Instructions to use my-ai-stack/Stack-2-9-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use my-ai-stack/Stack-2-9-finetuned with Transformers:

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

pipe = pipeline("text-generation", model="my-ai-stack/Stack-2-9-finetuned")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("my-ai-stack/Stack-2-9-finetuned")
model = AutoModelForCausalLM.from_pretrained("my-ai-stack/Stack-2-9-finetuned", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use my-ai-stack/Stack-2-9-finetuned with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "my-ai-stack/Stack-2-9-finetuned"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "my-ai-stack/Stack-2-9-finetuned",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/my-ai-stack/Stack-2-9-finetuned

SGLang

How to use my-ai-stack/Stack-2-9-finetuned 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 "my-ai-stack/Stack-2-9-finetuned" \
    --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": "my-ai-stack/Stack-2-9-finetuned",
		"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 "my-ai-stack/Stack-2-9-finetuned" \
        --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": "my-ai-stack/Stack-2-9-finetuned",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use my-ai-stack/Stack-2-9-finetuned with Docker Model Runner:
```
docker model run hf.co/my-ai-stack/Stack-2-9-finetuned
```

Stack-2-9-finetuned / docs /CONTRIBUTING.md

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Contributing to Stack 2.9

Last updated: April 2026

Thank you for your interest in contributing to Stack 2.9! This document outlines how you can help.

Project State

Before contributing, understand where the project stands:

Area	Status	Notes
Basic code generation	✅ Working	Main strength of the model
Tool calling	⚠️ Not trained	Needs fine-tuning on tool patterns
Benchmark scores	⚠️ Pending	Full evaluation not yet run
Self-evolution	🔧 Incomplete	Components exist but not connected
Documentation	🔧 In progress	Some areas need work

Quick Start

# 1. Fork the repository
git fork https://github.com/my-ai-stack/stack-2.9.git

# 2. Clone your fork
git clone https://github.com/YOUR_USER/stack-2.9.git
cd stack-2.9

# 3. Create a virtual environment
python -m venv .venv
source .venv/bin/activate  # Linux/Mac
# or .venv\Scripts\activate on Windows

# 4. Install dependencies
pip install -r requirements.txt

What to Work On

High Priority

Evaluation - Run full HumanEval/MBPP benchmarks
- See stack/eval/run_proper_evaluation.py
- Requires: Python, Ollama or API key
Tool calling tests - Test and document tool usage
- Run python stack.py -c "Your command here"
- Report what works/doesn't in issues
Documentation - Improve tool definitions, API docs
- Check docs/TOOLS.md for accuracy
- Update stack/internal/ARCHITECTURE.md

Medium Priority

Training scripts - Improve fine-tuning pipeline
- See stack/training/
- ⚠️ Do NOT modify Kaggle notebook or training data generation
Deployment - Fix deployment scripts
- See stack/deploy/, runpod_deploy.sh

Lower Priority

Pattern Memory - Connect Observer → Learner → Memory → Trainer
Voice integration - Test end-to-end voice pipeline
MCP support - Improve Model Context Protocol integration

What NOT to Touch

⚠️ Do NOT modify without explicit approval:

kaggle_train_stack29_v5.ipynb - Kaggle training notebook
colab_train_stack29.ipynb - Colab training notebook
Training data generation scripts in data/
Model weights in base_model_qwen7b/

These are core training components. Changes here affect the model itself.

Code Style

Python: Follow PEP 8, use type hints where possible
TypeScript: Use strict mode, add JSDoc comments
Shell: Use shellcheck on bash scripts
General: Add docstrings to new functions, include examples

Pre-commit Checks

# Run tests before submitting
pytest samples/ -v

# Check code formatting
ruff check src/ samples/ --fix
black src/ samples/

##提交PR

# Create a feature branch
git checkout -b feature/your-feature-name

# Make your changes
# ... edit files ...

# Run tests
pytest samples/ -v

# Commit with clear message
git commit -m "Add: description of what you changed"

# Push to your fork
git push origin feature/your-feature-name

# Open a Pull Request
# Fill in the PR template with:
# - What you changed
# - Why it's needed
# - Testing you did
# - Screenshots if applicable

Pull Request Guidelines

Describe the change clearly - What does this fix or add?
Link related issues - Use "Fixes #123" if applicable
Include tests - Add unit tests for new features
Update docs - If you add a feature, document it
Be patient - Reviewers may take a few days to respond

Reporting Issues

When reporting bugs:

## Description
Brief description of the issue

## Steps to Reproduce
1. Run `...`
2. See error

## Expected Behavior
What should happen

## Actual Behavior
What actually happened

## Environment
- OS: 
- Python version:
- Provider: (ollama/openai/etc)
- Model:

Communication

Issues: GitHub Issues for bugs/features
Discussions: GitHub Discussions for questions
Discord: Link in README

Recognition

Contributors will be listed in:

README.md "Acknowledgments" section
CONTRIBUTORS file (if created)

Questions? Open a GitHub Discussion or ask in Discord.