Instructions to use YTan2000/Muse-Glimmer-30B-TQ3_4S with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use YTan2000/Muse-Glimmer-30B-TQ3_4S 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 YTan2000/Muse-Glimmer-30B-TQ3_4S:Q8_0
# Run inference directly in the terminal:
llama cli -hf YTan2000/Muse-Glimmer-30B-TQ3_4S:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf YTan2000/Muse-Glimmer-30B-TQ3_4S:Q8_0
# Run inference directly in the terminal:
llama cli -hf YTan2000/Muse-Glimmer-30B-TQ3_4S:Q8_0

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 YTan2000/Muse-Glimmer-30B-TQ3_4S:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf YTan2000/Muse-Glimmer-30B-TQ3_4S:Q8_0

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 YTan2000/Muse-Glimmer-30B-TQ3_4S:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf YTan2000/Muse-Glimmer-30B-TQ3_4S:Q8_0

Use Docker

docker model run hf.co/YTan2000/Muse-Glimmer-30B-TQ3_4S:Q8_0

LM Studio
Jan

vLLM

How to use YTan2000/Muse-Glimmer-30B-TQ3_4S with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "YTan2000/Muse-Glimmer-30B-TQ3_4S"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "YTan2000/Muse-Glimmer-30B-TQ3_4S",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/YTan2000/Muse-Glimmer-30B-TQ3_4S:Q8_0

Ollama
How to use YTan2000/Muse-Glimmer-30B-TQ3_4S with Ollama:
```
ollama run hf.co/YTan2000/Muse-Glimmer-30B-TQ3_4S:Q8_0
```

Unsloth Studio

How to use YTan2000/Muse-Glimmer-30B-TQ3_4S 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 YTan2000/Muse-Glimmer-30B-TQ3_4S 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 YTan2000/Muse-Glimmer-30B-TQ3_4S to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for YTan2000/Muse-Glimmer-30B-TQ3_4S to start chatting

How to use YTan2000/Muse-Glimmer-30B-TQ3_4S with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf YTan2000/Muse-Glimmer-30B-TQ3_4S:Q8_0

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": "YTan2000/Muse-Glimmer-30B-TQ3_4S:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use YTan2000/Muse-Glimmer-30B-TQ3_4S with Docker Model Runner:
```
docker model run hf.co/YTan2000/Muse-Glimmer-30B-TQ3_4S:Q8_0
```

Lemonade

How to use YTan2000/Muse-Glimmer-30B-TQ3_4S with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull YTan2000/Muse-Glimmer-30B-TQ3_4S:Q8_0

Run and chat with the model

lemonade run user.Muse-Glimmer-30B-TQ3_4S-Q8_0

List all available models

lemonade list

Hermes Agent

How to use YTan2000/Muse-Glimmer-30B-TQ3_4S with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf YTan2000/Muse-Glimmer-30B-TQ3_4S:Q8_0

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 YTan2000/Muse-Glimmer-30B-TQ3_4S:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use YTan2000/Muse-Glimmer-30B-TQ3_4S with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf YTan2000/Muse-Glimmer-30B-TQ3_4S:Q8_0

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 "YTan2000/Muse-Glimmer-30B-TQ3_4S:Q8_0" \
  --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-TQ3_4S

Required Runtime

This model uses the custom TQ3_4S tensor type. It requires turbo-tan/llama.cpp-tq3. Stock llama.cpp builds without TurboQuant support cannot load it.

Model Files

File	Size	Purpose
`Muse-Glimmer-30B-TQ3_4S.gguf`	13.78 GiB	Main model (4.25 bpw)
`mmproj-Muse-Glimmer-30B-Q8_0.gguf`	2.0 GiB	Vision projection (image input)

Base Model

Upstream parent: unsloth/Muse-Glimmer-30B-GGUF (from meta-models/Muse-Glimmer-30B, Apache-2.0)
Quantization: TurboQuant TQ3_4S (four-scale turbo quant) with out6k recipe — output and embedding tensors preserved at q6_K precision
Native context: 131,072 tokens

Recommended Runtime

Text-only:

./build/bin/llama-server \
  -m Muse-Glimmer-30B-TQ3_4S.gguf \
  --host 127.0.0.1 --port 8080 \
  -c 32768 -np 1 -ngl 99 -fa on \
  --reasoning-format deepseek --jinja

With vision (mmproj):

./build/bin/llama-server \
  -m Muse-Glimmer-30B-TQ3_4S.gguf \
  --mmproj mmproj-Muse-Glimmer-30B-Q8_0.gguf \
  --host 127.0.0.1 --port 8080 \
  -c 32768 -np 1 -ngl 99 -fa on \
  --reasoning-format deepseek --jinja

Optional — DFlash speculative decoding (raises decode ~20%):

# separate drafter model required
--spec-type draft-dflash -md <drafter>.gguf --spec-draft-n-max 3

Benchmarks

Measured on NVIDIA RTX 3090 24 GB, turbo-tan/llama.cpp-tq3 build f755f1ac1, thinking ON, temperature 0.

Evalplus (official scorer)

Benchmark	pass@1
HumanEval	93.3
HumanEval+	89.0
MBPP	89.7
MBPP+	74.6

Hard86

Benchmark	Result
Hard86	74/86 (86.0%)

Task suites (task breakdown)

Suite	Score	Pass rate
instructfollow	96.7	14/15
coding	87.5	10/12
dataextract	82.8	9/15
reasonmath	80.0	12/15
toolcall	80.0	11/15
speed	70.8	9/9

Speed

Config	Result
llama-bench pp2048	1,155 tok/s
llama-bench tg128	43.3 tok/s
Decode, 8K context, no drafter	44.6 tok/s
Decode, 8K context, DFlash drafter (n_max=3)	53.7 tok/s (+20%)

Generation throughput during task suites: 47.9 tok/s.

Validation

Strict server smoke (--reasoning off): content exactly ok ✅
Drafter signature verified in server logs: block_size=16, mask_token_id=201818, n_extract=5