experiment:
  name: "progressive_reasoning_large_model"
  base_model: "meta-llama/Llama-3.2-8B"  # Or other whitelisted models
  output_dir: "./outputs"
  use_wandb: true
  wandb_project: "matsuo-llm-comp-2025"

model:
  load_in_4bit: true  # Enable 4-bit quantization for memory efficiency
  bnb_4bit_compute_dtype: "bfloat16"
  bnb_4bit_use_double_quant: true
  bnb_4bit_quant_type: "nf4"
  device_map: "auto"
  # Additional memory optimizations
  gradient_checkpointing: true
  use_flash_attention_2: true  # If available

progressive_stages:
  - name: "basic_cot"
    description: "Basic Chain-of-Thought reasoning"
    dataset_path: "./data/basic_cot/"
    adapter_config:
      r: 16  # Larger rank for bigger models
      lora_alpha: 32
      lora_dropout: 0.05
      target_modules: ["q_proj", "v_proj", "k_proj", "o_proj"]
      init_lora_weights: true  # Identity initialization
    training:
      num_epochs: 1
      per_device_batch_size: 1  # Small batch size for large models
      gradient_accumulation_steps: 16  # Effective batch size = 16
      learning_rate: 2e-4
      warmup_steps: 100
      max_length: 2048
      fp16: false
      bf16: true
      max_grad_norm: 0.3
      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: 32  # Increase rank for more complex tasks
      lora_alpha: 64
      lora_dropout: 0.05
      target_modules: ["q_proj", "v_proj", "k_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]
      init_lora_weights: true
    training:
      num_epochs: 2
      per_device_batch_size: 1
      gradient_accumulation_steps: 32  # Effective batch size = 32
      learning_rate: 1e-4
      warmup_steps: 200
      max_length: 4096
      bf16: true
      max_grad_norm: 0.3
      weight_decay: 0.001

  - name: "complex_reasoning"
    description: "Complex multi-step reasoning"
    dataset_path: "./data/complex_reasoning/"
    inherit_from: "math_reasoning"
    adapter_config:
      r: 64  # Maximum rank for most complex tasks
      lora_alpha: 128
      lora_dropout: 0.05
      target_modules: ["q_proj", "v_proj", "k_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]
      init_lora_weights: true
    training:
      num_epochs: 2
      per_device_batch_size: 1
      gradient_accumulation_steps: 64  # Effective batch size = 64
      learning_rate: 5e-5
      warmup_steps: 300
      max_length: 8192
      bf16: true
      max_grad_norm: 0.3
      weight_decay: 0.001

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

# Memory optimization settings
optimization:
  gradient_checkpointing: true
  gradient_checkpointing_kwargs:
    use_reentrant: false
  ddp_find_unused_parameters: false
  fsdp: "full_shard auto_wrap"  # For multi-GPU setups
  fsdp_transformer_layer_cls_to_wrap: "LlamaDecoderLayer"