Text Generation
Transformers
English
qwen2
code-generation
python
fine-tuning
Qwen
tools
agent-framework
multi-agent
conversational
Eval Results (legacy)
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
- Transformers
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
| set -e | |
| # Stack 2.9 vLLM Startup Script | |
| # Handles proper startup, logging, and signal handling | |
| echo "π Starting Stack 2.9 vLLM Server" | |
| echo "================================" | |
| # Configuration | |
| LOG_DIR="/app/logs" | |
| PID_FILE="/app/vllm.pid" | |
| LOG_FILE="${LOG_DIR}/vllm.log" | |
| # Create log directory if it doesn't exist | |
| mkdir -p "${LOG_DIR}" | |
| # Function to cleanup on exit | |
| cleanup() { | |
| echo "π Shutting down vLLM server..." | |
| if [ -f "${PID_FILE}" ]; then | |
| kill "$(cat ${PID_FILE})" 2>/dev/null || true | |
| rm "${PID_FILE}" | |
| fi | |
| exit 0 | |
| } | |
| # Trap signals | |
| trap cleanup SIGINT SIGTERM EXIT | |
| # Check if model directory exists | |
| if [ ! -d "/models" ] || [ -z "$(ls -A /models 2>/dev/null)" ]; then | |
| echo "β οΈ Warning: No model found in /models" | |
| echo " Expected model files in /models directory" | |
| echo " Mount a volume with your model or download via HF" | |
| fi | |
| # Check for required environment variables | |
| echo "π Environment Configuration:" | |
| echo " MODEL_PATH: ${MODEL_PATH:-/models}" | |
| echo " MODEL_NAME: ${MODEL_NAME:-meta-llama/Llama-3.1-8B-Instruct}" | |
| echo " GPU_MEMORY_UTILIZATION: ${GPU_MEMORY_UTILIZATION:-0.9}" | |
| echo " MAX_MODEL_LEN: ${MAX_MODEL_LEN:-131072}" | |
| echo "" | |
| # Start the server | |
| echo "Starting vLLM server..." | |
| python vllm_server.py 2>&1 | tee -a "${LOG_FILE}" & | |
| echo $! > "${PID_FILE}" | |
| echo "β vLLM server started with PID $(cat ${PID_FILE})" | |
| echo " Logs: ${LOG_FILE}" | |
| echo " Health: http://localhost:8000/health" | |
| echo "" | |
| echo "Press Ctrl+C to stop" | |
| # Wait for process | |
| wait "${PID_FILE}" 2>/dev/null || true | |