feat: add local training script for Mac MPS

- Simple one-command training on Mac
- Downloads model if not present
- Uses existing training data
- Configured for MPS (Apple Silicon)

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

train_local.py

@@ -0,0 +1,115 @@
+#!/usr/bin/env python3
+"""
+Stack 2.9 Local Training Script for Mac (MPS)
+Run this on your Mac to train the model locally.
+"""
+
+import os
+import sys
+
+# Add the training module to path
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'stack/training'))
+
+# Set environment for MPS
+os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1'
+
+def main():
+    print("=" * 60)
+    print("Stack 2.9 Local Training (Mac MPS)")
+    print("=" * 60)
+
+    # Check MPS availability
+    try:
+        import torch
+        print(f"PyTorch version: {torch.__version__}")
+        print(f"MPS available: {torch.backends.mps.is_available()}")
+        if torch.backends.mps.is_available():
+            print(f"MPS built: {torch.backends.mps.is_built()}")
+    except Exception as e:
+        print(f"⚠️ PyTorch/MPS check error: {e}")
+
+    # Check paths
+    base_model = "./base_model_qwen7b"
+    data_path = "./data/final/train.jsonl"
+    output_dir = "./training_output"
+    model_name = "Qwen/Qwen2.5-Coder-7B"
+
+    print(f"\n📁 Checking paths...")
+    print(f"   Base model: {base_model} - {'✅ exists' if os.path.exists(base_model) else '❌ not found'}")
+    print(f"   Data: {data_path} - {'✅ exists' if os.path.exists(data_path) else '❌ not found'}")
+
+    # Download model if not exists
+    if not os.path.exists(base_model):
+        print(f"\n⬇️  Downloading model ({model_name})...")
+        print("   This takes ~10-15 minutes...")
+        from transformers import AutoModelForCausalLM, AutoTokenizer
+
+        tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+        tokenizer.save_pretrained(base_model)
+        model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
+        model.save_pretrained(base_model)
+        print(f"   ✅ Model saved to {base_model}")
+    else:
+        print(f"   ✅ Model already exists!")
+
+    if not os.path.exists(data_path):
+        print("\n❌ Training data not found!")
+        print("   Expected: ./data/final/train.jsonl")
+        print("   Available data files:")
+        for root, dirs, files in os.walk("./data"):
+            for f in files:
+                if f.endswith(".jsonl"):
+                    print(f"   - {os.path.join(root, f)}")
+        return
+
+    # Create output directory
+    os.makedirs(output_dir, exist_ok=True)
+
+    # Load and update config
+    import yaml
+
+    config_path = "stack/training/train_config_local.yaml"
+    if os.path.exists(config_path):
+        with open(config_path, 'r') as f:
+            config = yaml.safe_load(f)
+    else:
+        print(f"⚠️ Config not found at {config_path}, using defaults")
+        config = {
+            'model': {'name': base_model, 'trust_remote_code': True},
+            'data': {'input_path': data_path, 'max_length': 2048},
+            'lora': {'r': 16, 'alpha': 32, 'target_modules': ['q_proj', 'k_proj', 'v_proj', 'o_proj']},
+            'training': {'num_epochs': 1, 'batch_size': 1, 'learning_rate': 2e-4},
+            'output': {'lora_dir': f'{output_dir}/lora', 'merged_dir': f'{output_dir}/merged'},
+            'hardware': {'device': 'mps'}
+        }
+
+    # Update config with local paths
+    config['model']['name'] = base_model
+    config['data']['input_path'] = data_path
+    config['output']['lora_dir'] = f"{output_dir}/lora"
+    config['output']['merged_dir'] = f"{output_dir}/merged"
+    config['hardware']['device'] = "mps"
+
+    # Save updated config
+    updated_config = f"{output_dir}/train_config.yaml"
+    with open(updated_config, 'w') as f:
+        yaml.dump(config, f)
+
+    print(f"\n✅ Config saved to: {updated_config}")
+    print(f"\n🚀 Starting training...")
+    print(f"   Output will be at: {output_dir}/")
+    print("=" * 60)
+
+    # Run training
+    from train_lora import train_lora
+    trainer = train_lora(updated_config)
+
+    print("=" * 60)
+    print("✅ TRAINING COMPLETED!")
+    print(f"   LoRA adapter: {output_dir}/lora/")
+    print(f"   Merged model: {output_dir}/merged/")
+    print("=" * 60)
+
+
+if __name__ == "__main__":
+    main()