diff --git a/clip.py b/clip.py
index 9741d96..b039cc9 100644
--- a/clip.py
+++ b/clip.py
@@ -22,6 +22,7 @@ from towhee.operator.base import NNOperator, OperatorFlag
 from towhee.types.arg import arg, to_image_color
 from towhee import register
 from transformers import CLIPTokenizer, CLIPTextModel ,CLIPModel,CLIPProcessor
+from train_clip_with_hf_trainer import train_with_hf_trainer
 
 
 @register(output_schema=['vec'])
@@ -32,9 +33,10 @@ class Clip(NNOperator):
     def __init__(self, model_name: str, modality: str):
         self.modality = modality
         self.device = "cuda" if torch.cuda.is_available() else "cpu"
-        self.model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
-        self.tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32")
-        self.processor =  CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
+        cfg = self._configs()[model_name]
+        self.model = CLIPModel.from_pretrained(cfg)
+        self.tokenizer = CLIPTokenizer.from_pretrained(cfg)
+        self.processor =  CLIPProcessor.from_pretrained(cfg)
 
     def inference_single_data(self, data):
         if self.modality == 'image':
@@ -70,3 +72,16 @@ class Clip(NNOperator):
         inputs = processor(images=img, return_tensors="pt")
         image_features = self.model.get_image_features(**inputs)
         return image_features
+
+    def train(self, **kwargs):
+        data_args = kwargs.pop('data_args', None)
+        training_args = kwargs.pop('training_args', None)
+        train_with_hf_trainer(self.model, self.tokenizer, data_args, training_args)
+    
+    def _configs(self):
+        config = {}
+        config['clip_vit_base_32'] = 'openai/clip-vit-base-patch16'
+        config['clip_vit_base_16'] = 'openai/clip-vit-base-patch32'
+        config['clip_vit_large_14'] = 'openai/clip-vit-large-patch14'
+        config['clip_vit_large_14_336'] ='openai/clip-vit-large-patch14-336'
+        return config
diff --git a/train_clip_with_hf_trainer.py b/train_clip_with_hf_trainer.py
new file mode 100644
index 0000000..797b7f8
--- /dev/null
+++ b/train_clip_with_hf_trainer.py
@@ -0,0 +1,470 @@
+import logging
+import os
+import sys
+import transformers
+import dataclasses
+import ipdb
+from dataclasses import dataclass, field
+from typing import Optional, List
+
+import torch
+from datasets import load_dataset
+from PIL import Image
+from torchvision.io import ImageReadMode, read_image
+from torchvision.transforms import CenterCrop, ConvertImageDtype, Normalize, Resize
+from torchvision.transforms.functional import InterpolationMode
+
+from transformers import (
+    MODEL_FOR_CAUSAL_LM_MAPPING,
+    TrainingArguments,
+    default_data_collator,
+    is_torch_tpu_available,
+    set_seed,
+)
+
+# We use torchvision for faster image pre-processing. The transforms are implemented as nn.Module,
+# so we jit it to be faster.
+
+
+logger = logging.getLogger(__name__)
+
+dataset_name_mapping = {
+    "image_caption_dataset.py": ("image_path", "caption"),
+}
+
+
+def dataclass_from_dict(klass, d):
+    try:
+        fieldtypes = {f.name: f.type for f in dataclasses.fields(klass)}
+        return klass(**{f: dataclass_from_dict(fieldtypes[f], d[f]) for f in d})
+    except:
+        return d  # Not a dataclass field
+
+
+@dataclass
+class DataTrainingArguments:
+    """
+    Arguments pertaining to what data we are going to input our model for training and eval.
+    """
+
+    dataset_name: Optional[str] = field(
+        default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
+    )
+    dataset_config_name: Optional[str] = field(
+        default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
+    )
+    data_dir: Optional[str] = field(default=None, metadata={"help": "The data directory containing input files."})
+    image_column: Optional[str] = field(
+        default="image_path",
+        metadata={"help": "The name of the column in the datasets containing the full image file paths."},
+    )
+    caption_column: Optional[str] = field(
+        default="caption",
+        metadata={"help": "The name of the column in the datasets containing the image captions."},
+    )
+    train_file: Optional[str] = field(
+        default=None, metadata={"help": "The input training data file (a jsonlines file)."}
+    )
+    validation_file: Optional[str] = field(
+        default=None,
+        metadata={"help": "An optional input evaluation data file (a jsonlines file)."},
+    )
+    max_seq_length: Optional[int] = field(
+        default=77,
+        metadata={
+            "help": (
+                "The maximum total input sequence length after tokenization. Sequences longer "
+                "than this will be truncated, sequences shorter will be padded."
