Instructions to use meta-models/Muse-Glimmer-30B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use meta-models/Muse-Glimmer-30B-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 meta-models/Muse-Glimmer-30B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf meta-models/Muse-Glimmer-30B-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 meta-models/Muse-Glimmer-30B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf meta-models/Muse-Glimmer-30B-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 meta-models/Muse-Glimmer-30B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf meta-models/Muse-Glimmer-30B-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 meta-models/Muse-Glimmer-30B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf meta-models/Muse-Glimmer-30B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/meta-models/Muse-Glimmer-30B-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use meta-models/Muse-Glimmer-30B-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "meta-models/Muse-Glimmer-30B-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": "meta-models/Muse-Glimmer-30B-GGUF", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/meta-models/Muse-Glimmer-30B-GGUF:Q4_K_M
- Ollama
How to use meta-models/Muse-Glimmer-30B-GGUF with Ollama:
ollama run hf.co/meta-models/Muse-Glimmer-30B-GGUF:Q4_K_M
- Unsloth Desktop
- Pi
How to use meta-models/Muse-Glimmer-30B-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf meta-models/Muse-Glimmer-30B-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": "meta-models/Muse-Glimmer-30B-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use meta-models/Muse-Glimmer-30B-GGUF with Docker Model Runner:
docker model run hf.co/meta-models/Muse-Glimmer-30B-GGUF:Q4_K_M
- Lemonade
How to use meta-models/Muse-Glimmer-30B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull meta-models/Muse-Glimmer-30B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Muse-Glimmer-30B-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use meta-models/Muse-Glimmer-30B-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 meta-models/Muse-Glimmer-30B-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 meta-models/Muse-Glimmer-30B-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use meta-models/Muse-Glimmer-30B-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf meta-models/Muse-Glimmer-30B-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 "meta-models/Muse-Glimmer-30B-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"
Jinja issue with double context size
This jinja file will be helpfull for those who dare launch muse on llama.cpp with claude code.
The problem this chat template solves - double context size with the initial prompt, in my case it reduces "twice" from 89k tokens to 48k tokens
I've tested on v2.1.227 (But it's not exactly this version issue)
- Claude Code (the client) sends the request with two system blocks by design: the top-level system field plus a role=system message in the middle of the history. This is its internal format β it looks exactly like that without llama-swap (your capture is precisely what CC itself produced).
- The official Muse ATEM chat template (embedded in the GGUF) injects the tool schemas after every system message. Two system messages β the schemas appear twice.
Solution:
Render the tool schemas and recipient list once, on the first system message, instead of after every system message.
Result: first prompt 89,282 into ~ 48k tokens (~42%);
Emojify this post if it works for you too π€
{#
Template: Muse Glimmer ATEM Chat Template (patched: tool definitions + recipients
rendered exactly once, on the first system message, instead of after every
system message; behavior for single-system/no-system requests is byte-identical
to the official template)
Renders the ATEM tool-calling protocol: reasoning channel (to=self), tool
channels (to=<tool>), and the user channel, plus tool definitions and the
valid-recipient list in the system block.
Whitespace note: every tag uses the {%- -%} / {{- -}} stripping markers, so
the indentation below is purely for readability and contributes nothing to
the rendered output.
