3B_full.yaml

# Config for multi-device full finetuning in full_finetune_distributed.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 4 devices, run the following command from root:
#   tune run --nproc_per_node 4 full_finetune_distributed --config llama3_2/3B_full
#
# You can add specific overrides through the command line. For example
# to override the checkpointer directory while launching training
# you can run:
#   tune run --nproc_per_node 4 full_finetune_distributed --config llama3_2/3B_full checkpointer.checkpoint_dir=<YOUR_CHECKPOINT_DIR>
#
# This config works best when the model is being fine-tuned on 2+ GPUs.
# Single device full finetuning requires more memory optimizations. It's
# best to use 3B_full_single_device.yaml for those cases


# Tokenizer
tokenizer:
  _component_: torchtune.models.llama3.llama3_tokenizer
  path: /tmp/Llama-3.2-3B-Instruct/original/tokenizer.model
  max_seq_len: null

# Dataset
dataset:
  packed: False # Set to true for great speed ups
  _component_: torchtune.datasets.alpaca_dataset
seed: null
shuffle: True

# Model Arguments
model:
  _component_: torchtune.models.llama3_2.llama3_2_3b

checkpointer:
  _component_: torchtune.training.FullModelHFCheckpointer
  checkpoint_dir: /tmp/Llama-3.2-3B-Instruct/
  checkpoint_files: [
    model-00001-of-00002.safetensors,
    model-00002-of-00002.safetensors,
  ]
  recipe_checkpoint: null
  output_dir: /tmp/Llama-3.2-3B-Instruct/
  model_type: LLAMA3_2
resume_from_checkpoint: False

# Fine-tuning arguments
batch_size: 4
epochs: 3

optimizer:
  _component_: torch.optim.AdamW
  lr: 2e-5
  fused: True
loss:
  _component_: torchtune.modules.loss.CEWithChunkedOutputLoss
max_steps_per_epoch: null
gradient_accumulation_steps: 4

# Training env
device: cuda

# Memory management
enable_activation_checkpointing: True
compile: False # pytorch compile, set to true for perf/memory improvement

# Reduced precision
dtype: bf16

# Logging
metric_logger:
  _component_: torchtune.training.metric_logging.DiskLogger
  log_dir: ${output_dir}
output_dir: /tmp/full-llama3.2-finetune
log_every_n_steps: 1
log_peak_memory_stats: True