+            )
+        },
+    )
+    max_train_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": (
+                "For debugging purposes or quicker training, truncate the number of training examples to this "
+                "value if set."
+            )
+        },
+    )
+    max_eval_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": (
+                "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
+                "value if set."
+            )
+        },
+    )
+    overwrite_cache: bool = field(
+        default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
+    )
+    preprocessing_num_workers: Optional[int] = field(
+        default=None,
+        metadata={"help": "The number of processes to use for the preprocessing."},
+    )
+    cache_dir: Optional[str] = field(
+        default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
+    )
+    image_mean: Optional[str] = field(
+        default=None, metadata={"help": "image preprocessing mean"}
+    )
+    image_std: Optional[str] = field(
+        default=None, metadata={"help": "image preprocessing std"}
+    )
+
+    def __post_init__(self):
+        if self.dataset_name is None and self.train_file is None and self.validation_file is None:
+            raise ValueError("Need either a dataset name or a training/validation file.")
+        else:
+            if self.train_file is not None:
+                extension = self.train_file.split(".")[-1]
+                assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
+            if self.validation_file is not None:
+                extension = self.validation_file.split(".")[-1]
+                assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
+            if self.validation_file is not None:
+                extension = self.validation_file.split(".")[-1]
+                assert extension == "json", "`validation_file` should be a json file."
+
+
+class Transform(torch.nn.Module):
+    def __init__(self, image_size, mean, std):
+        super().__init__()
+        self.transforms = torch.nn.Sequential(
+            Resize([image_size], interpolation=InterpolationMode.BICUBIC),
+            CenterCrop(image_size),
+            ConvertImageDtype(torch.float),
+            Normalize(mean, std),
+        )
+
+    def forward(self, x) -> torch.Tensor:
+        """`x` should be an instance of `PIL.Image.Image`"""
+        with torch.no_grad():
+            x = self.transforms(x)
+        return x
+
+def collate_fn(examples):
+    pixel_values = torch.stack([example["pixel_values"] for example in examples])
+    input_ids = torch.tensor([example["input_ids"] for example in examples], dtype=torch.long)
+    attention_mask = torch.tensor([example["attention_mask"] for example in examples], dtype=torch.long)
+    return {
+        "pixel_values": pixel_values,
+        "input_ids": input_ids,
+        "attention_mask": attention_mask,
+        "return_loss": True,
+    }
+
+
+def train_with_hf_trainer(model, tokenizer, data_args, training_args, **kwargs):
+
+    import evaluate
+    import datasets
+
+    from transformers import Trainer
+    from datasets import load_dataset
+    from towhee.trainer.training_config import get_dataclasses_help
+
+    print('**** DataTrainingArguments ****')
+    get_dataclasses_help(DataTrainingArguments)
+    data_args = dataclass_from_dict(DataTrainingArguments, data_args)
+
+    print('**** TrainingArguments ****')
+    get_dataclasses_help(TrainingArguments)
+    training_args = dataclass_from_dict(TrainingArguments, training_args)
+
+    # 2. Setup logging
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        handlers=[logging.StreamHandler(sys.stdout)],
+    ) 
+
+    # 2. Setup logging
+    #+ training_args
+    #log_level = training_args.get_process_log_level()
+    log_level = training_args.get_process_log_level()
+    logger.setLevel(log_level)
+    transformers.utils.logging.set_verbosity(log_level)
+    transformers.utils.logging.enable_default_handler()
+    transformers.utils.logging.enable_explicit_format()
+
+    #!!!!!!!!
+    temp_cache_dir = data_args.cache_dir
+    # Log on each process the small summary:
+    #training_args
+    # +local_rank
+    # +device
+    # +n_gpu
+    # +local_rank
+    # +fp16
+    logger.warning(
+        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+        + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
+    )
+    logger.info(f"Training/evaluation parameters {training_args}")
+    
+    # 3. Detecting last checkpoint and eventualy continue from last checkpoint
+    ### place holder ###
+    ### place holder ###
+    ### place holder ###
+
+
+    # 4. Load dataset
+    # Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
+    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
+    # (the dataset will be downloaded automatically from the datasets Hub).
+    #
+    # For CSV/JSON files this script will use the first column for the full image path and the second column for the
+    # captions (unless you specify column names for this with the `image_column` and `caption_column` arguments).
