Instructions to use bsisduck/Muse-Glimmer-30B-MLX-MXFP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bsisduck/Muse-Glimmer-30B-MLX-MXFP8 with MLX:

# Make sure mlx-vlm is installed
# pip install --upgrade mlx-vlm

from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config

# Load the model
model, processor = load("bsisduck/Muse-Glimmer-30B-MLX-MXFP8")
config = load_config("bsisduck/Muse-Glimmer-30B-MLX-MXFP8")

# Prepare input
image = ["http://images.cocodataset.org/val2017/000000039769.jpg"]
prompt = "Describe this image."

# Apply chat template
formatted_prompt = apply_chat_template(
    processor, config, prompt, num_images=1
)

# Generate output
output = generate(model, processor, formatted_prompt, image)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use bsisduck/Muse-Glimmer-30B-MLX-MXFP8 with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "bsisduck/Muse-Glimmer-30B-MLX-MXFP8"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "bsisduck/Muse-Glimmer-30B-MLX-MXFP8"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use bsisduck/Muse-Glimmer-30B-MLX-MXFP8 with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "bsisduck/Muse-Glimmer-30B-MLX-MXFP8"

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 bsisduck/Muse-Glimmer-30B-MLX-MXFP8

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use bsisduck/Muse-Glimmer-30B-MLX-MXFP8 with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "bsisduck/Muse-Glimmer-30B-MLX-MXFP8"

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 "bsisduck/Muse-Glimmer-30B-MLX-MXFP8" \
  --custom-provider-id mlx-lm \
  --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 MLX MXFP8

Staging status: tensor integrity is verified, but inference did not complete on the tested 32 GB host because Metal ran out of memory. This artifact is prepared for validation on a higher-memory Apple Silicon host and is not release-ready yet.

An MLX-VLM MXFP8 conversion of meta-models/Muse-Glimmer-30B, developed by Meta Superintelligence Lab. The conversion is pinned to source revision 97c77dff50b2797bcc558fa2d909761dbc575c59.

Model details

Property	Value
Architecture	Dense causal transformer with a dedicated perception encoder
Parameters	Approximately 29.8B total, including an approximately 1.8B vision encoder
Modalities	Text and image input; text output
Context length	131,072+ architecture limit documented upstream; not validated here
Quantization	RTN `mxfp8`, 8-bit, group size 32
Quantization scope	Eligible language and bridge linear layers; `vision_tower` remains unquantized
Artifact size	Approximately 32.53 GB
Release status	Staging; higher-memory inference validation required

Requirements for the next test

Apple Silicon with 64 GB or more unified memory is the next recommended validation target. This is a test recommendation, not a verified minimum.
Approximately 34 GB of disk space for the model, plus runtime and swap space.
macOS, Python 3.13, and the pinned MLX-VLM runtime below.

pip install "mlx-vlm @ git+https://github.com/Blaizzy/mlx-vlm.git@132fc5dea46a085f39400cb101d1ad7c1dabdd49"

Test commands

Text

mlx_vlm.generate \
  --model bsisduck/Muse-Glimmer-30B-MLX-MXFP8 \
  --prompt "Return only the result of 84 * 3 / 2." \
  --max-tokens 48 --temperature 0

Image

mlx_vlm.generate \
  --model bsisduck/Muse-Glimmer-30B-MLX-MXFP8 \
  --image ./image.jpg \
  --prompt "Describe this image and identify the important details." \
  --max-tokens 128 --temperature 0

Validation status

Check	Result
Conversion	Passed; bounded 2 GiB serialization, 30.06 seconds
Serialized tensor comparison	Passed; 1857/1857 tensors exactly match the pinned MXFP8 reference
Local-to-remote transfer	Passed; 24 files match SHA-256 manifest digest `65ec5c8d4e12879e397f9f662cb6410360b5a4e4ade24e01b9afd3badeb9e25b`
Text smoke on 32 GB	Not completed; Metal out of memory
Vision smoke on 32 GB	Not started because the text gate did not pass

The failed smoke attempt reported a 30997 MB model requirement against a 25559 MB recommended runtime limit. It does not indicate corrupted weights: the independent tensor audit matched every serialized tensor.

Limitations and safety

No inference-quality, throughput, long-context, or DFlash claim is made for this staging artifact.
Quantization can change model outputs relative to BF16 even when serialized tensors exactly match another MXFP8 conversion.
The model can produce inaccurate, biased, unsafe, or objectionable output.
Read the upstream model card and USAGE_POLICY.md before deployment.

Staging evidence

Downloads last month: 28

Safetensors

Model size

30B params

Tensor type

U32

BF16

MLX

Hardware compatibility

8-bit

Model tree for bsisduck/Muse-Glimmer-30B-MLX-MXFP8

Base model

meta-models/Muse-Glimmer-30B

Quantized

(199)

this model