diff --git a/chatllms/configs/data_args.py b/chatllms/configs/data_args.py
index 4bbac1b..9682923 100644
--- a/chatllms/configs/data_args.py
+++ b/chatllms/configs/data_args.py
@@ -11,12 +11,14 @@ class DatasetAttr(object):
     dataset_name: Optional[str] = None
     hf_hub_url: Optional[str] = None
     local_path: Optional[str] = None
+    dataset_format: Optional[str] = None
     dataset_sha1: Optional[str] = None
     load_from_local: bool = False
     multi_turn: Optional[bool] = False
 
     def __repr__(self) -> str:
-        return self.dataset_name
+        rep = f'dataset_name: {self.dataset_name},  hf_hub_url: {self.hf_hub_url}, local_path: {self.local_path}, data_formate:{self.dataset_format}   load_from_local: {self.load_from_local}, multi_turn: {self.multi_turn}'
+        return rep
 
     def __post_init__(self):
         self.prompt_column = 'instruction'
@@ -90,6 +92,8 @@ def init_for_training(self):  # support mixing multiple datasets
 
             dataset_attr = DatasetAttr()
             dataset_attr.dataset_name = name
+            dataset_attr.dataset_format = datasets_info[name].get(
+                'dataset_format', None)
             dataset_attr.hf_hub_url = datasets_info[name].get(
                 'hf_hub_url', None)
             dataset_attr.local_path = datasets_info[name].get(
diff --git a/chatllms/configs/train_args.py b/chatllms/configs/train_args.py
index 2177b53..ea16250 100644
--- a/chatllms/configs/train_args.py
+++ b/chatllms/configs/train_args.py
@@ -71,7 +71,7 @@ class TrainingArguments(TrainingArguments):
             'Group sequences into batches with same length. Saves memory and speeds up training considerably.'
         })
     model_max_length: int = field(
-        default=2048,
+        default=1024,
         metadata={
             'help':
             'Maximum sequence length. Sequences will be right padded (and possibly truncated).'
diff --git a/chatllms/data/data_utils.py b/chatllms/data/data_utils.py
index 68f73e7..7651e9b 100644
--- a/chatllms/data/data_utils.py
+++ b/chatllms/data/data_utils.py
@@ -242,12 +242,16 @@ def load_data(
     """
     if not os.path.exists(dataset_path):
         # Download dataset from HuggingFace Datasets
+        print(
+            f'Lodding dataset from huggingface, please ref to https://huggingface.co/datasets/{dataset_path}'
+        )
         dataset = load_dataset(dataset_path,
                                cache_dir='~/.cache/huggingface/datasets')
         return dataset
     else:
         # Load dataset from local file
         try:
+            print(f'Lodding dataset from local path: {dataset_path}')
             dataset = local_dataset(dataset_path, eval_dataset_size)
             return dataset
         except:
@@ -257,6 +261,7 @@ def load_data(
 def formate_instruction_dataset(
         dataset: Dataset,
         dataset_name: str,
+        dataset_format: str,
         instruction_template: str = 'default') -> Optional[Dict[str, Dataset]]:
     """
     Formats a given dataset based on its name and format.
@@ -270,6 +275,7 @@ def formate_instruction_dataset(
     Args:
         dataset: A dataset object to be formatted.
         dataset_name: A string representing the name of the dataset to be formatted.
+        dataset_format: A string representing the name of the dataset format to be used.
         instruction_template: A string representing the name of the prompt template to be used.
 
     Returns:
@@ -326,36 +332,33 @@ def _remove_unused_columns(dataset):
         ])
         return dataset
 
-    print('formate the dataset to the format we need.')
-    if dataset_name == 'dolly-15k':
+    # Format dataset
+    print(f'The {dataset_name} using {dataset_format} dataset format.')
+    if dataset_format == 'alpaca':
+        print('By default, We support the Alpaca dataset format.')
+    elif dataset_format == 'dolly':
         dataset = _format_dolly15k(dataset)
-    elif dataset_name == 'chip2':
+    elif dataset_format == 'chip2':
         dataset = _format_chip2(dataset)
-    elif dataset_name == 'self-instruct':
+    elif dataset_format == 'self-instruct':
         dataset = _format_self_instruct(dataset)
-    elif dataset_name == 'hh-rlhf':
+    elif dataset_format == 'hh-rlhf':
         dataset = _format_hh_rlhf(dataset)
-    elif dataset_name == 'oasst1':
+    elif dataset_format == 'oasst1':
         dataset = _format_oasst1(dataset)
-    elif dataset_name == '100PoisonMpts':
+    elif dataset_format == '100PoisonMpts':
         dataset = _format_100Poison(dataset)
     else:
-        print(
-            f'For dataset {dataset_name} with alpaca dataset formation, we do not need additional processing'
+        raise NotImplementedError(
+            f'Unsupported dataset format: {dataset_format},  Please add the formate function in data_utils.py'
         )
-        pass
-
     # encode_instruction_example
-    print(
-        f'Encoding the instruction example refer to : {instruction_template}')
+    print(f'Applying instruction template: {instruction_template}')
     if instruction_template == 'alpaca':
-        print('Using alpaca prompt template: ', {instruction_template})
         dataset = dataset.map(extract_alpaca_prompt_dataset)
     elif instruction_template == 'random':
-        print('Using random prompt template: ', {instruction_template})
         dataset = dataset.map(extract_random_prompt_dataset)
     else:
-        print('Using default prompt template: ', {instruction_template})
         dataset = dataset.map(extract_default_prompt_dataset)
 
