 t/s \| 24.3 t/s	72.5 t/s \| 31.8 t/s
DFlash ROCmFP8	6	50.2 t/s \| 24.6 t/s	73.4 t/s \| 24.1 t/s	71.8 t/s \| 33.0 t/s
DFlash ROCmFP4	15	50.2 t/s \| 24.6 t/s	74.2 t/s \| 18.5 t/s	72.5 t/s \| 30.7 t/s
DFlash ROCmFP8	15	50.4 t/s \| 25.2 t/s	73.5 t/s \| 19.2 t/s	72.1 t/s \| 34.6 t/s

This is a small local throughput benchmark, not a universal performance claim. Acceptance depends strongly on prompt and generation content. Six draft tokens was the best tested practical default on this host; tune it for your workload.

Additional verification:

731 tensors and muse-glimmer architecture in every text GGUF
809 tensors and 50 vision blocks in the projector
ROCmFP4 kernel copy/conversion tests: 34/34 passed on Vulkan
test-quantize-fns and architecture tests passed
SHA-256 hashes supplied in SHA256SUMS

DFlash, not MTP

These new companion files are converted from Meta's official Muse-Glimmer-30B-assistant checkpoint. It is a five-layer MuseGlimmerAssistantModel using DFlash block diffusion with a trained block size of 16. Run it with --spec-type draft-dflash; it is not an MTP checkpoint and should not be run with draft-mtp.

Provenance

Source revision: f84ecc3a0ea984a4c04542a84269e3d065350a6e
DFlash source revision: 2c86316d689027b91123638739743fef1d425233
Conversion: upstream llama.cpp d2f83055dca6dd009d8a52bdff792fbb286f4444
Every published GGUF is covered by SHA256SUMS; source, intermediate, and calibration hashes are retained in PROVENANCE_SHA256SUMS
Detailed local build report: BUILD_RESULTS.md

The original model license and usage policy apply. See the source model card before use.

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

Base model

meta-models/Muse-Glimmer-30B

Quantized

(30)

this model

Mode	Window	Prompt 1 `[Prompt \| Output]`	Prompt 2 `[Prompt \| Output]`	Prompt 3 `[Prompt \| Output]`
No speculation	—	55.3 t/s \| 13.7 t/s	83.5 t/s \| 13.7 t/s	82.4 t/s \| 13.7 t/s
DFlash ROCmFP4	6	50.3 t/s \| 28.8 t/s	73.7 t/s \| 24.3 t/s	72.5 t/s \| 31.8 t/s
DFlash ROCmFP8	6	50.2 t/s \| 24.6 t/s	73.4 t/s \| 24.1 t/s	71.8 t/s \| 33.0 t/s
DFlash ROCmFP4	15	50.2 t/s \| 24.6 t/s	74.2 t/s \| 18.5 t/s	72.5 t/s \| 30.7 t/s
DFlash ROCmFP8	15	50.4 t/s \| 25.2 t/s	73.5 t/s \| 19.2 t/s	72.1 t/s \| 34.6 t/s