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11B_lora.yaml
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11B_lora.yaml
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# Config for multi-device LoRA finetuning in lora_finetune_distributed.py
# using a Llama3.2 11B Vision Instruct model
#
# This config assumes that you've run the following command before launching:
# tune download meta-llama/Llama-3.2-11B-Vision-Instruct --output-dir /tmp/Llama-3.2-11B-Vision-Instruct
#
# To launch on 2 devices, run the following command from root:
# tune run --nproc_per_node 2 lora_finetune_distributed --config llama3_2_vision/11B_lora
#
# You can add specific overrides through the command line. For example
# to override the checkpointer directory while launching training:
# tune run --nproc_per_node 2 lora_finetune_distributed --config llama3_2_vision/11B_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 11B_lora_single_device.yaml
# or 11B_qlora_single_device.yaml
# Model arguments
model:
_component_: torchtune.models.llama3_2_vision.lora_llama3_2_vision_11b
decoder_trainable: "frozen"
encoder_trainable: "lora"
fusion_trainable: "lora"
lora_attn_modules: ['q_proj', 'v_proj']
apply_lora_to_mlp: False
apply_lora_to_output: False
lora_rank: 8
lora_alpha: 16
lora_dropout: 0.0
image_size: 560 # Make sure this matches the image_size in tokenizer
# Transform
tokenizer:
_component_: torchtune.models.llama3_2_vision.llama3_2_vision_transform
path: /tmp/Llama-3.2-11B-Vision-Instruct/original/tokenizer.model
image_size: 560
# Checkpointer
checkpointer:
_component_: torchtune.training.FullModelMetaCheckpointer
checkpoint_dir: /tmp/Llama-3.2-11B-Vision-Instruct/original/
checkpoint_files: [consolidated.pth]
recipe_checkpoint: null
output_dir: /tmp/Llama-3.2-11B-Vision-Instruct/
model_type: LLAMA3_VISION
resume_from_checkpoint: False
# Dataset
dataset:
_component_: torchtune.datasets.multimodal.the_cauldron_dataset
subset: ocrvqa
seed: null
shuffle: True
collate_fn: torchtune.data.padded_collate_tiled_images_and_mask
# Fine-tuning arguments
epochs: 1
max_steps_per_epoch: null
batch_size: 2
gradient_accumulation_steps: 4
optimizer:
_component_: torch.optim.AdamW
fused: True
weight_decay: 0.01
lr: 2e-5
lr_scheduler:
_component_: torchtune.modules.get_cosine_schedule_with_warmup
num_warmup_steps: 100
loss:
_component_: torchtune.modules.loss.CEWithChunkedOutputLoss
clip_grad_norm: 1.0
compile: False # set it to True for better memory and performance
# Training env
device: cuda
# Memory management
enable_activation_checkpointing: True
enable_activation_offloading: False
dtype: bf16
# Logging
output_dir: /tmp/full-llama3.2-vision-finetune
metric_logger:
_component_: torchtune.training.metric_logging.DiskLogger
log_dir: /tmp/Llama-3.2-11B-Vision-Instruct/logs
log_every_n_steps: 1
log_peak_memory_stats: False