Instructions to use ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni", filename="martha_9b_v2_F16.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni 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 ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni:Q4_K_M_LATEST # Run inference directly in the terminal: llama cli -hf ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni:Q4_K_M_LATEST
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni:Q4_K_M_LATEST # Run inference directly in the terminal: llama cli -hf ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni:Q4_K_M_LATEST
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 ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni:Q4_K_M_LATEST # Run inference directly in the terminal: ./llama-cli -hf ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni:Q4_K_M_LATEST
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 ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni:Q4_K_M_LATEST # Run inference directly in the terminal: ./build/bin/llama-cli -hf ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni:Q4_K_M_LATEST
Use Docker
docker model run hf.co/ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni:Q4_K_M_LATEST
- LM Studio
- Jan
- Ollama
How to use ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni with Ollama:
ollama run hf.co/ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni:Q4_K_M_LATEST
- Unsloth Studio
How to use ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni to start chatting
- Pi
How to use ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni:Q4_K_M_LATEST
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni:Q4_K_M_LATEST" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni:Q4_K_M_LATEST
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 ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni:Q4_K_M_LATEST
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni:Q4_K_M_LATEST
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 "ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni:Q4_K_M_LATEST" \ --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"
- Docker Model Runner
How to use ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni with Docker Model Runner:
docker model run hf.co/ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni:Q4_K_M_LATEST
- Lemonade
How to use ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni:Q4_K_M_LATEST
Run and chat with the model
lemonade run user.Miss_MARTHA-9B-Qwen3.5-Omni-Q4_K_M_LATEST
List all available models
lemonade list
File size: 7,756 Bytes
a3c8e9a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 | {%- set image_count = namespace(value=0) %}
{%- set video_count = namespace(value=0) %}
{%- macro render_content(content, do_vision_count, is_system_content=false) %}
{%- if content is string %}
{{- content }}
{%- elif content is iterable and content is not mapping %}
{%- for item in content %}
{%- if 'image' in item or 'image_url' in item or item.type == 'image' %}
{%- if is_system_content %}
{{- raise_exception('System message cannot contain images.') }}
{%- endif %}
{%- if do_vision_count %}
{%- set image_count.value = image_count.value + 1 %}
{%- endif %}
{%- if add_vision_id %}
{{- 'Picture ' ~ image_count.value ~ ': ' }}
{%- endif %}
{{- '<|vision_start|><|image_pad|><|vision_end|>' }}
{%- elif 'video' in item or item.type == 'video' %}
{%- if is_system_content %}
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{%- endif %}
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{{- item.text }}
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{{- '' }}
{%- else %}
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{%- endmacro %}
{%- if not messages %}
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{%- endif %}
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{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
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{%- set content = render_content(message.content, false)|trim %}
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{%- set ns.multi_step_tool = false %}
{%- set ns.last_query_index = index %}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if ns.multi_step_tool %}
{{- raise_exception('No user query found in messages.') }}
{%- endif %}
{%- for message in messages %}
{%- set content = render_content(message.content, true)|trim %}
{%- if message.role == "system" %}
{%- if not loop.first %}
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{%- elif message.role == "user" %}
{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{%- set reasoning_content = '' %}
{%- if message.reasoning_content is string %}
{%- set reasoning_content = message.reasoning_content %}
{%- else %}
{%- if '</think>' in content %}
{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
{%- set content = content.split('</think>')[-1].lstrip('\n') %}
{%- endif %}
{%- endif %}
{%- set reasoning_content = reasoning_content|trim %}
{%- if loop.index0 > ns.last_query_index %}
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content + '\n</think>\n\n' + content }}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- if message.tool_calls and message.tool_calls is iterable and message.tool_calls is not mapping %}
{%- for tool_call in message.tool_calls %}
{%- if tool_call.function is defined %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{%- if loop.first %}
{%- if content|trim %}
{{- '\n\n<tool_call>\n<function=' + tool_call.name + '>\n' }}
{%- else %}
{{- '<tool_call>\n<function=' + tool_call.name + '>\n' }}
{%- endif %}
{%- else %}
{{- '\n<tool_call>\n<function=' + tool_call.name + '>\n' }}
{%- endif %}
{%- if tool_call.arguments is defined %}
{%- for args_name, args_value in tool_call.arguments|items %}
{{- '<parameter=' + args_name + '>\n' }}
{%- set args_value = args_value | tojson | safe if args_value is mapping or (args_value is sequence and args_value is not string) else args_value | string %}
{{- args_value }}
{{- '\n</parameter>\n' }}
{%- endfor %}
{%- endif %}
{{- '</function>\n</tool_call>' }}
{%- endfor %}
{%- endif %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if loop.previtem and loop.previtem.role != "tool" %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- content }}
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{%- if not loop.last and loop.nextitem.role != "tool" %}
{{- '<|im_end|>\n' }}
{%- elif loop.last %}
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{%- endif %}
{%- else %}
{{- raise_exception('Unexpected message role.') }}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- if enable_thinking is defined and enable_thinking is false %}
{{- '<think>\n\n</think>\n\n' }}
{%- else %}
{{- '<think>\n' }}
{%- endif %}
{%- endif %} |