Instructions to use fivetech/Harbour with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use fivetech/Harbour with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("/home/fivetech/finetune/models/Qwen3.6-35B-A3B")
model = PeftModel.from_pretrained(base_model, "fivetech/Harbour")

Transformers

How to use fivetech/Harbour with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="fivetech/Harbour")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("fivetech/Harbour", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use fivetech/Harbour 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 fivetech/Harbour:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf fivetech/Harbour:Q4_K_M

Install from WinGet (Windows)

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

Use Docker

docker model run hf.co/fivetech/Harbour:Q4_K_M

LM Studio
Jan

vLLM

How to use fivetech/Harbour with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "fivetech/Harbour"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fivetech/Harbour",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/fivetech/Harbour:Q4_K_M

SGLang

How to use fivetech/Harbour with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "fivetech/Harbour" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fivetech/Harbour",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "fivetech/Harbour" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fivetech/Harbour",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use fivetech/Harbour with Ollama:
```
ollama run hf.co/fivetech/Harbour:Q4_K_M
```

Unsloth Studio

How to use fivetech/Harbour 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 fivetech/Harbour 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 fivetech/Harbour to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for fivetech/Harbour to start chatting

How to use fivetech/Harbour with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf fivetech/Harbour: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": "fivetech/Harbour:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use fivetech/Harbour with Docker Model Runner:
```
docker model run hf.co/fivetech/Harbour:Q4_K_M
```

Lemonade

How to use fivetech/Harbour with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull fivetech/Harbour:Q4_K_M

Run and chat with the model

lemonade run user.Harbour-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use fivetech/Harbour with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf fivetech/Harbour: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 fivetech/Harbour:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use fivetech/Harbour with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf fivetech/Harbour: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 "fivetech/Harbour: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"

fivetech commited on Jun 21

Commit

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+# Harbour Fine-tuning Dataset
+## Overview
+This dataset contains 996 training entries extracted from:
+- 1037 Harbour PRG (.prg) source files
+- 143 Harbour Header (.ch) files
+177 PRG files and 7 CH files were skipped due to quality issues.
+## Dataset Format
+The dataset is provided in JSONL format with the following structure:
+### Instruction Format (harbour_train.jsonl / harbour_val.jsonl)
+```json
+{"instruction": "...", "input": "", "output": "..."}
+```
+### Full Dataset (harbour_dataset_full.jsonl)
+```json
+{"instruction": "...", "input": "", "output": "...", "metadata": {"file_path": "...", "language": "harbour", "category": "...", "subcategory": "..."}}
+```
+## Categories
+- **include**: Header files with constants/macros (59 files)
+- **rtl**: Harbour Runtime Library (80 files)
+- **contrib**: Contribution libraries (583 files)
+- **tests**: Test programs (225 files)
+- **utils**: Utility programs (13 files)
+- **extras**: Extra libraries (25 files)
+## Cleaning Applied
+- Copyright/license headers removed
+- Disabled code blocks (#if 0) removed
+- Excessive trailing comments removed
+- Excessive blank lines removed
+- Files without actual code filtered out
+- Incomplete code (missing ENDCLASS, etc.) filtered out
+## Usage for Fine-tuning
+```bash
+# Using Ollama with Modelfile
+FROM qwen2.5-coder:14b
+# Training command
+ollama create harbour-coder -f Modelfile
+# Or use with other training frameworks
+# The JSONL format is compatible with:
+# - OpenAI fine-tuning API
+# - Hugging Face transformers
+# - Axolotl
+# - LLaMA-Factory
+```
+## File Structure
+- `harbour_train.jsonl` - Training set (896 entries)
+- `harbour_val.jsonl` - Validation set (100 entries)
+- `harbour_dataset_full.jsonl` - Full dataset with metadata
+- `dataset_stats.json` - Dataset statistics
+- `generate_dataset.py` - This script
+## Source
+The source files are from the Harbour project (https://harbour.github.io/),
+an open-source Clipper-compatible compiler.