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

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf staccs/Muse-Glimmer-30B-lecore-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf staccs/Muse-Glimmer-30B-lecore-GGUF:Q4_K_M

Use Docker

docker model run hf.co/staccs/Muse-Glimmer-30B-lecore-GGUF:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use staccs/Muse-Glimmer-30B-lecore-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "staccs/Muse-Glimmer-30B-lecore-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": "staccs/Muse-Glimmer-30B-lecore-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/staccs/Muse-Glimmer-30B-lecore-GGUF:Q4_K_M

Ollama
How to use staccs/Muse-Glimmer-30B-lecore-GGUF with Ollama:
```
ollama run hf.co/staccs/Muse-Glimmer-30B-lecore-GGUF:Q4_K_M
```
Unsloth Desktop

How to use staccs/Muse-Glimmer-30B-lecore-GGUF with Pi:

Start the llama.cpp server

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

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "staccs/Muse-Glimmer-30B-lecore-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use staccs/Muse-Glimmer-30B-lecore-GGUF with Docker Model Runner:
```
docker model run hf.co/staccs/Muse-Glimmer-30B-lecore-GGUF:Q4_K_M
```

Lemonade

How to use staccs/Muse-Glimmer-30B-lecore-GGUF with Lemonade:

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Hermes Agent

How to use staccs/Muse-Glimmer-30B-lecore-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 staccs/Muse-Glimmer-30B-lecore-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 staccs/Muse-Glimmer-30B-lecore-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use staccs/Muse-Glimmer-30B-lecore-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf staccs/Muse-Glimmer-30B-lecore-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 "staccs/Muse-Glimmer-30B-lecore-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"

Muse-Glimmer-30B-lecore-GGUF

GGUF builds of meta-models/Muse-Glimmer-30B for llama.cpp, Ollama and LM Studio. These are companions to the MLX export staccs/Muse-Glimmer-30B-lecore-mlx, which has the measured perplexity numbers.

File	Size	What it is
`Muse-Glimmer-30B-Q4_K_M.gguf`	16.94 GB	stock llama.cpp Q4_K_M
`Muse-Glimmer-30B-lecore-mix.gguf`	15.84 GB	leCore per-tensor mix: each linear layer gets the GGUF K-quant matching the bits leCore measured for it (2→Q2_K, 3→Q3_K, 4→Q4_K, 8→Q8_0); embeddings and output follow the MLX export
`Muse-Glimmer-30B-mmproj-f16.gguf`	3.85 GB	stock llama.cpp Q4_K_M

The lecore-mix file is quantized from the original bf16 weights with llama.cpp K-quants. The bit width of each tensor follows leCore's measured (KL-damage) allocation, so its numbers are close to, but not identical to, the MLX export. Some tensor widths are not multiples of 256, so llama.cpp may fall back to a compatible type for those tensors.

Usage

Ollama: download a file, then

printf 'FROM ./Muse-Glimmer-30B-Q4_K_M.gguf
' > Modelfile && ollama create muse-glimmer-30b-lecore-gguf -f Modelfile && ollama run muse-glimmer-30b-lecore-gguf

or pull straight from the Hub with ollama run hf.co/staccs/Muse-Glimmer-30B-lecore-GGUF:Q4_K_M

LM Studio: search for staccs/Muse-Glimmer-30B-lecore-GGUF in the model browser and pick a file. On a 36 GB Mac, the lecore-mix file is the one that fits.

llama.cpp

llama-cli -hf staccs/Muse-Glimmer-30B-lecore-GGUF -p "Hello" -n 256

Benchmarks (lm-eval-harness 0.4.9, run on the shipped weights dequantized to bf16 on GPU)

MMLU 5-shot on the first 10 questions of each of the 57 subjects; GSM8K 5-shot on the first 100 test problems (greedy, 320 new tokens, no chat template). These are subsets, so expect about ±2–4 points of sampling noise.

Model	MMLU (5-shot)	GSM8K strict	GSM8K flexible
leCore mix (this repo)	81.8%	96.0%	96.0%
stock 3-bit affine, group 64 (simulated)	78.6%	90.0%	90.0%

License

Base model: meta-models/Muse-Glimmer-30B, released under the Apache License 2.0. Only quantization was done here, with no retraining. Vision: this MLX export is text-only, because mlx-lm's Muse-Glimmer port has no vision tower. The companion GGUF repo includes an unquantized f16 mmproj vision projector for llama.cpp image input, if its conversion succeeded. Perplexity and benchmarks below are text-only.

Related builds

Same base model, other formats in the lecore set:

repo	format	size
Muse-Glimmer-30B-lecore-mlx	MLX (Apple Silicon)	16.04 GB

Downloads last month: 742

GGUF

Model size

28B params

Architecture

muse-glimmer

Hardware compatibility

4-bit

View +1 variant

Model tree for staccs/Muse-Glimmer-30B-lecore-GGUF

Base model

meta-models/Muse-Glimmer-30B

Quantized

(199)

this model