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 / .dockerignore

walidsobhie-code

feat: add production infrastructure - CI/CD, Docker, code quality, and monitoring

b5998ff about 2 months ago

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	# =============================================================================
	# .dockerignore — Stack 2.9
	# Excludes everything not needed at runtime to keep image build fast & small.
	# =============================================================================

	# --- Git ------------------------------------------------------------
	.git
	.gitignore
	.github

	# --- Documentation -------------------------------------------------
	*.md
	LICENSE
	CODE_OF_CONDUCT.md
	CONTRIBUTING.md
	SECURITY.md
	CHANGELOG.md
	DIRECTORY_STRUCTURE.md

	# --- Build / CI artifacts ------------------------------------------
	*.egg-info/
	dist/
	build/
	*.whl

	# --- Python --------------------------------------------------------
	__pycache__/
	*.py[cod]
	*$py.class
	*.so
	.Python
	env/
	venv/
	.venv/
	ENV/
	pip-log.txt
	pip-delete-this-directory.txt
	.pytest_cache/
	.mypy_cache/
	*.egg

	# --- Node / npm ----------------------------------------------------
	node_modules/
	package-lock.json
	npm-debug.log*
	tsconfig.json

	# --- Jupyter / notebooks -------------------------------------------
	*.ipynb
	.ipynb_checkpoints/

	# --- Training -------------------------------------------------------
	# DO NOT include training scripts (per task requirement)
	train_*.py
	train_local.py
	merge_simple.py
	evaluate_model.py
	kaggle_train_stack29_v5.ipynb
	colab_train_stack29.ipynb
	training-configs/
	training-data/
	scripts/
	samples/

	# --- Data & output -------------------------------------------------
	data/
	output/
	logs/
	*.log
	*.jsonl
	*.jsonlines

	# --- Model weights -------------------------------------------------
	# (These are mounted at runtime via docker-compose.volumes.
	# Never COPY them into the build context.)
	base_model_qwen7b/
	*.safetensors
	*.bin
	*.ckpt
	*.pt
	*.pth

	# --- HuggingFace cache ---------------------------------------------
	.huggingface/
	cache/

	# --- Temporary -----------------------------------------------------
	tmp/
	temp/
	*.tmp
	*.npy
	*.npz

	# --- IDE / editor --------------------------------------------------
	.vscode/
	.idea/
	*.swp
	*.swo
	*~
	.DS_Store

	# --- Environment / secrets ----------------------------------------
	.env
	.env.local
	.env.*
	.secrets/
	*.pem
	*.key

	# --- Misc ----------------------------------------------------------
	*.npy
	*.npz
	Makefile
	GIT_PUSH.md
	LAUNCH_*.md
	runpod_deploy.sh
	vastai_deploy.sh
	TOOLS.md