Instructions to use meta-models/Muse-Glimmer-30B-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 meta-models/Muse-Glimmer-30B-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 meta-models/Muse-Glimmer-30B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf meta-models/Muse-Glimmer-30B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf meta-models/Muse-Glimmer-30B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf meta-models/Muse-Glimmer-30B-GGUF:Q4_K_M

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 meta-models/Muse-Glimmer-30B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf meta-models/Muse-Glimmer-30B-GGUF:Q4_K_M

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 meta-models/Muse-Glimmer-30B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf meta-models/Muse-Glimmer-30B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/meta-models/Muse-Glimmer-30B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use meta-models/Muse-Glimmer-30B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "meta-models/Muse-Glimmer-30B-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": "meta-models/Muse-Glimmer-30B-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/meta-models/Muse-Glimmer-30B-GGUF:Q4_K_M

Ollama
How to use meta-models/Muse-Glimmer-30B-GGUF with Ollama:
```
ollama run hf.co/meta-models/Muse-Glimmer-30B-GGUF:Q4_K_M
```

Unsloth Studio

How to use meta-models/Muse-Glimmer-30B-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 meta-models/Muse-Glimmer-30B-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 meta-models/Muse-Glimmer-30B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for meta-models/Muse-Glimmer-30B-GGUF to start chatting

How to use meta-models/Muse-Glimmer-30B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf meta-models/Muse-Glimmer-30B-GGUF:Q4_K_M

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": "meta-models/Muse-Glimmer-30B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use meta-models/Muse-Glimmer-30B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf meta-models/Muse-Glimmer-30B-GGUF:Q4_K_M

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 "meta-models/Muse-Glimmer-30B-GGUF:Q4_K_M" \
  --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 meta-models/Muse-Glimmer-30B-GGUF with Docker Model Runner:
```
docker model run hf.co/meta-models/Muse-Glimmer-30B-GGUF:Q4_K_M
```

Lemonade

How to use meta-models/Muse-Glimmer-30B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull meta-models/Muse-Glimmer-30B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Muse-Glimmer-30B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use meta-models/Muse-Glimmer-30B-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 meta-models/Muse-Glimmer-30B-GGUF:Q4_K_M

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 meta-models/Muse-Glimmer-30B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

betodepaola commited on 3 days ago

Commit

b1f3e6e

verified ·

1 Parent(s): 67e9aed

Add Muse Glimmer 30B GGUF set (k-quants, draft, mmproj)

Browse files

Adds the Muse Glimmer 30B GGUF set, built from the same artifacts published to
someorgtoo/onyx_final:gguf_weights/.

Added (LFS):
- muse-glimmer-30B-kquant-dynamic.gguf (~19.7 GB) dynamic K-quant
- muse-glimmer-30B-kquant-17gb.gguf (~16.8 GB) K-quant, ~17 GB (recommended)
- dflash-kquant.gguf (~1.6 GB) DFlash speculative draft
- mmproj-kquant.gguf (~1.4 GB) vision projector

.gitattributes updated so *.gguf routes to LFS.

Files changed (5) hide show

.gitattributes +1 -0
dflash-kquant.gguf +3 -0
mmproj-kquant.gguf +3 -0
muse-glimmer-30B-kquant-17gb.gguf +3 -0
muse-glimmer-30B-kquant-dynamic.gguf +3 -0

.gitattributes CHANGED Viewed

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text

 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.gguf filter=lfs diff=lfs merge=lfs -text

dflash-kquant.gguf ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:27d9a805fa29b943cfb6ad4843367cd4eaaaf06bd452d8cc3e00a2cd18a677bc
+size 1631205312

mmproj-kquant.gguf ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:f48b452316f9b213758e8659444029b961a24a07f99a1abb2a9f88b06f7c00c6
+size 1400328928

muse-glimmer-30B-kquant-17gb.gguf ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:7e9b74b7c8875e9e265695df9613bf6290f2392e479ce740495a129019c488d8
+size 16756681056

muse-glimmer-30B-kquant-dynamic.gguf ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:513109c8319115f69eb09fb7b118c97c8167d15bc014fd7670d2e30489bf106c
+size 19653957984