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 / README.md

vmlinux

Fix artifact download links in model card

399cc07 verified about 3 hours ago

preview code

Raw

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	---
	base_model:
	- meta-models/Muse-Glimmer-30B
	- meta-models/Muse-Glimmer-30B-assistant
	base_model_relation: quantized
	library_name: llama.cpp
	pipeline_tag: image-text-to-text
	license: apache-2.0
	tags:
	- gguf
	- llama.cpp
	- rocm
	- rocmfpx
	- amd
	- strix-halo
	- gfx1151
	- imatrix
	- multimodal
	- muse-glimmer
	- conversational
	- dflash
	- speculative-decoding
	---

	# Muse-Glimmer-30B ROCmFPX GGUF

	ROCmFP4 and ROCmFP8 builds of
	[meta-models/Muse-Glimmer-30B](https://huggingface.co/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](https://github.com/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`](https://huggingface.co/vmlinux/Muse-Glimmer-30B-ROCmFPX-GGUF/blob/main/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

	Click a filename to download it directly from the Hub.

	\| File \| ROCmFPX preset \| Size \| BPW \| iMatrix \| Prompt t/s \| Output t/s \|
	\| --- \| --- \| ---: \| ---: \| --- \| ---: \| ---: \|
	\| [`Muse-Glimmer-30B-ROCmFP4.gguf`](https://huggingface.co/vmlinux/Muse-Glimmer-30B-ROCmFPX-GGUF/resolve/main/Muse-Glimmer-30B-ROCmFP4.gguf?download=true) \| `Q4_0_ROCMFP4_STRIX` \| 14.17 GiB \| 4.36 \| Yes \| 113.7 \| 14.9 \|
	\| [`Muse-Glimmer-30B-ROCmFP4-Q6-QUALITY.gguf`](https://huggingface.co/vmlinux/Muse-Glimmer-30B-ROCmFPX-GGUF/resolve/main/Muse-Glimmer-30B-ROCmFP4-Q6-QUALITY.gguf?download=true) \| `Q4_0_ROCMFP4_COHERENT` \| 14.94 GiB \| 4.60 \| Yes \| 39.0 \| 14.0 \|
	\| [`Muse-Glimmer-30B-ROCmFP8.gguf`](https://huggingface.co/vmlinux/Muse-Glimmer-30B-ROCmFPX-GGUF/resolve/main/Muse-Glimmer-30B-ROCmFP8.gguf?download=true) \| `Q8_0_ROCMFPX` \| 26.77 GiB \| 8.25 \| No \| 96.7 \| 7.8 \|
	\| [`mmproj-Muse-Glimmer-30B-BF16.gguf`](https://huggingface.co/vmlinux/Muse-Glimmer-30B-ROCmFPX-GGUF/resolve/main/mmproj-Muse-Glimmer-30B-BF16.gguf?download=true) \| BF16 vision projector \| 3.59 GiB \| — \| — \| 81.7 \| 14.9 \|
	\| [`Muse-Glimmer-30B-DFlash-ROCmFP4.gguf`](https://huggingface.co/vmlinux/Muse-Glimmer-30B-ROCmFPX-GGUF/resolve/main/Muse-Glimmer-30B-DFlash-ROCmFP4.gguf?download=true) \| `Q4_0_ROCMFP4_STRIX` drafter \| 1.39 GiB \| 4.63 \| No \| 65.5¹ \| 28.3¹ \|
	\| [`Muse-Glimmer-30B-DFlash-ROCmFP8.gguf`](https://huggingface.co/vmlinux/Muse-Glimmer-30B-ROCmFPX-GGUF/resolve/main/Muse-Glimmer-30B-DFlash-ROCmFP8.gguf?download=true) \| `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](https://github.com/charlie12345/ROCmFPX) base commit
	`00d54526e24e3aba4c76474e3147cbf9c7cc034c`
	- upstream llama.cpp Muse support commit
	[`62bf73d25c53b8161f8a22894d4f90c4aebbd7d0`](https://github.com/ggml-org/llama.cpp/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](https://huggingface.co/vmlinux/Muse-Glimmer-30B-ROCmFPX-GGUF/blob/main/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:

	```bash
	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

	```bash
	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:

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

	Vision:

	```bash
	./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):

	```bash
	./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`](https://huggingface.co/meta-models/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`](https://huggingface.co/vmlinux/Muse-Glimmer-30B-ROCmFPX-GGUF/blob/main/SHA256SUMS);
	source, intermediate, and calibration hashes are retained in
	[`PROVENANCE_SHA256SUMS`](https://huggingface.co/vmlinux/Muse-Glimmer-30B-ROCmFPX-GGUF/blob/main/PROVENANCE_SHA256SUMS)
	- Detailed local build report:
	[`BUILD_RESULTS.md`](https://huggingface.co/vmlinux/Muse-Glimmer-30B-ROCmFPX-GGUF/blob/main/BUILD_RESULTS.md)

	The original model license and usage policy apply. See the
	[source model card](https://huggingface.co/meta-models/Muse-Glimmer-30B) before
	use.