first commit
Browse files- epoch_1.pth +3 -0
- xtuner_config.py +187 -0
epoch_1.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:f0a53fea99a12930a0518199ffd5f1703f24b0f086204b559f032ebbbe7561d2
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size 83920363
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xtuner_config.py
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SYSTEM = ''
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accumulative_counts = 1
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batch_size = 32
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betas = (
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0.9,
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0.999,
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)
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custom_hooks = [
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dict(
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tokenizer=dict(
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padding_side='right',
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pretrained_model_name_or_path='lmsys/vicuna-7b-v1.5',
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trust_remote_code=True,
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type='transformers.AutoTokenizer.from_pretrained'),
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type='xtuner.engine.DatasetInfoHook'),
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dict(
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evaluation_images='https://llava-vl.github.io/static/images/view.jpg',
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evaluation_inputs=[
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'',
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],
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every_n_iters=500,
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processor=dict(
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pretrained_model_name_or_path='openai/clip-vit-large-patch14-336',
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trust_remote_code=True,
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type='transformers.CLIPImageProcessor.from_pretrained'),
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prompt_template='xtuner.utils.PROMPT_TEMPLATE.vicuna',
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system='',
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tokenizer=dict(
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padding_side='right',
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pretrained_model_name_or_path='lmsys/vicuna-7b-v1.5',
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trust_remote_code=True,
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type='transformers.AutoTokenizer.from_pretrained'),
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type='xtuner.engine.EvaluateChatHook'),
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]
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data_path = './data/llava_data/LLaVA-Pretrain/blip_laion_cc_sbu_558k.json'
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dataloader_num_workers = 0
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default_hooks = dict(
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checkpoint=dict(interval=1, type='mmengine.hooks.CheckpointHook'),
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logger=dict(interval=10, type='mmengine.hooks.LoggerHook'),
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param_scheduler=dict(type='mmengine.hooks.ParamSchedulerHook'),
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sampler_seed=dict(type='mmengine.hooks.DistSamplerSeedHook'),
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timer=dict(type='mmengine.hooks.IterTimerHook'))
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env_cfg = dict(
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cudnn_benchmark=False,
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dist_cfg=dict(backend='nccl'),
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mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0))
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evaluation_freq = 500
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evaluation_images = 'https://llava-vl.github.io/static/images/view.jpg'
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evaluation_inputs = [
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'',
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]
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image_folder = './data/llava_data/LLaVA-Pretrain/images'
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launcher = 'pytorch'
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llava_data_root = './data/llava_data/'
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llava_dataset = dict(
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data_path='./data/llava_data/LLaVA-Pretrain/blip_laion_cc_sbu_558k.json',
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dataset_map_fn='xtuner.dataset.map_fns.llava_map_fn',
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image_folder='./data/llava_data/LLaVA-Pretrain/images',
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max_length=1472,
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pad_image_to_square=False,
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processor=dict(
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pretrained_model_name_or_path='openai/clip-vit-large-patch14-336',
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trust_remote_code=True,
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type='transformers.CLIPImageProcessor.from_pretrained'),
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template_map_fn=dict(
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template='xtuner.utils.PROMPT_TEMPLATE.vicuna',
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type='xtuner.dataset.map_fns.template_map_fn_factory'),
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tokenizer=dict(
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padding_side='right',
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pretrained_model_name_or_path='lmsys/vicuna-7b-v1.5',
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trust_remote_code=True,
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type='transformers.AutoTokenizer.from_pretrained'),
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type='xtuner.dataset.LLaVADataset')
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llm_name_or_path = 'lmsys/vicuna-7b-v1.5'
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load_from = None
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log_level = 'INFO'
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lr = 0.001
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max_epochs = 1
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max_length = 1472
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max_norm = 1
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model = dict(
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freeze_llm=True,
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freeze_visual_encoder=True,
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llm=dict(
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pretrained_model_name_or_path='lmsys/vicuna-7b-v1.5',
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quantization_config=dict(
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bnb_4bit_compute_dtype='torch.float16',
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bnb_4bit_quant_type='nf4',
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bnb_4bit_use_double_quant=True,
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llm_int8_has_fp16_weight=False,
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llm_int8_threshold=6.0,
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load_in_4bit=True,
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load_in_8bit=False,
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type='transformers.BitsAndBytesConfig'),
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torch_dtype='torch.float16',
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trust_remote_code=True,
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type='transformers.AutoModelForCausalLM.from_pretrained'),
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type='xtuner.model.LLaVAModel',
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visual_encoder=dict(
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pretrained_model_name_or_path='openai/clip-vit-large-patch14-336',
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type='transformers.CLIPVisionModel.from_pretrained'))
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optim_type = 'torch.optim.AdamW'
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optim_wrapper = dict(
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optimizer=dict(
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betas=(
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0.9,
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0.999,
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),
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lr=0.001,
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type='torch.optim.AdamW',
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weight_decay=0),
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type='DeepSpeedOptimWrapper')
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param_scheduler = [
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dict(
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begin=0,
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by_epoch=True,
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convert_to_iter_based=True,
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end=0.03,
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start_factor=1e-05,
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type='mmengine.optim.LinearLR'),
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dict(
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T_max=1,
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begin=0.03,
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by_epoch=True,
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convert_to_iter_based=True,
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eta_min=0.0,
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type='mmengine.optim.CosineAnnealingLR'),
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]
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processor = dict(
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pretrained_model_name_or_path='openai/clip-vit-large-patch14-336',
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trust_remote_code=True,
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type='transformers.CLIPImageProcessor.from_pretrained')
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prompt_template = 'xtuner.utils.PROMPT_TEMPLATE.vicuna'
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randomness = dict(deterministic=False, seed=None)
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resume = False
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runner_type = 'FlexibleRunner'
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strategy = dict(
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config=dict(
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bf16=dict(enabled=True),
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fp16=dict(enabled=False, initial_scale_power=16),
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gradient_accumulation_steps='auto',
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gradient_clipping='auto',
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train_micro_batch_size_per_gpu='auto',
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zero_allow_untested_optimizer=True,
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zero_force_ds_cpu_optimizer=False,
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zero_optimization=dict(overlap_comm=True, stage=2)),
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exclude_frozen_parameters=True,
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gradient_accumulation_steps=1,
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gradient_clipping=1,
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train_micro_batch_size_per_gpu=32,
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type='xtuner.engine.DeepSpeedStrategy')
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tokenizer = dict(
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padding_side='right',
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pretrained_model_name_or_path='lmsys/vicuna-7b-v1.5',
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trust_remote_code=True,
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type='transformers.AutoTokenizer.from_pretrained')
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train_cfg = dict(by_epoch=True, max_epochs=1, val_interval=1)
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train_dataloader = dict(
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batch_size=32,
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collate_fn=dict(type='xtuner.dataset.collate_fns.default_collate_fn'),
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dataset=dict(
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data_path=
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'./data/llava_data/LLaVA-Pretrain/blip_laion_cc_sbu_558k.json',
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dataset_map_fn='xtuner.dataset.map_fns.llava_map_fn',
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image_folder='./data/llava_data/LLaVA-Pretrain/images',
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max_length=1472,
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pad_image_to_square=False,
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processor=dict(
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pretrained_model_name_or_path='openai/clip-vit-large-patch14-336',
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trust_remote_code=True,
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type='transformers.CLIPImageProcessor.from_pretrained'),
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template_map_fn=dict(
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template='xtuner.utils.PROMPT_TEMPLATE.vicuna',
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type='xtuner.dataset.map_fns.template_map_fn_factory'),
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tokenizer=dict(
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padding_side='right',
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pretrained_model_name_or_path='lmsys/vicuna-7b-v1.5',
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trust_remote_code=True,
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type='transformers.AutoTokenizer.from_pretrained'),
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type='xtuner.dataset.LLaVADataset'),
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num_workers=0,
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sampler=dict(shuffle=True, type='mmengine.dataset.DefaultSampler'))
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visual_encoder_name_or_path = 'openai/clip-vit-large-patch14-336'
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visualizer = None
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warmup_ratio = 0.03
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weight_decay = 0
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work_dir = './work_dirs/llava_vicuna_7b_v15_clip_vit_large_p14_336_e1_gpu8_pretrain'
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