Text Generation
Transformers
English
qwen2
code-generation
python
fine-tuning
Qwen
tools
agent-framework
multi-agent
conversational
Eval Results (legacy)
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
- Transformers
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
| # 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 | |
| ```bash | |
| # 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 | |
| 1. **Evaluation** - Run full HumanEval/MBPP benchmarks | |
| - See `stack/eval/run_proper_evaluation.py` | |
| - Requires: Python, Ollama or API key | |
| 2. **Tool calling tests** - Test and document tool usage | |
| - Run `python stack.py -c "Your command here"` | |
| - Report what works/doesn't in issues | |
| 3. **Documentation** - Improve tool definitions, API docs | |
| - Check `docs/TOOLS.md` for accuracy | |
| - Update `stack/internal/ARCHITECTURE.md` | |
| ### Medium Priority | |
| 4. **Training scripts** - Improve fine-tuning pipeline | |
| - See `stack/training/` | |
| - ⚠️ Do NOT modify Kaggle notebook or training data generation | |
| 5. **Deployment** - Fix deployment scripts | |
| - See `stack/deploy/`, `runpod_deploy.sh` | |
| ### Lower Priority | |
| 6. **Pattern Memory** - Connect Observer → Learner → Memory → Trainer | |
| 7. **Voice integration** - Test end-to-end voice pipeline | |
| 8. **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 | |
| ```bash | |
| # Run tests before submitting | |
| pytest samples/ -v | |
| # Check code formatting | |
| ruff check src/ samples/ --fix | |
| black src/ samples/ | |
| ``` | |
| ##提交PR | |
| ```bash | |
| # 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 | |
| 1. **Describe the change clearly** - What does this fix or add? | |
| 2. **Link related issues** - Use "Fixes #123" if applicable | |
| 3. **Include tests** - Add unit tests for new features | |
| 4. **Update docs** - If you add a feature, document it | |
| 5. **Be patient** - Reviewers may take a few days to respond | |
| ## Reporting Issues | |
| When reporting bugs: | |
| ```markdown | |
| ## 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. |