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

Notebooks
Google Colab
Kaggle
Local Apps Settings

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 Desktop

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

Run Pi

# Start Pi in your project directory:
pi

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

Hermes Agent

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

OpenClaw

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"

mach-kernel commited on Apr 3

Commit

ec440de

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1 Parent(s): 9216a4e

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+---
+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](https://huggingface.co/mach-kernel/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](https://huggingface.co/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
+```bash
+# 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
+```bash
+llama-cli -m ecu-pilot-q4_k_m.gguf -ngl 99 -cnv
+```
+## All variants
+| Format | Repository | Size |
+|--------|-----------|------|
+| FP16 | [mach-kernel/ecu-pilot-fp16](https://huggingface.co/mach-kernel/ecu-pilot-fp16) | ~67 GB |
+| GGUF Q4_K_M (this repo) | [mach-kernel/ecu-pilot-q4km](https://huggingface.co/mach-kernel/ecu-pilot-q4km) | ~20 GB |
+| GGUF Q8_0 | [mach-kernel/ecu-pilot-q8_0](https://huggingface.co/mach-kernel/ecu-pilot-q8_0) | ~35 GB |
+| LoRA adapter | [mach-kernel/ecu-pilot-fp16-lora](https://huggingface.co/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.