experiment:
  name: "progressive_reasoning_gemma2_small"
  base_model: "google/gemma-2-2b-it"  # Instruction-tuned version
  output_dir: "./outputs"
  use_wandb: true
  wandb_project: "matsuo-llm-comp-2025"

model:
  load_in_4bit: false  # 2B model is manageable without quantization
  bnb_4bit_compute_dtype: "bfloat16"
  bnb_4bit_use_double_quant: true
  device_map: "auto"
  gradient_checkpointing: false
  use_flash_attention_2: false
  use_eager_attention: true  # Required for Gemma 3 models

progressive_stages:
  - name: "basic_cot"
    description: "Basic Chain-of-Thought reasoning"
    dataset_path: "./data/basic_cot/"
    adapter_config:
      r: 8  # Start with smaller rank for small model
      lora_alpha: 16
      lora_dropout: 0.1
      target_modules: ["q_proj", "k_proj", "v_proj", "o_proj"]
      init_lora_weights: true
    training:
      num_epochs: 3
      per_device_batch_size: 8  # Larger batch size for small model
      gradient_accumulation_steps: 2
      learning_rate: 5e-4  # Higher learning rate for small model
      warmup_steps: 50
      max_length: 1024
      bf16: true
      max_grad_norm: 1.0
      weight_decay: 0.001
      save_steps: 50
      logging_steps: 10

  - name: "math_reasoning"
    description: "Mathematical reasoning with think tags"
    dataset_path: "./data/math_reasoning/"
    inherit_from: "basic_cot"
    adapter_config:
      r: 16
      lora_alpha: 32
      lora_dropout: 0.1
      target_modules: ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]
      init_lora_weights: true
    training:
      num_epochs: 3
      per_device_batch_size: 4
      gradient_accumulation_steps: 4
      learning_rate: 3e-4
      warmup_steps: 100
      max_length: 2048
      bf16: true
      max_grad_norm: 1.0

  - name: "complex_reasoning"
    description: "Complex multi-step reasoning with Mixture-of-Thoughts"
    dataset_path: "open-r1/Mixture-of-Thoughts"  # HuggingFace dataset
    inherit_from: "math_reasoning"
    adapter_config:
      r: 32
      lora_alpha: 64
      lora_dropout: 0.1
      target_modules: ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]
      init_lora_weights: true
    training:
      num_epochs: 1  # Large dataset, fewer epochs
      per_device_batch_size: 2
      gradient_accumulation_steps: 8
      learning_rate: 2e-4
      warmup_steps: 200
      max_length: 4096
      bf16: true
      max_grad_norm: 1.0
      save_steps: 500
      logging_steps: 50
    dataset_config:
      streaming: true
      max_samples: 30000
      split: "train"

evaluation:
  benchmarks:
    - "HLE"
    - "Do-Not-Answer"
  save_results: true
  results_dir: "./outputs/evaluation_results"