Instructions to use Krasnopjorovs/Muse-Glimmer-30B-Imatrix-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 Krasnopjorovs/Muse-Glimmer-30B-Imatrix-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 Krasnopjorovs/Muse-Glimmer-30B-Imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Krasnopjorovs/Muse-Glimmer-30B-Imatrix-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 Krasnopjorovs/Muse-Glimmer-30B-Imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Krasnopjorovs/Muse-Glimmer-30B-Imatrix-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 Krasnopjorovs/Muse-Glimmer-30B-Imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Krasnopjorovs/Muse-Glimmer-30B-Imatrix-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 Krasnopjorovs/Muse-Glimmer-30B-Imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Krasnopjorovs/Muse-Glimmer-30B-Imatrix-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Krasnopjorovs/Muse-Glimmer-30B-Imatrix-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Krasnopjorovs/Muse-Glimmer-30B-Imatrix-GGUF with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Krasnopjorovs/Muse-Glimmer-30B-Imatrix-GGUF:Q4_K_M

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

How to use Krasnopjorovs/Muse-Glimmer-30B-Imatrix-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Krasnopjorovs/Muse-Glimmer-30B-Imatrix-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": "Krasnopjorovs/Muse-Glimmer-30B-Imatrix-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

How to use Krasnopjorovs/Muse-Glimmer-30B-Imatrix-GGUF with Lemonade:

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Hermes Agent

How to use Krasnopjorovs/Muse-Glimmer-30B-Imatrix-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 Krasnopjorovs/Muse-Glimmer-30B-Imatrix-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 Krasnopjorovs/Muse-Glimmer-30B-Imatrix-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Krasnopjorovs/Muse-Glimmer-30B-Imatrix-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Krasnopjorovs/Muse-Glimmer-30B-Imatrix-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 "Krasnopjorovs/Muse-Glimmer-30B-Imatrix-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 — imatrix GGUF quantizations

GGUF imatrix builds of meta-models/Muse-Glimmer-30B.

Quantized with llama.cpp version: 10358 (030ebb558) using importance-matrix calibration on a public multilingual + code + math corpus.

Prompt format

<|start|>system<|message|>{system}<|eot|><|start|>user<|message|>{prompt}<|eot|><|start|>assistant

Requires a recent llama.cpp build. The muse-glimmer architecture landed in PR #26841. Older builds will refuse to load these files. Run the server with --jinja, otherwise the model's reasoning channel leaks into content instead of reasoning_content.

Verified on this release

Tool calling was exercised against the Q6_K build via llama-server --jinja: finish_reason: tool_calls, empty content, valid JSON arguments, and reasoning correctly separated into reasoning_content. The loader prints special_eot_id is not in special_eog_ids — this is harmless here, generation stops cleanly at end of turn.

What these files are not

Text only. The vision projector (mmproj) is not included. For multimodal use, take the official meta-models/Muse-Glimmer-30B-GGUF.
No DFlash drafter. Meta's block-diffusion drafter gives a large decode speedup and ships in the official repo. Pair it with these weights using --spec-type draft-dflash --spec-draft-n-max 15.

The importance matrix is published separately at Krasnopjorovs/Imatrices. It was computed over 2148 chunks at -c 512 on a single 72 GB card in about 90 minutes; on CPU the same run takes a day or more. Drop it into llama-quantize --imatrix and build any quant type you want without repeating the calibration pass. Neither Meta nor Unsloth ship theirs.

Apache 2.0, with Meta's separate USAGE_POLICY.md also applying.

Available quants

Filename	Quant	Size (GiB)	Description
Muse-Glimmer-30B-Q8_0.gguf	`Q8_0`	27.58 GB	Practically lossless. Closest to source with significant size cut.
Muse-Glimmer-30B-Q6_K_L.gguf	`Q6_K`	21.90 GB	Q6_K with Q8_0 embed/output tensors. Near-lossless top tier.
Muse-Glimmer-30B-Q6_K.gguf	`Q6_K`	21.30 GB	Near-lossless quality. Recommended for highest practical fidelity.
Muse-Glimmer-30B-Q5_K_L.gguf	`Q5_K_M`	19.22 GB	Q5_K_M with Q8_0 embed/output. High quality with small overhead.
Muse-Glimmer-30B-Q5_K_M.gguf	`Q5_K_M`	18.45 GB	High quality, balanced size. Recommended general-purpose.
Muse-Glimmer-30B-Q5_K_S.gguf	`Q5_K_S`	18.02 GB	Slightly smaller than Q5_K_M with similar quality.
Muse-Glimmer-30B-Q4_K_L.gguf	`Q4_K_M`	16.70 GB	Q4_K_M with Q8_0 embed/output. Sweet spot of quality and size.
Muse-Glimmer-30B-Q4_K_M.gguf	`Q4_K_M`	15.77 GB	Best size/quality tradeoff. Recommended default.
Muse-Glimmer-30B-Q4_K_S.gguf	`Q4_K_S`	15.03 GB	Compact with minor quality loss versus Q4_K_M.
Muse-Glimmer-30B-IQ4_NL.gguf	`IQ4_NL`	14.94 GB	Slightly larger than IQ4_XS. Online repacking for ARM CPU inference.
Muse-Glimmer-30B-IQ4_XS.gguf	`IQ4_XS`	14.17 GB	Most efficient sub-Q4. Smaller than Q4_K_S with comparable quality.

Calibration

Imatrix generated from reapmix (community calibration mix) — ~400K tokens — multilingual + code + math. This is the same class of public calibration data used by other community GGUF publishers; no claim of unique calibration is made for this release.

*_L and *_XL variants override the output tensor and/or token embedding to Q8_0 (versus the base type), at small extra disk for typically improved output stability at low bit-rates.

Download

Single file:

hf download Krasnopjorovs/Muse-Glimmer-30B-Imatrix-GGUF --include "Muse-Glimmer-30B-Q4_K_M.gguf" --local-dir .

Whole repo:

hf download Krasnopjorovs/Muse-Glimmer-30B-Imatrix-GGUF --local-dir ./Muse-Glimmer-30B-gguf

Run

./llama-server -m Muse-Glimmer-30B-Q4_K_M.gguf -c 32768 -ngl 99 --host 0.0.0.0 --port 8080

Picking a quant

Q8_0 / Q6_K_L — RAM headroom, want ceiling quality
Q5_K_M / Q4_K_L — workstation default, very small quality loss
Q4_K_M — best general size/quality tradeoff, the default choice
Q4_K_S / IQ4_NL — tighter budgets; IQ4_NL repacks for ARM CPUs
IQ4_XS — smallest here, fits a 16 GB card with context to spare

Build info

llama.cpp release: version: 10358 (030ebb558)
Generated: 2026-08-11T06:03:59

Credits

Original model by meta-models
Calibration: reapmix (community calibration mix)
llama.cpp by ggerganov and contributors

Downloads last month: 603

GGUF

Model size

28B params

Architecture

muse-glimmer

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

Model tree for Krasnopjorovs/Muse-Glimmer-30B-Imatrix-GGUF

Base model

meta-models/Muse-Glimmer-30B

Quantized

(176)

this model