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2.59 kB
| # AETHER Quickstart — One-command setup and training | |
| # Usage: bash quickstart.sh | |
| set -e | |
| echo "==========================================" | |
| echo " AETHER: Self-Evolving Neuro-Symbolic AGI" | |
| echo "==========================================" | |
| echo "" | |
| # Check for GPU | |
| if command -v nvidia-smi &> /dev/null; then | |
| echo "✓ GPU detected:" | |
| nvidia-smi --query-gpu=name,memory.total --format=csv,noheader | head -1 | |
| DEVICE="gpu" | |
| else | |
| echo "⚠No GPU detected — will train on CPU (slower)" | |
| DEVICE="cpu" | |
| fi | |
| # Check Python | |
| if ! command -v python3 &> /dev/null; then | |
| echo "✗ Python 3 not found. Please install Python 3.10+" | |
| exit 1 | |
| fi | |
| echo "✓ Python: $(python3 --version)" | |
| # Create venv if doesn't exist | |
| if [ ! -d "venv" ]; then | |
| echo "" | |
| echo "Creating virtual environment..." | |
| python3 -m venv venv | |
| fi | |
| source venv/bin/activate | |
| # Install dependencies | |
| echo "" | |
| echo "Installing dependencies..." | |
| pip install -q --upgrade pip | |
| pip install -q torch transformers datasets accelerate peft trl networkx numpy sentencepiece protobuf huggingface-hub | |
| echo "✓ Dependencies installed" | |
| # Clone AETHER if not present | |
| if [ ! -d "aether-core" ]; then | |
| echo "" | |
| echo "Downloading AETHER..." | |
| git clone https://huggingface.co/camdog920/aether-core | |
| fi | |
| cd aether-core | |
| # Set HF token if available | |
| if [ -n "$HF_TOKEN" ]; then | |
| echo "✓ HF_TOKEN found" | |
| huggingface-cli login --token "$HF_TOKEN" --add-to-git-credential | |
| else | |
| echo "âš HF_TOKEN not set. Set it with: export HF_TOKEN=hf_xxxx" | |
| echo " Get token: https://huggingface.co/settings/tokens" | |
| fi | |
| # Choose training script | |
| if [ "$DEVICE" = "gpu" ]; then | |
| echo "" | |
| echo "==========================================" | |
| echo " Starting GPU Training (GRPO)" | |
| echo " Model: Qwen/Qwen2.5-0.5B-Instruct" | |
| echo " Duration: ~2-3 hours" | |
| echo "==========================================" | |
| python aether_train.py | |
| else | |
| echo "" | |
| echo "==========================================" | |
| echo " Starting CPU Training (SFT)" | |
| echo " Model: Qwen/Qwen2.5-0.5B-Instruct" | |
| echo " Duration: ~6-8 hours" | |
| echo "==========================================" | |
| python aether_train_cpu.py | |
| fi | |
| echo "" | |
| echo "==========================================" | |
| echo " Training Complete!" | |
| echo " Model pushed to HuggingFace Hub" | |
| echo "==========================================" | |
| echo "" | |
| echo "Next steps:" | |
| echo " 1. Load model: from transformers import AutoModelForCausalLM" | |
| echo " 2. Integrate with AETHER core for self-evolution" | |
| echo " 3. Run: python aether_demo.py" | |