Image-Text-to-Text
GGUF
llama.cpp
rocm
rocmfpx
amd
strix-halo
gfx1151
imatrix
multimodal
muse-glimmer
conversational
dflash
speculative-decoding
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
- llama.cpp
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
- Pi
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
File size: 5,230 Bytes
eba6d2c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | # Muse Glimmer Usage Policy
Meta is committed to promoting safe and fair use of its tools and features, including Muse Glimmer. This Usage Policy (“Policy”) applies to your access or use of Muse Glimmer. The most recent copy of this policy can be found at: [https://huggingface.co/meta-models/Muse-Glimmer-30B/blob/main/USAGE\_POLICY](https://huggingface.co/meta-models/Muse-Glimmer-30B/blob/main/USAGE_POLICY.md)
## Prohibited Uses
We want everyone to use Muse Glimmer safely and responsibly. Muse Glimmer is not intended for individuals under the age of 18\.
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Please report any violation of this Policy, software "bug," or other problems that could lead to a violation of this Policy through one of the following means:
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