Instructions to use RadixArk/Muse-Glimmer-q4-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RadixArk/Muse-Glimmer-q4-MLX with MLX:

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

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("RadixArk/Muse-Glimmer-q4-MLX")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use RadixArk/Muse-Glimmer-q4-MLX with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "RadixArk/Muse-Glimmer-q4-MLX"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "RadixArk/Muse-Glimmer-q4-MLX"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use RadixArk/Muse-Glimmer-q4-MLX with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "RadixArk/Muse-Glimmer-q4-MLX"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "RadixArk/Muse-Glimmer-q4-MLX"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "RadixArk/Muse-Glimmer-q4-MLX",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use RadixArk/Muse-Glimmer-q4-MLX 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 "RadixArk/Muse-Glimmer-q4-MLX"

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 RadixArk/Muse-Glimmer-q4-MLX

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use RadixArk/Muse-Glimmer-q4-MLX with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "RadixArk/Muse-Glimmer-q4-MLX"

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 "RadixArk/Muse-Glimmer-q4-MLX" \
  --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"

Sync chat template with meta-models/Muse-Glimmer-30B (fixes duplicate reasoning effort)

by Jiminator - opened 5 days ago

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+212

-2

Jiminator

RadixArk org 5 days ago

This repo ships a stale copy of the Muse Glimmer chat template (7,175 bytes,
md5 6dba2759). Upstream fixed it in
meta-models/Muse-Glimmer-30B#35;
the current template is 9,992 bytes, md5 216c1d8e. This copies it verbatim.

Why this matters

The stale template appends its own reasoning directive unconditionally. If the
caller's system prompt already specifies one, the model receives two contradictory
instructions and the appended one wins:

system prompt	stale template renders	current template renders
`Reasoning effort: low.`	`Reasoning effort: low.` + `Reasoning strength: high.`	`Reasoning strength: low.`
`Reasoning strength: low.`	`Reasoning strength: low.` + `Reasoning strength: high.`	`Reasoning strength: low.`
(none)	`Reasoning strength: high.`	identical

The current template normalises "Reasoning effort" to "Reasoning strength" across
casings and only appends when the system prompt has no directive of its own.

Measured impact

Same prompt, same weights, greedy, on Muse-Glimmer-q4km-gs128-MLX via mlx-lm on
an M5 Pro, system prompt asking for low reasoning effort:

	stale template	current template
reasoning characters	160	102
total generated	225	167
wall clock	7.5 s	3.0 s
answer	correct	correct

So a user asking for low effort gets 57% more reasoning and 2.5x the latency for
an identical answer. It only manifests when the system prompt mentions reasoning
effort, which is why a default render is byte-identical and this went unnoticed.

Verification

No inference-code change is needed for the new template: serving
meta-models/Muse-Glimmer-30B (which already carries it) on stock upstream SGLang
passes the full tool-call suite 5/5 and splits reasoning correctly. The delta
between the two templates is mostly a minified-to-pretty-printed reformat plus the
one conditional above; none of the channel markers SGLang parses changed.

Sync chat template with meta-models/Muse-Glimmer-30B (fixes duplicate reasoning effort)aa79704c

Jiminator changed pull request status to merged 5 days ago

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment