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SYSTEM = ''
accumulative_counts = 8
batch_size = 4
betas = (
0.9,
0.999,
)
custom_hooks = [
dict(
tokenizer=dict(
padding_side='right',
pretrained_model_name_or_path='internlm/internlm2-chat-1_8b',
trust_remote_code=True,
type='transformers.AutoTokenizer.from_pretrained'),
type='xtuner.engine.hooks.DatasetInfoHook'),
dict(
evaluation_images='https://llava-vl.github.io/static/images/view.jpg',
evaluation_inputs=[
'请描述一下这张照片',
'Please describe this picture',
],
every_n_iters=500,
image_processor=dict(
pretrained_model_name_or_path='google/siglip-so400m-patch14-384',
trust_remote_code=True,
type='transformers.SiglipImageProcessor.from_pretrained'),
prompt_template='xtuner.utils.PROMPT_TEMPLATE.internlm2_chat',
system='',
tokenizer=dict(
padding_side='right',
pretrained_model_name_or_path='internlm/internlm2-chat-1_8b',
trust_remote_code=True,
type='transformers.AutoTokenizer.from_pretrained'),
type='xtuner.engine.hooks.EvaluateChatHook'),
]
data_path = './LLaVA-Instruct-150K/llava_v1_5_mix665k.json'
data_root = './'
dataloader_num_workers = 4
default_hooks = dict(
checkpoint=dict(
by_epoch=False,
interval=500,
max_keep_ckpts=2,
type='mmengine.hooks.CheckpointHook'),
logger=dict(
interval=10,
log_metric_by_epoch=False,
type='mmengine.hooks.LoggerHook'),
param_scheduler=dict(type='mmengine.hooks.ParamSchedulerHook'),
sampler_seed=dict(type='mmengine.hooks.DistSamplerSeedHook'),
timer=dict(type='mmengine.hooks.IterTimerHook'))
env_cfg = dict(
cudnn_benchmark=False,
dist_cfg=dict(backend='nccl'),
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0))
evaluation_freq = 500
evaluation_images = 'https://llava-vl.github.io/static/images/view.jpg'
evaluation_inputs = [
'请描述一下这张照片',
'Please describe this picture',
]
image_folder = './llava_images'
image_processor = dict(
pretrained_model_name_or_path='google/siglip-so400m-patch14-384',
trust_remote_code=True,
type='transformers.SiglipImageProcessor.from_pretrained')
launcher = 'pytorch'
llava_dataset = dict(
data_path='./LLaVA-Instruct-150K/llava_v1_5_mix665k.json',
dataset_map_fn='xtuner.dataset.map_fns.llava_map_fn',
image_folder='./llava_images',
image_processor=dict(
pretrained_model_name_or_path='google/siglip-so400m-patch14-384',
trust_remote_code=True,
type='transformers.SiglipImageProcessor.from_pretrained'),
max_length=1472,
pad_image_to_square=True,
template_map_fn=dict(
template='xtuner.utils.PROMPT_TEMPLATE.internlm2_chat',
type='xtuner.dataset.map_fns.template_map_fn_factory'),
tokenizer=dict(
padding_side='right',
pretrained_model_name_or_path='internlm/internlm2-chat-1_8b',
trust_remote_code=True,
type='transformers.AutoTokenizer.from_pretrained'),
type='xtuner.dataset.LLaVADataset')
llm_name_or_path = 'internlm/internlm2-chat-1_8b'
load_from = None
log_level = 'INFO'
log_processor = dict(by_epoch=False)
lr = 0.0002
max_epochs = 1
max_length = 1472
max_norm = 1
model = dict(
freeze_llm=True,
freeze_visual_encoder=True,
llm=dict(
pretrained_model_name_or_path='internlm/internlm2-chat-1_8b',
quantization_config=dict(
bnb_4bit_compute_dtype='torch.float16',
bnb_4bit_quant_type='nf4',
bnb_4bit_use_double_quant=True,
llm_int8_has_fp16_weight=False,
llm_int8_threshold=6.0,
load_in_4bit=True,
load_in_8bit=False,
type='transformers.BitsAndBytesConfig'),
