Instructions to use json-l/ecu-pilot-qwen3-8b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use json-l/ecu-pilot-qwen3-8b-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 json-l/ecu-pilot-qwen3-8b-GGUF
# Run inference directly in the terminal:
llama cli -hf json-l/ecu-pilot-qwen3-8b-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf json-l/ecu-pilot-qwen3-8b-GGUF
# Run inference directly in the terminal:
llama cli -hf json-l/ecu-pilot-qwen3-8b-GGUF

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 json-l/ecu-pilot-qwen3-8b-GGUF
# Run inference directly in the terminal:
./llama-cli -hf json-l/ecu-pilot-qwen3-8b-GGUF

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 json-l/ecu-pilot-qwen3-8b-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf json-l/ecu-pilot-qwen3-8b-GGUF

Use Docker

docker model run hf.co/json-l/ecu-pilot-qwen3-8b-GGUF

LM Studio
Jan

vLLM

How to use json-l/ecu-pilot-qwen3-8b-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "json-l/ecu-pilot-qwen3-8b-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": "json-l/ecu-pilot-qwen3-8b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/json-l/ecu-pilot-qwen3-8b-GGUF

Ollama
How to use json-l/ecu-pilot-qwen3-8b-GGUF with Ollama:
```
ollama run hf.co/json-l/ecu-pilot-qwen3-8b-GGUF
```
Unsloth Desktop

How to use json-l/ecu-pilot-qwen3-8b-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf json-l/ecu-pilot-qwen3-8b-GGUF

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": "json-l/ecu-pilot-qwen3-8b-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use json-l/ecu-pilot-qwen3-8b-GGUF with Docker Model Runner:
```
docker model run hf.co/json-l/ecu-pilot-qwen3-8b-GGUF
```

Lemonade

How to use json-l/ecu-pilot-qwen3-8b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull json-l/ecu-pilot-qwen3-8b-GGUF

Run and chat with the model

lemonade run user.ecu-pilot-qwen3-8b-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use json-l/ecu-pilot-qwen3-8b-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 json-l/ecu-pilot-qwen3-8b-GGUF

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 json-l/ecu-pilot-qwen3-8b-GGUF

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use json-l/ecu-pilot-qwen3-8b-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf json-l/ecu-pilot-qwen3-8b-GGUF

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 "json-l/ecu-pilot-qwen3-8b-GGUF" \
  --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"

ecu-pilot-qwen3-8b-GGUF / README.md

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metadata

license: apache-2.0
base_model: Qwen/Qwen3-8B
tags:
  - dbt
  - tool-calling
  - gguf
  - qwen3
  - fine-tuned
  - unsloth
model_type: qwen3
quantized_by: llama.cpp
pipeline_tag: text-generation

ecu-pilot-qwen3-8b-GGUF

A fine-tuned Qwen3-8B model for dbt project assistance via tool calling against dbt-index.

Overview

ecu-pilot is trained to autonomously select and call the correct dbt-index tool (out of 9 available tools) based on natural language questions about a dbt project, then summarize the results concisely.

Tools

Tool	Description
`status`	Project overview — model/test/source counts
`schema`	Database schema exploration
`search`	Full-text search across models, columns, descriptions
`node`	Detailed info on a specific model/source/test
`lineage`	Upstream/downstream dependency graph
`query`	Run SQL against the dbt-index DuckDB
`report`	Coverage reports (tests, docs, etc.)
`impact`	Blast radius analysis for model changes
`diff`	Compare project state across branches

Training

Base model: Qwen3-8B (via unsloth/Qwen3-8B-bnb-4bit)
Method: QLoRA (r=32, alpha=32) with Unsloth + TRL SFTTrainer
Data: 667 tool-calling conversation examples from 8 dbt projects (BIRD benchmark databases)
Epochs: 1
Hardware: AWS EC2 g6 (NVIDIA L40S 48GB)

Quantization

Format: GGUF Q4_K_M (4-bit quantized via llama.cpp)
Size: ~4.7 GB

Chat Template

This model uses a custom chat template for tool calling. The template is included as chat_template.jinja in this repo.

Key differences from stock Qwen3:

Tool definitions are injected into the system message within <tools> XML tags
Tool calls use <tool_call> / </tool_call> XML delimiters
Tool responses are wrapped in <tool_response> / </tool_response> and sent as user messages

When using with inference servers that support Jinja templates (vLLM, TGI, llama.cpp server), point to this template file.

Usage

LM Studio

Download the .gguf file
Place in ~/.lmstudio/models/json-l/ecu-pilot-qwen3-8b-GGUF/
Load in LM Studio, set the chat template to the contents of chat_template.jinja, and start the local server

Ollama

Create a Modelfile:

FROM ./ecu-pilot-qwen3-8b-q4km.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 }}
{{ json . }}
{{- end }}
</tools>
<|im_end|>
{{ end }}{{- range .Messages }}<|im_start|>{{ .Role }}
{{- if .Content }}
{{ .Content }}
{{- end }}{{- if .ToolCalls }}
<tool_call>
{{- range .ToolCalls }}
{"name": "{{ .Function.Name }}", "arguments": {{ json .Function.Arguments }}}
{{- end }}
</tool_call>
{{- end }}<|im_end|>
{{ end }}<|im_start|>assistant
"""

SYSTEM """You are an expert dbt project assistant with access to a dbt-index metadata server."""

PARAMETER stop "<|im_end|>"
PARAMETER stop "<tool_call>"
PARAMETER temperature 0.7
PARAMETER num_ctx 4096

Then:

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

Evaluation

On 8 test prompts (2 runs each):

Metric	Score
Tool called	88%
Correct tool	75%
Answer generated	100%

License

Apache 2.0