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# Config for multi-device LoRA finetuning in lora_finetune_distributed.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 2 devices, run the following command from root:
# tune run --nproc_per_node 2 lora_finetune_distributed --config llama3/8B_lora
#
# 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 2 lora_finetune_distributed --config llama3/8B_lora checkpointer.checkpoint_dir=<YOUR_CHECKPOINT_DIR>
#
# This config works best when the model is being fine-tuned on 2+ GPUs.
# For single device LoRA finetuning please use 8B_lora_single_device.yaml
# or 8B_qlora_single_device.yaml
# Tokenizer
tokenizer:
_component_: torchtune.models.llama3.llama3_tokenizer
path: ./model/original/tokenizer.model
# 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
checkpointer:
_component_: torchtune.utils.FullModelMetaCheckpointer
checkpoint_dir: ./model/original/
checkpoint_files: [
consolidated.00.pth
]
recipe_checkpoint: null
output_dir: ./finetuned_model/
model_type: LLAMA3
resume_from_checkpoint: False
# Dataset and Sampler
# InstructDataset(
# tokenizer=tokenizer,
# source=source,
# template=GrammarErrorCorrectionTemplate,
# column_map={"sentence": "input"},
# train_on_input=train_on_input,
# split="train",
# )
dataset:
_component_: torchtune.datasets.instruct_dataset
source: grammarly/coedit
template: GrammarErrorCorrectionTemplate
column_map: {"sentence": "src", "output": "tgt"}
train_on_input: False
split: train
seed: 123
shuffle: True
batch_size: 4
# 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: 2
max_steps_per_epoch: null
gradient_accumulation_steps: 32
# Logging
output_dir: ./lora_finetune_output
metric_logger:
_component_: torchtune.utils.metric_logging.WandBLogger
project: torchtune
group: llama3-grammarly
log_every_n_steps: null
# Environment
device: cuda
dtype: bf16
enable_activation_checkpointing: False
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