torch_dtype='torch.float16',
trust_remote_code=True,
type='transformers.AutoModelForCausalLM.from_pretrained'),
llm_lora=dict(
bias='none',
lora_alpha=256,
lora_dropout=0.05,
r=512,
task_type='CAUSAL_LM',
type='peft.LoraConfig'),
pretrained_pth='./work_dirs/pretrain/iter_8721.pth',
type='xtuner.model.LLaVAModel',
visual_encoder=dict(
pretrained_model_name_or_path='google/siglip-so400m-patch14-384',
type='transformers.SiglipVisionModel.from_pretrained'),
visual_encoder_lora=dict(
bias='none',
lora_alpha=16,
lora_dropout=0.05,
r=64,
type='peft.LoraConfig'))
optim_type = 'torch.optim.AdamW'
optim_wrapper = dict(
optimizer=dict(
betas=(
0.9,
0.999,
),
lr=0.0002,
type='torch.optim.AdamW',
weight_decay=0),
type='DeepSpeedOptimWrapper')
param_scheduler = [
dict(
begin=0,
by_epoch=True,
convert_to_iter_based=True,
end=0.03,
start_factor=1e-05,
type='mmengine.optim.LinearLR'),
dict(
begin=0.03,
by_epoch=True,
convert_to_iter_based=True,
end=1,
eta_min=0.0,
type='mmengine.optim.CosineAnnealingLR'),
]
prefetch = 5
pretrained_pth = './work_dirs/pretrain/iter_8721.pth'
prompt_template = 'xtuner.utils.PROMPT_TEMPLATE.internlm2_chat'
randomness = dict(deterministic=False, seed=None)
resume = False
runner_type = 'FlexibleRunner'
save_steps = 500
save_total_limit = 2
strategy = dict(
config=dict(
bf16=dict(enabled=True),
fp16=dict(enabled=False, initial_scale_power=16),
gradient_accumulation_steps='auto',
gradient_clipping='auto',
train_micro_batch_size_per_gpu='auto',
zero_allow_untested_optimizer=True,
zero_force_ds_cpu_optimizer=False,
zero_optimization=dict(overlap_comm=True, stage=2)),
exclude_frozen_parameters=True,
gradient_accumulation_steps=8,
gradient_clipping=1,
train_micro_batch_size_per_gpu=4,
type='xtuner.engine.DeepSpeedStrategy')
tokenizer = dict(
padding_side='right',
pretrained_model_name_or_path='internlm/internlm2-chat-1_8b',
trust_remote_code=True,
type='transformers.AutoTokenizer.from_pretrained')
train_cfg = dict(max_epochs=1, type='xtuner.engine.runner.TrainLoop')
train_dataloader = dict(
batch_size=4,
collate_fn=dict(type='xtuner.dataset.collate_fns.default_collate_fn'),
dataset=dict(
data_path='./LLaVA-Instruct-150K/llava_v1_5_mix665k.json',
dataset_map_fn='xtuner.dataset.map_fns.llava_map_fn',
image_folder='./llava_images',
image_processor=dict(
pretrained_model_name_or_path='google/siglip-so400m-patch14-384',
trust_remote_code=True,
type='transformers.SiglipImageProcessor.from_pretrained'),
max_length=1472,
pad_image_to_square=True,
template_map_fn=dict(
template='xtuner.utils.PROMPT_TEMPLATE.internlm2_chat',
type='xtuner.dataset.map_fns.template_map_fn_factory'),
tokenizer=dict(
padding_side='right',
pretrained_model_name_or_path='internlm/internlm2-chat-1_8b',
trust_remote_code=True,
type='transformers.AutoTokenizer.from_pretrained'),
type='xtuner.dataset.LLaVADataset'),
num_workers=4,
prefetch_factor=5,
sampler=dict(
length_property='modality_length',
per_device_batch_size=32,
type='xtuner.dataset.samplers.LengthGroupedSampler'))
visual_encoder_name_or_path = 'google/siglip-so400m-patch14-384'
visualizer = dict(
type='mmengine.visualization.Visualizer',
vis_backends=[
dict(type='mmengine.visualization.TensorboardVisBackend'),
])
warmup_ratio = 0.03
weight_decay = 0
work_dir = './work_dirs/finetune'
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