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_PLAN.md

vmlinux

Document DFlash conversion and ROCm benchmarks

9796a93 verified 9 days ago

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	# Muse-Glimmer-30B ROCmFPX build

	## Pinned inputs

	- Hugging Face model: `meta-models/Muse-Glimmer-30B`
	- Model revision: `f84ecc3a0ea984a4c04542a84269e3d065350a6e`
	- Hugging Face drafter: `meta-models/Muse-Glimmer-30B-assistant`
	- Drafter revision: `2c86316d689027b91123638739743fef1d425233`
	- ROCmFPX base: `00d54526e24e3aba4c76474e3147cbf9c7cc034c`
	- Upstream Muse support: `62bf73d25c53b8161f8a22894d4f90c4aebbd7d0`
	- Upstream converter worktree: `d2f83055dca6dd009d8a52bdff792fbb286f4444`
	- Converter environment: Python venv with `transformers==5.15.0`
	- Target GPU: AMD Strix Halo, `gfx1151`

	The toolchain directory is an uncommitted worktree based on ROCmFPX with the
	upstream Muse support adapted to the older ROCmFPX APIs. It does not alter the
	canonical `/home/vmlinux/models/llama.cpp` checkout or build.

	## Deliverables

	\| Artifact label \| Quantizer preset \| Intent \|
	\| --- \| --- \| --- \|
	\| ROCmFP4 \| `Q4_0_ROCMFP4_STRIX` \| Strix Halo speed/quality recipe; dual-scale FP4 attention K/V and Q6_K token embeddings \|
	\| ROCmFP8 \| `Q8_0_ROCMFPX` \| High-fidelity 8.25-bpw reference layout \|
	\| ROCmFP4-Q6-QUALITY \| `Q4_0_ROCMFP4_COHERENT` \| Dual-scale FP4 body with Q6_K token embeddings for the coherence-biased quality build \|
	\| DFlash ROCmFP4 \| `Q4_0_ROCMFP4_STRIX` \| Small, speed-oriented official DFlash companion; recommended on Strix Halo \|
	\| DFlash ROCmFP8 \| `Q8_0_ROCMFPX` \| Higher-precision DFlash reference for acceptance and hardware tuning \|

	The two FP4 builds use a 500-chunk importance matrix generated from the local
	`narrative-general-imatrix-sample.txt` corpus. This supplies varied prose,
	reasoning, and general-language activations; the coherence preset itself
	provides the agent/tool-oriented tensor routing. The FP8 reference does not
	need an importance matrix because its preset does not consume importance
	weights.

	The DFlash drafters do not use the main-model iMatrix. They are quantized from
	the official assistant checkpoint, and the FP4 conversion keeps the feature
	fusion `fc.weight` at Q8_0 as a precision floor.

	## Paths

	- Source snapshot: `source/Muse-Glimmer-30B/`
	- DFlash source snapshot: `source/Muse-Glimmer-30B-assistant/`
	- BF16 text model and projector: `intermediate/`
	- Importance matrix: `calibration/`
	- Final models: `artifacts/`
	- Logs and checksums: `logs/` and `manifests/`

	## Conversion and quantization outline

	```bash
	.venv/bin/python converter-upstream/convert_hf_to_gguf.py source/Muse-Glimmer-30B \
	--outtype bf16 --outfile intermediate/Muse-Glimmer-30B-BF16.gguf

	.venv/bin/python converter-upstream/convert_hf_to_gguf.py source/Muse-Glimmer-30B \
	--mmproj --outtype bf16 \
	--outfile intermediate/mmproj-Muse-Glimmer-30B-BF16.gguf

	toolchain/build-muse-rocmfpx/bin/llama-quantize \
	--imatrix calibration/Muse-Glimmer-30B-narrative-general.imatrix \
	intermediate/Muse-Glimmer-30B-BF16.gguf \
	artifacts/Muse-Glimmer-30B-ROCmFP4.gguf Q4_0_ROCMFP4_STRIX

	toolchain/build-muse-rocmfpx/bin/llama-quantize \
	intermediate/Muse-Glimmer-30B-BF16.gguf \
	artifacts/Muse-Glimmer-30B-ROCmFP8.gguf Q8_0_ROCMFPX

	toolchain/build-muse-rocmfpx/bin/llama-quantize \
	--imatrix calibration/Muse-Glimmer-30B-narrative-general.imatrix \
	intermediate/Muse-Glimmer-30B-BF16.gguf \
	artifacts/Muse-Glimmer-30B-ROCmFP4-Q6-QUALITY.gguf \
	Q4_0_ROCMFP4_COHERENT

	.venv/bin/python toolchain/scripts/convert_hf_to_gguf_modular.py \
	source/Muse-Glimmer-30B-assistant \
	--target-model-dir source/Muse-Glimmer-30B \
	--outtype bf16 \
	--outfile intermediate/Muse-Glimmer-30B-DFlash-BF16.gguf

	toolchain/build-muse-rocmfpx/bin/llama-quantize \
	intermediate/Muse-Glimmer-30B-DFlash-BF16.gguf \
	artifacts/Muse-Glimmer-30B-DFlash-ROCmFP4.gguf \
	Q4_0_ROCMFP4_STRIX

	toolchain/build-muse-rocmfpx/bin/llama-quantize \
	intermediate/Muse-Glimmer-30B-DFlash-BF16.gguf \
	artifacts/Muse-Glimmer-30B-DFlash-ROCmFP8.gguf \
	Q8_0_ROCMFPX
	```

	Exact tensor inventories, sizes, checksums, and smoke-test results are in
	`BUILD_RESULTS.md` and `manifests/SHA256SUMS`.