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

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# Stack 2.9 Directory Structure

## Quick Overview

```
stack-2.9/
├── src/           # Core source code (voice, LLM, MCP, indexing)
├── stack/         # Components (deploy, training, eval, voice, docs)
├── data/          # Training datasets
├── scripts/       # Utility scripts
├── samples/       # Examples & tests
├── docs/          # Documentation
│
├── README.md      # Main docs
├── LICENSE        # Apache 2.0
├── package.json   # npm config
├── pyproject.toml # Python config
└── .env.example   # Environment template
```

## Structure Details

### Root Files (User-Facing)
| File | Purpose |
|------|---------|
| README.md | Main documentation |
| LICENSE | Apache 2.0 license |
| CHANGELOG.md | Version history |
| CONTRIBUTING.md | Contribution guide |
| SECURITY.md | Security policy |
| .env.example | Environment variables |
| package.json | npm dependencies |
| pyproject.toml | Python project |
| requirements.txt | Python deps |
| Dockerfile | Container config |
| Makefile | Build targets |
| colab_train_stack29.ipynb | Colab training |

### Core Modules (`src/`)
- **src/voice/** - Voice integration (recording, synthesis, cloning)
- **src/llm/** - Multi-provider LLM client
- **src/mcp/** - Model Context Protocol client
- **src/indexing/** - Code indexing (RAG)
- **src/cli/** - CLI tools
- **src/utils/** - Utilities

### Components (`stack/`)
- **stack/deploy/** - Docker & deployment configs
- **stack/training/** - Model fine-tuning code
- **stack/eval/** - Evaluation & benchmarks
- **stack/voice/** - Python voice API server
- **stack/docs/** - API documentation
- **stack/internal/** - Internal docs (archive)

### Data & Scripts
- **data/** - Training datasets
- **scripts/** - Build & utility scripts
- **samples/** - Examples & test files