7B_qlora_single_device.yaml

# Config for single device QLoRA finetuning in lora_finetune_single_device.py
# using a Mistral 7B model
#
# This config uses hyperparameters based on small set of experiments and information
# available on various forums. These are not meant to replicate the numbers
# from the paper
#
# This config assumes that you've run the following command before launching
# this run:
#   tune download mistralai/Mistral-7B-v0.1 --hf-token <HF_TOKEN> --output-dir /tmp/Mistral-7B-v0.1
#
# To launch on a single device, run the following command from root:
#   tune run lora_finetune_single_device --config mistral/7B_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 mistral/7B_qlora_single_device checkpointer.checkpoint_dir=<YOUR_CHECKPOINT_DIR>
#
# This config works only for training on single device.


# Tokenizer
tokenizer:
  _component_: torchtune.models.mistral.mistral_tokenizer
  path: /tmp/Mistral-7B-v0.1/tokenizer.model

# Dataset
dataset:
  _component_: torchtune.datasets.alpaca_dataset
seed: null
shuffle: True

# Model Arguments
model:
  _component_: torchtune.models.mistral.qlora_mistral_7b
  lora_attn_modules: ['q_proj', 'k_proj', 'v_proj']
  apply_lora_to_mlp: True
  apply_lora_to_output: False
  lora_rank: 64
  lora_alpha: 16

checkpointer:
  _component_: torchtune.utils.FullModelHFCheckpointer
  checkpoint_dir: /tmp/Mistral-7B-v0.1
  checkpoint_files: [
    pytorch_model-00001-of-00002.bin,
    pytorch_model-00002-of-00002.bin
  ]
  recipe_checkpoint: null
  output_dir: /tmp/Mistral-7B-v0.1
  model_type: MISTRAL
resume_from_checkpoint: False

optimizer:
  _component_: torch.optim.AdamW
  lr: 2e-5

lr_scheduler:
  _component_: torchtune.modules.get_cosine_schedule_with_warmup
  num_warmup_steps: 100

loss:
  _component_: torch.nn.CrossEntropyLoss

# Fine-tuning arguments
batch_size: 4
epochs: 3
max_steps_per_epoch: null
gradient_accumulation_steps: 4
compile: False

# Training env
device: cuda

# Memory management
enable_activation_checkpointing: True

# Reduced precision
dtype: bf16

# Logging
metric_logger:
  _component_: torchtune.utils.metric_logging.DiskLogger
  log_dir: ${output_dir}
output_dir: /tmp/Mistral-7B-v0.1
log_every_n_steps: 1
log_peak_memory_stats: False

# Show case the usage of pytorch profiler
# Set enabled to False as it's only needed for debugging training
profiler:
  _component_: torchtune.utils.profiler
  enabled: False
  output_dir: /tmp/alpaca-llama2-finetune/torchtune_perf_tracing.json