Stack 2.9 β Quick Start
AI coding assistant powered by Qwen2.5-Coder-32B with Pattern Memory.
git clone https://github.com/my-ai-stack/stack-2.9.git
cd stack-2.9
pip install -r requirements.txt
cp .env.example .env
python stack.py
That's it. Keep reading for details.
Prerequisites
- Python 3.10+
- GPU (optional β runs on CPU via cloud providers too)
- Git
Install & Run
# Clone
git clone https://github.com/my-ai-stack/stack-2.9.git
cd stack-2.9
# Install
python3 -m venv venv && source venv/bin/activate
pip install -r requirements.txt
# Configure (pick a provider below, then edit .env)
cp .env.example .env
# Run!
python stack.py
Configure Your Model Provider
Edit .env with one of these:
Ollama (Local, Private) β Recommended
MODEL_PROVIDER=ollama
OLLAMA_MODEL=qwen2.5-coder:32b
# First: curl -fsSL https://ollama.ai/install.sh | sh && ollama pull qwen2.5-coder:32b
Together AI (Cloud, Fast)
MODEL_PROVIDER=together
TOGETHER_API_KEY=tog-your-key-here
TOGETHER_MODEL=togethercomputer/qwen2.5-32b-instruct
OpenAI (GPT-4o)
MODEL_PROVIDER=openai
OPENAI_API_KEY=sk-your-key-here
OPENAI_MODEL=gpt-4o
Anthropic (Claude)
MODEL_PROVIDER=anthropic
ANTHROPIC_API_KEY=sk-ant-your-key-here
ANTHROPIC_MODEL=claude-3-5-sonnet-20240229
Usage
Interactive Chat
python stack.py
Single Query
python stack.py -c "Write a Python function to reverse a string"
Evaluate Model (GPU required)
python evaluate_model.py --model-path ./output/merged --benchmark humaneval
Deploy with Docker
docker build -t stack-2.9 . && docker run -p 7860:7860 stack-2.9
5-Minute Overview
| Feature | Command |
|---|---|
| Start chatting | python stack.py |
| Ask one question | python stack.py -c "your question" |
| Run benchmarks | python evaluate_model.py --model-path ./merged --benchmark both |
| List patterns | python stack.py --patterns list |
| Deploy locally | docker build -t stack-2.9 . && docker run -p 7860:7860 stack-2.9 |
Hardware Requirements
| Model | Minimum | Recommended |
|---|---|---|
| 7B | RTX 3060 (6GB) | A100 40GB |
| 32B | RTX 3090 (24GB) | A100 80GB |
No GPU? Use Ollama on your machine or any cloud provider in .env.
Key Links
- π Full docs: docs/QUICKSTART.md
- π§ 46 tools: TOOLS.md
- π§ Pattern memory: docs/pattern-moat.md
- π Training guide: docs/TRAINING_7B.md
- π³ Kubernetes: k8s/
What's Inside
- Qwen2.5-Coder-32B β 32B parameter code-specialized model
- Pattern Memory β learns from successful interactions
- 46 built-in tools β file ops, git, shell, search, memory, tasks
- Multi-provider β Ollama, OpenAI, Anthropic, Together AI, OpenRouter
- 128K context β handles large codebases
- Self-hosted β full control, private
- MCP support β integrates with any Model Context Protocol server
- Voice-ready β Coqui XTTS for voice cloning
Built with β€οΈ for developers who want an AI that grows with them.