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 / stack /deploy /docker-compose.yaml

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	version: '3.8'

	services:
	stack-2.9:
	build:
	context: .
	dockerfile: Dockerfile
	args:
	- PYTHON_VERSION=3.10
	- VLLM_VERSION=0.6.3
	- CUDA_VERSION=12.1.0
	container_name: stack-2.9-server
	restart: unless-stopped
	ports:
	- "${STACK_PORT:-8000}:8000"
	environment:
	# Model configuration
	- MODEL_ID=${MODEL_ID:-TheBloke/Llama-2-7B-Chat-AWQ}
	- HUGGING_FACE_TOKEN=${HUGGING_FACE_TOKEN:-}
	- QUANTIZATION=${QUANTIZATION:-awq}

	# vLLM engine parameters
	- TENSOR_PARALLEL_SIZE=${TENSOR_PARALLEL_SIZE:-1}
	- GPU_MEMORY_UTILIZATION=${GPU_MEMORY_UTILIZATION:-0.9}
	- MAX_MODEL_LEN=${MAX_MODEL_LEN:-4096}
	- MAX_NUM_SEQS=${MAX_NUM_SEQS:-64}
	- MAX_NUM_BATCHED_TOKENS=${MAX_NUM_BATCHED_TOKENS:-4096}
	- ENFORCE_EAGER=${ENFORCE_EAGER:-false}
	- DISABLE_LOG_STATS=${DISABLE_LOG_STATS:-false}

	# Server configuration
	- HOST=${HOST:-0.0.0.0}
	- PORT=${PORT:-8000}
	- MODEL_CACHE_DIR=${MODEL_CACHE_DIR:-/home/vllm/.cache/huggingface}

	# Performance tuning
	- OMP_NUM_THREADS=${OMP_NUM_THREADS:-4}
	- CUDA_LAUNCH_BLOCKING=${CUDA_LAUNCH_BLOCKING:-0}
	- CUDNN_LOGINFO_DBG=1

	volumes:
	# Model cache persistence
	- model_cache:/home/vllm/.cache/huggingface:rw
	# Optional: mount custom models
	- ./models:/app/models:ro
	networks:
	- stack-network
	# GPU configuration - uncomment for GPU support
	deploy:
	resources:
	reservations:
	devices:
	- driver: nvidia
	count: all
	capabilities: [gpu]
	# Runtime configuration
	runtime: nvidia
	# Health check
	healthcheck:
	test: ["CMD", "curl", "-f", "http://localhost:8000/health"]
	interval: 30s
	timeout: 10s
	retries: 3
	start_period: 60s
	# Resource limits
	# mem_limit: ${MEM_LIMIT:-8g}
	# mem_reservation: ${MEM_RESERVATION:-4g}

	volumes:
	model_cache:
	driver: local

	networks:
	stack-network:
	driver: bridge