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
File size: 1,575 Bytes
6379283 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 | #!/bin/bash
set -e
# Stack 2.9 vLLM Startup Script
# Handles proper startup, logging, and signal handling
echo "๐ 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 "๐ 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 "โ ๏ธ 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 "๐ 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 "โ
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
|