output_dir: /tmp/llama-3.2-1B_ft-output # /tmp may be deleted by your system. Change it to your preference.

# Model Arguments
model:
  _component_: torchtune.models.llama3_2.lora_llama3_2_1b
  lora_attn_modules: ['q_proj', 'v_proj', 'output_proj']
  apply_lora_to_mlp: True
  lora_rank: 64  # higher increases accuracy and memory
  lora_alpha: 128  # usually alpha=2*rank
  lora_dropout: 0.0

# Tokenizer
tokenizer:
  _component_: torchtune.models.llama3.llama3_tokenizer
  path:  /tmp/Llama-3.2-1B-Instruct/original/tokenizer.model
  special_tokens_path: null
  max_seq_len: 512
  prompt_template: null

# Dataset
dataset:
  _component_: torchtune.datasets.alpaca_dataset
  packed: False  # True increases speed
seed: null
shuffle: True

learning_rate: 5e-3

checkpointer:
  _component_: torchtune.training.FullModelHFCheckpointer
  checkpoint_dir: /tmp/Llama-3.2-1B-Instruct/
  checkpoint_files: [
    model.safetensors
  ]
  recipe_checkpoint: null
  output_dir: ${output_dir}
  model_type: LLAMA3_2
resume_from_checkpoint: False

# Fine-tuning arguments
batch_size: 1
epochs: 1
optimizer:
  _component_: torch.optim.AdamW
  fused: True
  weight_decay: 0.01
  lr: 3e-4

loss:
  _component_: torchtune.modules.loss.CEWithChunkedOutputLoss
max_steps_per_epoch: null
gradient_accumulation_steps: 1  # Use to increase effective batch size
optimizer_in_bwd: True  # True saves memory. Requires gradient_accumulation_steps=1
clip_grad_norm: null
compile: False  # torch.compile the model + loss, True increases speed + decreases memory

# Training environment
device: cpu
dtype: fp32

# Memory management
enable_activation_checkpointing: False  # True reduces memory
enable_activation_offloading: False  # True reduces memory

# Logging
metric_logger:
  _component_: torchtune.training.metric_logging.DiskLogger
  log_dir: ${output_dir}/logs
log_every_n_steps: 1
log_peak_memory_stats: True


# Profiler (disabled)
profiler:
  _component_: torchtune.training.setup_torch_profiler
  enabled: False

  #Output directory of trace artifacts
  output_dir: ${output_dir}/profiling_outputs

  #`torch.profiler.ProfilerActivity` types to trace
  cpu: True
  cuda: True

  #trace options passed to `torch.profiler.profile`
  profile_memory: False
  with_stack: False
  record_shapes: True
  with_flops: False

  # `torch.profiler.schedule` options:
  # wait_steps -> wait, warmup_steps -> warmup, active_steps -> active, num_cycles -> repeat
  wait_steps: 5
  warmup_steps: 3
  active_steps: 2
  num_cycles: 1
