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")
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

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 /README_QUICKSTART.md

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reorganize: consolidate root level to 20 folders

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	# Stack 2.9 — 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 — runs on CPU via cloud providers too)
	- Git

	---

	## Install & Run

	```bash
	# 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) — Recommended
	```env
	MODEL_PROVIDER=ollama
	OLLAMA_MODEL=qwen2.5-coder:32b
	```
	```bash
	# First: curl -fsSL https://ollama.ai/install.sh \| sh && ollama pull qwen2.5-coder:32b
	```

	### Together AI (Cloud, Fast)
	```env
	MODEL_PROVIDER=together
	TOGETHER_API_KEY=tog-your-key-here
	TOGETHER_MODEL=togethercomputer/qwen2.5-32b-instruct
	```

	### OpenAI (GPT-4o)
	```env
	MODEL_PROVIDER=openai
	OPENAI_API_KEY=sk-your-key-here
	OPENAI_MODEL=gpt-4o
	```

	### Anthropic (Claude)
	```env
	MODEL_PROVIDER=anthropic
	ANTHROPIC_API_KEY=sk-ant-your-key-here
	ANTHROPIC_MODEL=claude-3-5-sonnet-20240229
	```

	---

	## Usage

	### Interactive Chat
	```bash
	python stack.py
	```

	### Single Query
	```bash
	python stack.py -c "Write a Python function to reverse a string"
	```

	### Evaluate Model (GPU required)
	```bash
	python evaluate_model.py --model-path ./output/merged --benchmark humaneval
	```

	### Deploy with Docker
	```bash
	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

	- 📖 Full docs: [docs/QUICKSTART.md](docs/QUICKSTART.md)
	- 🔧 46 tools: [TOOLS.md](TOOLS.md)
	- 🧠 Pattern memory: [docs/pattern-moat.md](docs/pattern-moat.md)
	- 🚀 Training guide: [docs/TRAINING_7B.md](docs/TRAINING_7B.md)
	- 🐳 Kubernetes: [k8s/](k8s/)

	---

	## What's Inside

	- Qwen2.5-Coder-32B — 32B parameter code-specialized model
	- Pattern Memory — learns from successful interactions
	- 46 built-in tools — file ops, git, shell, search, memory, tasks
	- Multi-provider — Ollama, OpenAI, Anthropic, Together AI, OpenRouter
	- 128K context — handles large codebases
	- Self-hosted — full control, private
	- MCP support — integrates with any Model Context Protocol server
	- Voice-ready — Coqui XTTS for voice cloning

	---

	Built with ❤️ for developers who want an AI that grows with them.