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 8 days ago

preview code

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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

.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.