#}
{%- macro render_content(content) -%}
{%- if content is string -%}
{{- content -}}
{%- elif content is not none -%}
{%- for part in content -%}
{%- if part['type'] == 'image' -%}
{{- '<|patch|>' -}}
{%- elif part['type'] == 'video' -%}
{{- '<|video|>' -}}
{%- elif part['type'] == 'text' -%}
{{- part['text'] -}}
{%- endif -%}
{%- endfor -%}
{%- endif -%}
{%- endmacro -%}
{%- macro render_atem(tc) -%}
{%- set args = tc.function.arguments -%}
{%- if args is not mapping -%}
{{- raise_exception('Muse Glimmer ATEM chat template requires tool_call.function.arguments to be a dict (mapping); a JSON string cannot be parsed in the HF jinja sandbox.') -}}
{%- endif -%}
{{- '<atem:function_calls>\n<atem:invoke name="' + tc.function.name + '">\n' -}}
{%- for k, v in args.items() -%}
{{- '<atem:parameter name="' + k + '">' -}}
{%- if v is boolean -%}
{%- if v -%}
true
{%- else -%}
false
{%- endif -%}
{%- elif v is none -%}
null
{%- elif v is mapping or (v is iterable and v is not string) -%}
{{- v | tojson -}}
{%- else -%}
{{- v -}}
{%- endif -%}
{{- '</atem:parameter>\n' -}}
{%- endfor -%}
{{- '</atem:invoke>\n</atem:function_calls>' -}}
{%- endmacro -%}
{%- macro render_tool_defs(tools) -%}
{{- 'In this environment you have access to a set of tools you can use to answer the user\'s question.\n\n' -}}
{{- 'You can invoke a function by writing a "<atem:function_calls>" block like the following:\n' -}}
{{- '<atem:function_calls>\n<atem:invoke name="$FUNCTION_NAME">\n<atem:parameter name="$PARAMETER_NAME">$PARAMETER_VALUE</atem:parameter>\n...\n</atem:invoke>\n</atem:function_calls>\n\n' -}}
{{- 'String and scalar parameters should be specified as is, while lists and objects should use JSON format. Note that spaces for string values are not stripped. The output is not expected to be valid XML and is parsed with regular expressions.\n' -}}
{{- 'Here are the functions available in JSONSchema format:\n' -}}
{{- '// Tool metadata\n' -}}
{%- set nsns = namespace(seen=[]) -%}
{%- for tool in tools -%}
{%- set fn = tool.function if tool.function is defined else tool -%}
{%- set tns = fn.name.split('.')[0] -%}
{%- if tns not in nsns.seen -%}
{%- set nsns.seen = nsns.seen + [tns] -%}
{%- endif -%}
{%- endfor -%}
{%- set nd = tool_namespace_descriptions if tool_namespace_descriptions is defined else {} -%}
{%- for tns in nsns.seen -%}
{{- '{"name": ' + (tns | tojson) + ', "description": ' + ((nd[tns] if tns in nd else '') | tojson) + '}\n' -}}
{%- endfor -%}
{{- '// Function schemas' -}}
{%- for tool in tools -%}
{%- set fn = tool.function if tool.function is defined else tool -%}
{{- '\n{"name": ' + (fn.name | tojson) + ', "description": ' + (fn.description | tojson) + ', "parameters": ' + (fn.parameters | tojson) + '}' -}}
{%- endfor -%}
{{- '\n\nHere\'s an example of how to call a function in the tool set:\n' -}}
{{- '(If the tool namespace is not specified, invoke the function directly as `example_function_name` rather than `example_tool_name.example_function_name`)\n\n' -}}
{{- 'to=example_tool_name.example_function_name\n\n' -}}
{{- '<atem:function_calls>\n<atem:invoke name="example_tool_name.example_function_name">\n' -}}
{{- '<atem:parameter name="example_parameter_1">value_1</atem:parameter>\n' -}}
{{- '<atem:parameter name="example_parameter_2">This is the value for the second parameter\nthat can span\n"multiple" lines\n</atem:parameter>\n' -}}
{{- '</atem:invoke>\n</atem:function_calls>' -}}
{%- endmacro -%}
{%- macro render_reasoning() -%}
{%- set rs = reasoning_strength if reasoning_strength is defined and reasoning_strength else 'high' -%}
{{- 'Reasoning strength: ' + rs + '.' -}}
{%- endmacro -%}
{%- macro render_system_meta(tools) -%}
{%- set rns = namespace(recipients=['"self"'], nslist=[]) -%}
{%- if tools -%}
{%- for tool in tools -%}
{%- set fn = tool.function if tool.function is defined else tool -%}
{%- set tns = fn.name.split('.')[0] -%}
{%- if tns not in rns.nslist -%}
{%- set rns.nslist = rns.nslist + [tns] -%}
{%- endif -%}
{%- endfor -%}
{%- for tns in rns.nslist -%}
{%- set rns.recipients = rns.recipients + ['"' + tns + '.*"'] -%}
{%- endfor -%}
{%- endif -%}
{%- set rns.recipients = rns.recipients + ['"user"'] -%}
{{- '# Valid recipients: ' + rns.recipients | join(', ') + '.' -}}
{%- endmacro -%}
{{- bos_token -}}
{%- set ns = namespace(has_system=false) -%}
{%- set tdefs = namespace(rendered=false) -%}
{%- for m in messages -%}
{%- if m['role'] == 'system' -%}
{%- set ns.has_system = true -%}
{%- endif -%}
{%- endfor -%}
{%- if not ns.has_system -%}
{{- '<|start|>system<|message|>You are a helpful AI assistant.' -}}
{%- set kc = knowledge_cutoff if knowledge_cutoff is defined and knowledge_cutoff else '2026-01-04' -%}
{{- '\nKnowledge cutoff: ' + kc + '.' -}}
{%- if current_date is defined and current_date -%}
{{- '\nCurrent date: ' + current_date + '.' -}}
{%- elif strftime_now is defined -%}
{{- '\nCurrent date: ' + strftime_now('%Y-%m-%d') + '.' -}}
{%- endif -%}
{{- '\n\n' -}}
{{- render_reasoning() -}}
{%- if tools -%}
{%- if not tdefs.rendered -%}
{%- set tdefs.rendered = true -%}
{{- '\n\n' -}}
{{- render_tool_defs(tools) -}}
{{- '\n\n' -}}
{{- render_system_meta(tools) -}}
{%- endif -%}
{%- else -%}
{{- '\n\n' -}}
{{- render_system_meta(tools) -}}
{%- endif -%}
{{- '<|eot|>' -}}
{%- endif -%}
{%- for message in messages -%}
{%- set role = message['role'] -%}
{%- set end_token = '<|eom|>' if (not loop.last and messages[loop.index0 + 1]['role'] == role) else '<|eot|>' -%}
{%- if role == 'system' -%}
{#- Callers sometimes write the directive into the system prompt themselves.