+    #
+
+    #data_args
+    # +dataset_name
+    # +dataset_config_name
+    # +cache_dir
+    # +data_dir
+    # +train_file
+    # +validation_file
+    # +test_file
+    #model_args
+    # +use_auth_token 
+    # +cache_dir
+    if data_args.dataset_name is not None:
+        # Downloading and loading a dataset from the hub.
+        dataset = load_dataset(
+            data_args.dataset_name,
+            data_args.dataset_config_name,
+            cache_dir=temp_cache_dir,
+            keep_in_memory=False,
+            data_dir=data_args.data_dir,
+        #    use_auth_token=True if model_args.use_auth_token else None,
+        )
+    else:
+        data_files = {}
+        if data_args.train_file is not None:
+            data_files["train"] = data_args.train_file
+            extension = data_args.train_file.split(".")[-1]
+        if data_args.validation_file is not None:
+            data_files["validation"] = data_args.validation_file
+            extension = data_args.validation_file.split(".")[-1]
+        if data_args.test_file is not None:
+            data_files["test"] = data_args.test_file
+            extension = data_args.test_file.split(".")[-1]
+        dataset = load_dataset(
+            extension,
+            data_files=data_files,
+            cache_dir=temp_cache_dir,
+        #    use_auth_token=True if model_args.use_auth_token else None,
+        )
+    # 5. Load pretrained model, tokenizer, and feature extractor
+    #model_args
+    # +tokenizer_name
+    # +cache_dir
+    # +use_fast_tokenizer
+
+    #@if model_args.tokenizer_name:
+    #@    tokenizer = AutoTokenizer.from_pretrained(
+    #@        model_args.tokenizer_name, cache_dir=temp_cache_dir, use_fast=model_args.use_fast_tokenizer
+    #@    )
+    #@# --- for CLIP, tokenizer is fixed
+
+    # Load feature_extractor, in this script we only use this to get the mean and std for normalization.
+    #model_args
+    # +feature_extractor_name
+    # +model_name_or_path
+    # +cache_dir
+    # +model_revision
+    # +use_auth_token
+
+    #@feature_extractor = AutoFeatureExtractor.from_pretrained(
+    #@    model_args.feature_extractor_name or model_args.model_name_or_path,
+    #@    cache_dir=temp_cache_dir,
+    #@    revision=model_args.model_revision,
+    #@    use_auth_token=True if model_args.use_auth_token else None,
+    #@)
+    # 只为了拿到mean 和 std
+
+    # load model
+    #@model = AutoModel.from_pretrained()
+    config = model.config
+
+    #选择text或者vision freeze住
+    #model_args
+    # +freeze_vision_model
+    # +freeze_text_model
+    def _freeze_params(module):
+        for param in module.parameters():
+                param.requires_grad = False
+
+        if model_args.freeze_vision_model:
+            _freeze_params(model.vision_model)
+
+        if model_args.freeze_text_model:
+            _freeze_params(model.text_model)
+    #!!!!!!! freeze
+    freeze_vision_model = False
+    freeze_text_model = False
+    if freeze_vision_model is True:
+        _freeze_params(model.vision_model)
+
+    if freeze_text_model is True:
+        _freeze_params(model.text_model)
+
+    set_seed(training_args.seed)
+
+    if training_args.do_train:
+        column_names = dataset["train"].column_names
+    elif training_args.do_eval:
+        column_names = dataset["validation"].column_names
+    elif training_args.do_predict:
+        column_names = dataset["test"].column_names
+    else:
+        logger.info("There is nothing to do. Please pass `do_train`, `do_eval` and/or `do_predict`.")
+        return
+
+    dataset_columns = dataset_name_mapping.get(data_args.dataset_name, None)
+    if data_args.image_column is None:
+        image_column = dataset_columns[0] if dataset_columns is not None else column_names[0]
+    else:
+        image_column = data_args.image_column
+        if image_column not in column_names:
+            raise ValueError(
+                f"--image_column' value '{data_args.image_column}' needs to be one of: {', '.join(column_names)}"
+            )
+    if data_args.caption_column is None:
+        caption_column = dataset_columns[1] if dataset_columns is not None else column_names[1]
+    else:
+        caption_column = data_args.caption_column
+        if caption_column not in column_names:
+            raise ValueError(
+                f"--caption_column' value '{data_args.caption_column}' needs to be one of: {', '.join(column_names)}"
+            )
+
+
+    #image_column = 'image_path'
+    #caption_column = 'caption'
+
+    image_mean, image_std = data_args.image_mean, data_args.image_std
+    # 7. Preprocessing the datasets.
+    # Initialize torchvision transforms and jit it for faster processing.
