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Delete xtuner_config.py

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  1. xtuner_config.py +0 -189
xtuner_config.py DELETED
@@ -1,189 +0,0 @@
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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-13b-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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- '请描述一下这张照片',
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- 'Please describe this picture',
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- ],
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- every_n_iters=500,
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- image_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-13b-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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- data_root = './data/llava_data/'
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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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- '请描述一下这张照片',
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- 'Please describe this picture',
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- ]
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- image_folder = './data/llava_data/LLaVA-Pretrain/images'
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- image_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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- launcher = 'pytorch'
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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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- image_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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- max_length=1472,
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- pad_image_to_square=False,
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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-13b-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-13b-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-13b-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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- 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-13b-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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- image_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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- max_length=1472,
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- pad_image_to_square=False,
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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-13b-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_13b_v15_clip_vit_large_p14_336_e1_gpu8_pretrain'