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 / README.md

mach-kernel

Upload README.md with huggingface_hub

ec440de verified 3 months ago

preview code

Raw

History Blame Contribute Delete

1.57 kB

metadata

license: apache-2.0
base_model: Qwen/Qwen3.5-35B-A3B-Base
tags:
  - gguf
  - fine-tuned
  - tool-calling
  - mcp
  - dbt
  - q4_k_m

ecu-pilot (GGUF Q4_K_M)

Quantized GGUF of ecu-pilot-fp16 — a fine-tuned Qwen3.5-35B-A3B for structured tool calling against project metadata via MCP.

Quantization


Source	mach-kernel/ecu-pilot-fp16
Method	Q4_K_M via llama.cpp
Size	~20 GB
Architecture	Mixture of Experts (35B total, 3B active per token)

Usage

Ollama

# Create a Modelfile
echo 'FROM ./ecu-pilot-q4_k_m.gguf
PARAMETER temperature 0.2
PARAMETER num_ctx 8192
PARAMETER stop <|im_end|>' > Modelfile

ollama create ecu-pilot -f Modelfile
ollama run ecu-pilot

llama.cpp

llama-cli -m ecu-pilot-q4_k_m.gguf -ngl 99 -cnv

All variants

Format	Repository	Size
FP16	mach-kernel/ecu-pilot-fp16	~67 GB
GGUF Q4_K_M (this repo)	mach-kernel/ecu-pilot-q4km	~20 GB
GGUF Q8_0	mach-kernel/ecu-pilot-q8_0	~35 GB
LoRA adapter	mach-kernel/ecu-pilot-fp16-lora	~4 GB

Why "ecu"

No reason. Just liked how it sounded. Definitely not a Caesar cipher of anything. Don't look into it.