Instructions to use mach-kernel/ecu-pilot-q4km with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mach-kernel/ecu-pilot-q4km with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="mach-kernel/ecu-pilot-q4km",
	filename="ecu-pilot-q4_k_m.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 mach-kernel/ecu-pilot-q4km 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 mach-kernel/ecu-pilot-q4km:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mach-kernel/ecu-pilot-q4km:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mach-kernel/ecu-pilot-q4km:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mach-kernel/ecu-pilot-q4km: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 mach-kernel/ecu-pilot-q4km:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mach-kernel/ecu-pilot-q4km: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 mach-kernel/ecu-pilot-q4km:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mach-kernel/ecu-pilot-q4km:Q4_K_M

Use Docker

docker model run hf.co/mach-kernel/ecu-pilot-q4km:Q4_K_M

LM Studio
Jan
Ollama
How to use mach-kernel/ecu-pilot-q4km with Ollama:
```
ollama run hf.co/mach-kernel/ecu-pilot-q4km:Q4_K_M
```

Unsloth Studio

How to use mach-kernel/ecu-pilot-q4km 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 mach-kernel/ecu-pilot-q4km 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 mach-kernel/ecu-pilot-q4km to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for mach-kernel/ecu-pilot-q4km to start chatting

How to use mach-kernel/ecu-pilot-q4km with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mach-kernel/ecu-pilot-q4km:Q4_K_M

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": "mach-kernel/ecu-pilot-q4km:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use mach-kernel/ecu-pilot-q4km with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mach-kernel/ecu-pilot-q4km: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 mach-kernel/ecu-pilot-q4km:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use mach-kernel/ecu-pilot-q4km with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mach-kernel/ecu-pilot-q4km: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 "mach-kernel/ecu-pilot-q4km: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"

Docker Model Runner
How to use mach-kernel/ecu-pilot-q4km with Docker Model Runner:
```
docker model run hf.co/mach-kernel/ecu-pilot-q4km:Q4_K_M
```

Lemonade

How to use mach-kernel/ecu-pilot-q4km with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mach-kernel/ecu-pilot-q4km:Q4_K_M

Run and chat with the model

lemonade run user.ecu-pilot-q4km-Q4_K_M

List all available models

lemonade list

ecu-pilot-q4km / Modelfile

mach-kernel

update Modelfile: add Qwen3 ChatML tool-calling template with .Tools block

5dfcffc verified 3 months ago

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1.19 kB

	FROM ./ecu-pilot-q4_k_m.gguf

	TEMPLATE """{{- if .System }}<\|im_start\|>system
	{{ .System }}<\|im_end\|>
	{{ end }}{{- if .Tools }}<\|im_start\|>system

	# Tools

	You are provided with function signatures within <tools></tools> XML tags:
	<tools>
	{{- range .Tools }}
	{"type": "function", "function": {{ .Function }} }
	{{- end }}
	</tools>

	For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
	<tool_call>
	{"name": <function-name>, "arguments": <args-json-object>}
	</tool_call><\|im_end\|>
	{{ end }}{{- range .Messages }}{{- if eq .Role "user" }}<\|im_start\|>user
	{{ .Content }}<\|im_end\|>
	{{ else if eq .Role "assistant" }}<\|im_start\|>assistant
	{{ if .Content }}{{ .Content }}{{ end }}{{- if .ToolCalls }}
	{{- range .ToolCalls }}
	<tool_call>
	{"name": "{{ .Function.Name }}", "arguments": {{ .Function.Arguments }}}
	</tool_call>
	{{- end }}{{ end }}<\|im_end\|>
	{{ else if eq .Role "tool" }}<\|im_start\|>user
	<tool_response>
	{{ .Content }}
	</tool_response><\|im_end\|>
	{{ end }}{{- end }}<\|im_start\|>assistant
	"""

	PARAMETER stop "<\|im_end\|>"
	PARAMETER stop "<\|im_start\|>"
	PARAMETER temperature 0.6
	PARAMETER top_p 0.95
	PARAMETER num_ctx 32768