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# Config for single device LoRA finetuning in lora_finetune_single_device.py
# using a Llama3 8B model
#
# This config assumes that you've run the following command before launching
# this run:
# tune download meta-llama/Meta-Llama-3-8B --output-dir /tmp/Meta-Llama-3-8B --hf-token <HF_TOKEN>
#
# To launch on a single device, run the following command from root:
# tune run lora_finetune_single_device --config llama3/8B_lora_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 llama3/8B_lora_single_device checkpointer.checkpoint_dir=<YOUR_CHECKPOINT_DIR>
#
# This config works only for training on single device.
# Model Arguments
model:
_component_: torchtune.models.llama3.lora_llama3_8b
lora_attn_modules: ['q_proj', 'v_proj']
apply_lora_to_mlp: False
apply_lora_to_output: False
lora_rank: 8
lora_alpha: 16
# Tokenizer
tokenizer:
_component_: torchtune.models.llama3.llama3_tokenizer
path: /home/aorogat/Meta-Llama-3-8B/original/tokenizer.model
checkpointer:
_component_: torchtune.utils.FullModelMetaCheckpointer
checkpoint_dir: /home/aorogat/Meta-Llama-3-8B/original/
checkpoint_files: [
consolidated.00.pth
]
recipe_checkpoint: null
output_dir: /home/aorogat/q_to_template/
model_type: LLAMA3
resume_from_checkpoint: False
# Dataset and Sampler
dataset:
_component_: torchtune.datasets.instruct_dataset
split: train
source: /home/aorogat/q_to_template/data
template: AlpacaInstructTemplate
train_on_input: False
seed: null
shuffle: True
batch_size: 1
# Optimizer and Scheduler
optimizer:
_component_: torch.optim.AdamW
weight_decay: 0.01
lr: 3e-4
lr_scheduler:
_component_: torchtune.modules.get_cosine_schedule_with_warmup
num_warmup_steps: 100
loss:
_component_: torch.nn.CrossEntropyLoss
# Training
epochs: 1
max_steps_per_epoch: null
gradient_accumulation_steps: 64
compile: False
# Logging
output_dir: /home/aorogat/lora_finetune_output
metric_logger:
_component_: torchtune.utils.metric_logging.DiskLogger
log_dir: ${output_dir}
log_every_n_steps: null
# Environment
device: cuda
dtype: bf16
enable_activation_checkpointing: True
# Profiler (disabled)
profiler:
_component_: torchtune.utils.profiler
enabled: False
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