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 /DIRECTORY_STRUCTURE.md

walidsobhie-code

reorganize: consolidate root level to 20 folders

b8e3e42 about 1 month ago

preview code

raw

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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

	# 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