Normalise "Reasoning effort" to "Reasoning strength" (jinja has no
case-insensitive replace, hence the four realistic casings), then skip
the kwarg-driven line below if the prompt already carries one. -#}
{%- set sys_text = render_content(message['content'])
| replace('Reasoning effort', 'Reasoning strength')
| replace('Reasoning Effort', 'Reasoning Strength')
| replace('reasoning effort', 'reasoning strength')
| replace('REASONING EFFORT', 'REASONING STRENGTH') -%}
{{- '<|start|>system<|message|>' -}}
{{- sys_text -}}
{%- if 'reasoning strength' not in (sys_text | lower) -%}
{{- '\n\n' -}}
{{- render_reasoning() -}}
{%- endif -%}
{%- if tools -%}
{%- if not tdefs.rendered -%}
{%- set tdefs.rendered = true -%}
{{- '\n\n' -}}
{{- render_tool_defs(tools) -}}
{{- '\n\n' -}}
{{- render_system_meta(tools) -}}
{%- endif -%}
{%- else -%}
{{- '\n\n' -}}
{{- render_system_meta(tools) -}}
{%- endif -%}
{{- '<|eot|>' -}}
{%- elif role == 'user' -%}
{{- '<|start|>user<|message|>' -}}
{{- render_content(message['content']) -}}
{{- '<|eot|>' -}}
{%- elif role == 'tool' -%}
{%- set tname = message.get('name') -%}
{%- if not tname -%}
{%- set tcid = message.get('tool_call_id') -%}
{%- set rns = namespace(name=tcid if tcid else '') -%}
{%- for m in messages -%}
{%- if m.get('tool_calls') -%}
{%- for tc in m['tool_calls'] -%}
{%- if tcid is not none and tc.id is defined and tc.id == tcid -%}
{%- set rns.name = tc.function.name -%}
{%- endif -%}
{%- endfor -%}
{%- endif -%}
{%- endfor -%}
{%- set tname = rns.name -%}
{%- endif -%}
{{- '<|start|>tool ' + tname + '<|message|><tool_output name="' + tname + '">\n' -}}
{{- render_content(message['content']) -}}
{{- '\n</tool_output><|eot|>' -}}
{%- elif role == 'assistant' -%}
{%- if message.get('reasoning_content') -%}
{{- '<|start|>assistant to=self<|message|>' + message['reasoning_content'] + '<|eom|>' -}}
{%- endif -%}
{%- if message.get('tool_calls') -%}
{%- for tc in message['tool_calls'] -%}
{{- '<|start|>assistant to=' + tc.function.name + '<|message|>' -}}
{{- render_atem(tc) -}}
{%- if loop.last -%}
{{- end_token -}}
{%- else -%}
{{- '<|eom|>' -}}
{%- endif -%}
{%- endfor -%}
{%- else -%}
{%- set recipient = message.get('recipient') or 'user' -%}
{%- set end_turn = message.get('end_turn') -%}
{%- if end_turn is none -%}
{%- set end_turn = not (recipient and recipient != 'user') -%}
{%- endif -%}
{{- '<|start|>assistant' -}}
{%- if recipient -%}
{{- ' to=' + recipient -}}
{%- endif -%}
{{- '<|message|>' -}}
{{- render_content(message['content']) -}}
{{- ('<|eot|>' if end_turn else '<|eom|>') -}}
{%- endif -%}
{%- endif -%}
{%- endfor -%}
{%- if add_generation_prompt -%}
{{- '<|start|>assistant' -}}
{%- endif -%}