7B_lora.yaml

# 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
#
# Run this config on 4 GPUs using the following:
# tune run --nproc_per_node 4 lora_finetune_distributed --config mistral/7B_lora


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

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

# Model Arguments
model:
  _component_: torchtune.models.mistral.lora_mistral_7b
  lora_attn_modules: ['q_proj', 'k_proj', 'v_proj']
  apply_lora_to_mlp: True
  apply_lora_to_output: True
  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: 1

# 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: null