3B_qlora_single_device.yaml

# Config for single device QLoRA with lora_finetune_single_device.py
# using a Llama3.2 3B Instruct model
#
# This config assumes that you've run the following command before launching
# this run:
#   tune download meta-llama/Llama-3.2-3B-Instruct --output-dir /tmp/Llama-3.2-3B-Instruct --ignore-patterns "original/consolidated.00.pth"
#
# To launch on a single device, run the following command from root:
#   tune run lora_finetune_single_device --config llama3_2/3B_qlora_single_device
#
# You can add specific overrides through the command line. For example
# to override the checkpointer directory while launching training
# you can run:
#   tune run lora_finetune_single_device --config llama3_2/3B_qlora_single_device checkpointer.checkpoint_dir=<YOUR_CHECKPOINT_DIR>
#
# This config works only for training on single device.

# Model Arguments
model:
  _component_: torchtune.models.llama3_2.qlora_llama3_2_3b
  lora_attn_modules: ['q_proj', 'v_proj', 'output_proj']
  apply_lora_to_mlp: True
  apply_lora_to_output: False
  lora_rank: 64
  lora_alpha: 128
  lora_dropout: 0.0

# Tokenizer
tokenizer:
  _component_: torchtune.models.llama3.llama3_tokenizer
  path: /home/nathan/.llama/checkpoints/Llama3.2-3B-Instruct/tokenizer.model
  max_seq_len: null

checkpointer:
  _component_: torchtune.training.FullModelHFCheckpointer
  checkpoint_dir: /home/nathan/.cache/huggingface/hub/models--meta-llama--Llama-3.2-3B-Instruct/snapshots/392a143b624368100f77a3eafaa4a2468ba50a72
  checkpoint_files: [
    model-00001-of-00002.safetensors,
    model-00002-of-00002.safetensors,
  ]
  recipe_checkpoint: /Llama-Train/Llama-3.2-3B-Instruct/
  output_dir: /home/nathan/.cache/huggingface/hub/models--meta-llama--Llama-3.2-3B-Instruct/snapshots/392a143b624368100f77a3eafaa4a2468ba50a72/Llama-Train/Llama-3.2-3B-Instruct/
  model_type: LLAMA3_2
resume_from_checkpoint: False
save_adapter_weights_only: False

# Dataset and Sampler
dataset:
  _component_: torchtune.datasets.chat_dataset
  source: json
  data_files: train.json
  conversation_column: conversations
  conversation_style: sharegpt
  train_on_input: False
  packed: False
  split: train
seed: null
shuffle: True
batch_size: 1

# Optimizer and Scheduler
optimizer:
  _component_: torch.optim.AdamW
  fused: True
  weight_decay: 0.01
  lr: 3e-4
lr_scheduler:
  _component_: torchtune.modules.get_cosine_schedule_with_warmup
  num_warmup_steps: 100

loss:
  _component_: torchtune.modules.loss.CEWithChunkedOutputLoss

# Training
epochs: 1
max_steps_per_epoch: null
gradient_accumulation_steps: 4
compile: True # set it to True for better memory and performance

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

# Environment
device: cuda
dtype: bf16

# Activations Memory
enable_activation_checkpointing: False
enable_activation_offloading: False

# 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: 5
  active_steps: 2
  num_cycles: 1