| SYSTEM = '' |
| accumulative_counts = 2 |
| batch_size = 8 |
| betas = ( |
| 0.9, |
| 0.999, |
| ) |
| custom_hooks = [ |
| dict( |
| tokenizer=dict( |
| padding_side='right', |
| pretrained_model_name_or_path='internlm/internlm2-1_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/internlm2-1_8b', |
| trust_remote_code=True, |
| type='transformers.AutoTokenizer.from_pretrained'), |
| type='xtuner.engine.hooks.EvaluateChatHook'), |
| ] |
| data_path = './llava_data/llava_v1_5_lrv_mix1008k.json' |
| data_root = './llava_data/' |
| 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')) |
| dino_path = 'facebook/dinov2-large' |
| 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_data/llava_images' |
| image_processor = dict( |
| pretrained_model_name_or_path='google/siglip-so400m-patch14-384', |
| trust_remote_code=True, |
| type='transformers.SiglipImageProcessor.from_pretrained') |
| image_processor_path = 'google/siglip-so400m-patch14-384' |
| launcher = 'pytorch' |
| llava_dataset = dict( |
| data_path='./llava_data/llava_v1_5_lrv_mix1008k.json', |
| dataset_map_fn='xtuner.dataset.map_fns.llava_map_fn', |
| image_folder='./llava_data/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=False, |
| 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/internlm2-1_8b', |
| trust_remote_code=True, |
| type='transformers.AutoTokenizer.from_pretrained'), |
| type='xtuner.dataset.LLaVADataset') |
| llm_name_or_path = 'internlm/internlm2-1_8b' |
| load_from = None |
| log_level = 'INFO' |
| log_processor = dict(by_epoch=False) |
| lr = 2e-05 |
| max_epochs = 2 |
| max_length = 1472 |
| max_norm = 1 |
| model = dict( |
| dino=dict( |
| pretrained_model_name_or_path='facebook/dinov2-large', |
| type='transformers.Dinov2Model.from_pretrained'), |
| freeze_llm=False, |
| freeze_visual_encoder=True, |
| llm=dict( |
| pretrained_model_name_or_path='internlm/internlm2-1_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'), |
| siglip=dict( |
| pretrained_model_name_or_path='google/siglip-so400m-patch14-384', |
| type='transformers.SiglipVisionModel.from_pretrained'), |
| type='xtuner.model.LLaVAModel') |
| optim_type = 'torch.optim.AdamW' |
| optim_wrapper = dict( |
| optimizer=dict( |
| betas=( |
| 0.9, |
| 0.999, |
| ), |
| lr=2e-05, |
| type='torch.optim.AdamW', |
| weight_decay=0.1), |
| type='DeepSpeedOptimWrapper') |
| param_scheduler = [ |
| dict( |
| begin=0, |
| by_epoch=True, |
| convert_to_iter_based=True, |
| end=0.06, |
| start_factor=1e-05, |
| type='mmengine.optim.LinearLR'), |
| dict( |
| begin=0.06, |
| by_epoch=True, |
| convert_to_iter_based=True, |
| end=2, |
| eta_min=0.0, |
| type='mmengine.optim.CosineAnnealingLR'), |
| ] |
| 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 |
| siglip_path = 'google/siglip-so400m-patch14-384' |
| 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=2, |
| gradient_clipping=1, |
| train_micro_batch_size_per_gpu=8, |
| type='xtuner.engine.DeepSpeedStrategy') |
| tokenizer = dict( |
| padding_side='right', |
| pretrained_model_name_or_path='internlm/internlm2-1_8b', |
| trust_remote_code=True, |
| type='transformers.AutoTokenizer.from_pretrained') |
| train_cfg = dict(max_epochs=2, type='xtuner.engine.runner.TrainLoop') |
| train_dataloader = dict( |
| batch_size=8, |
| collate_fn=dict(type='xtuner.dataset.collate_fns.default_collate_fn'), |
| dataset=dict( |
| data_path='./llava_data/llava_v1_5_lrv_mix1008k.json', |
| dataset_map_fn='xtuner.dataset.map_fns.llava_map_fn', |
| image_folder='./llava_data/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=False, |
| 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/internlm2-1_8b', |
| trust_remote_code=True, |
| type='transformers.AutoTokenizer.from_pretrained'), |
| type='xtuner.dataset.LLaVADataset'), |
| num_workers=4, |
| sampler=dict(shuffle=True, type='mmengine.dataset.DefaultSampler')) |
| visualizer = dict( |
| type='mmengine.visualization.Visualizer', |
| vis_backends=[ |
| dict(type='mmengine.visualization.TensorboardVisBackend'), |
| ]) |
| warmup_ratio = 0.03 |
| weight_decay = 0.1 |
| work_dir = './work_dirs/train_config' |
|
|
