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

File size: 2,060 Bytes

b6ae7b8

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