+    #这个地方需要image_size,image_mean,image_std
+    image_transformations = Transform(
+        config.vision_config.image_size, image_mean, image_std
+    )
+    image_transformations = torch.jit.script(image_transformations)
+
+    # Preprocessing the datasets.
+    # We need to tokenize input captions and transform the images.
+    #data_args
+    # +max_seq_length
+    #caption_column
+    #!!!!!!!!!!!!!!!!!
+
+    #from transformers import CLIPTokenizer, CLIPTextModel ,CLIPModel,CLIPProcessor
+    #tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32")
+
+    def tokenize_captions(examples):
+        captions = [caption for caption in examples[caption_column]]
+        text_inputs = tokenizer(captions, max_length=data_args.max_seq_length, padding="max_length", truncation=True)
+        examples["input_ids"] = text_inputs.input_ids
+        examples["attention_mask"] = text_inputs.attention_mask
+        return examples
+
+    def transform_images(examples):
+        images = [read_image(image_file, mode=ImageReadMode.RGB) for image_file in examples[image_column]]
+        examples["pixel_values"] = [image_transformations(image) for image in images]
+        return examples
+
+    def filter_corrupt_images(examples):
+        """remove problematic images"""
+        valid_images = []
+        for image_file in examples[image_column]:
+            try:
+                Image.open(image_file)
+                valid_images.append(True)
+            except Exception:
+                valid_images.append(False)
+        return valid_images
+    
+    if training_args.do_train:
+        if "train" not in dataset:
+            raise ValueError("--do_train requires a train dataset")
+        train_dataset = dataset["train"]
+        if data_args.max_train_samples is not None:
+            max_train_samples = min(len(train_dataset), data_args.max_train_samples)
+            train_dataset = train_dataset.select(range(max_train_samples))
+        train_dataset = train_dataset.filter(
+            filter_corrupt_images, batched=True, num_proc=data_args.preprocessing_num_workers
+        )
+        train_dataset = train_dataset.map(
+            function=tokenize_captions,
+            batched=True,
+            remove_columns=[col for col in column_names if col != image_column],
+            num_proc=data_args.preprocessing_num_workers,
+            load_from_cache_file=not data_args.overwrite_cache,
+            desc="Running tokenizer on train dataset",
+        )
+
+        # Transform images on the fly as doing it on the whole dataset takes too much time.
+        train_dataset.set_transform(transform_images)
+    #training_args 
+    # +do_eval
+    #data_args
+    # +max_eval_samples
+    # +preprocessing_num_workers
+    # +overwrite_cache
+    if training_args.do_eval:
+        if "validation" not in dataset:
+            raise ValueError("--do_eval requires a train validation")
+        eval_dataset = dataset["validation"]
+        if data_args.max_eval_samples is not None:
+            max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
+            eval_dataset = eval_dataset.select(range(max_eval_samples))
+
+        eval_dataset = eval_dataset.filter(
+            filter_corrupt_images, batched=True, num_proc=data_args.preprocessing_num_workers
+        )
+        eval_dataset = eval_dataset.map(
+            function=tokenize_captions,
+            batched=True,
+            num_proc=data_args.preprocessing_num_workers,
+            remove_columns=[col for col in column_names if col != image_column],
+            load_from_cache_file=not data_args.overwrite_cache,
+            desc="Running tokenizer on validation dataset",
+        )
+
+        # Transform images on the fly as doing it on the whole dataset takes too much time.
+        eval_dataset.set_transform(transform_images)
+
+    # 8. Initalize our trainer
+    trainer = Trainer(
+        model=model,
+        args=training_args,
+        train_dataset=train_dataset if training_args.do_train else None,
+        eval_dataset=eval_dataset if training_args.do_eval else None,
+        data_collator=collate_fn,
+    )
+
+    # Training
+    last_checkpoint = None
+    if training_args.do_train:
+        checkpoint = None
+        if training_args.resume_from_checkpoint is not None:
+            checkpoint = training_args.resume_from_checkpoint
+        elif last_checkpoint is not None:
+            checkpoint = last_checkpoint
+        train_result = trainer.train(resume_from_checkpoint=checkpoint)
+        trainer.save_model()
+        trainer.log_metrics("train", train_result.metrics)
+        trainer.save_metrics("train", train_result.metrics)
+        trainer.save_state()
+    #training_args
+    # +do_eval
+    # 10. Evaluation
+    if training_args.do_eval:
+        metrics = trainer.evaluate()
+        trainer.log_metrics("eval", metrics)
+        trainer.save_metrics("eval", metrics)
+
+