     # Remove unused columns.
@@ -402,13 +405,16 @@ def split_train_eval(
         else:
             # Split train dataset in train and validation according to `eval_dataset_size`
             print(
-                'Splitting train dataset in train and validation according to `eval_dataset_size`'
+                f'Splitting the dataset into train and validation according to `eval_dataset_size`:  {eval_dataset_size}'
             )
             dataset = dataset['train'].train_test_split(
                 test_size=eval_dataset_size, shuffle=True, seed=42)
             eval_dataset = dataset['test']
 
         # Reduce evaluation dataset size (if specified)
+        print(
+            f'You have set the max_eval_samples: {max_eval_samples}, will do sampling ...'
+        )
         if max_eval_samples is not None and len(
                 eval_dataset) > max_eval_samples:
             eval_dataset = eval_dataset.select(np.arange(max_eval_samples))
@@ -418,6 +424,9 @@ def split_train_eval(
         train_dataset = dataset['train']
 
         # Reduce training dataset size (if specified)
+        print(
+            f'You have set the max_train_samples: {max_train_samples}, will do sampling ...'
+        )
         if max_train_samples is not None and len(
                 train_dataset) > max_train_samples:
             train_dataset = train_dataset.select(np.arange(max_train_samples))
@@ -461,12 +470,13 @@ def make_data_module(args):
     ), 'All datasets should be multi-turn or single-turn. As follwing we will concat all datasets, so they should be in the same format.'
 
     for dataset_attr in args.datasets_list:
-        print('Loading dataset {}...'.format(dataset_attr))
+        print('=' * 80)
+        print('DatasetAttr:  {}...'.format(dataset_attr))
 
         if dataset_attr.load_from_local:
             dataset_path = dataset_attr.local_path
         elif dataset_attr.hf_hub_url:
-            dataset_path = dataset_attr.dataset_name
+            dataset_path = dataset_attr.hf_hub_url
 
         dataset = load_data(dataset_path,
                             eval_dataset_size=args.eval_dataset_size)
@@ -475,6 +485,7 @@ def make_data_module(args):
             dataset = formate_instruction_dataset(
                 dataset,
                 dataset_name=dataset_attr.dataset_name,
+                dataset_format=dataset_attr.dataset_format,
                 instruction_template=args.instruction_template,
             )
 
diff --git a/data/README.md b/data/README.md
index f45b2fe..00cfcb1 100644
--- a/data/README.md
+++ b/data/README.md
@@ -1,7 +1,7 @@
 
-## How to use the data
+# How to use the data
 
-### Datasets Supported by the Framework
+## Datasets Supported by the Framework
 
 We provide the following datasets for the experiments in this framework.
 
@@ -25,26 +25,183 @@ We provide the following datasets for the experiments in this framework.
 - [timdettmers/openassistant-guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco)
 - [Evol-Instruct](https://huggingface.co/datasets/victor123/evol_instruct_70k)
 
-### Dataset formation
+## Dataset formation
 
-The `dataset_info.yaml` file contains the information of the datasets. By defaullt, the framework will load the datasets from the HuggingFace hub. If you want to use the datasets from local files, please specify the `local_path` in the `dataset_info.yaml` file. For example, if you want to use the Alpaca dataset from local files, please specify the following in `dataset_info.yaml`.
+The `dataset_info.yaml` file contains the information of the datasets, main including the following fields.
+
+```yaml
+dataset_name:
+    hf_hub_url: # "the name of the dataset repository on the HuggingFace hub. (if specified, ignore below 3 arguments)",
+    local_path: # "the name of the dataset file in the this directory. (required if above are not specified)",
+    dataset_format: # "the format of the dataset. (required), e.g., alpaca, dolly, etc.",
+    multi_turn:  # "whether the dataset is multi-turn. (default: False)"
+```
+
+For example, the following is the dataset information of the Stanford Alpaca dataset.
 
 ```yaml
 alpaca:
   hf_hub_url: tatsu-lab/alpaca
-  local_path: tatsu-lab/alpaca/alpaca.json
+  local_path:
+  dataset_format: alpaca
   multi_turn: False
 ```
+While training, the framework will load the dataset from the HuggingFace hub. If you want to load the dataset from local files, please specify the `local_path` field.
 
-### Custom datasets
+```yaml
+alpaca:
+  hf_hub_url: tatsu-lab/alpaca
+  local_path: path/to/alpaca.json
+  dataset_format: alpaca
+  multi_turn: False
+```
 
-If you are using a custom dataset, please provide your dataset definition in the following format in `dataset_info.yaml`.
+## Custom datasets
 
-```yaml
-dataset_name:
-    hf_hub_url: # "the name of the dataset repository on the HuggingFace hub. (if specified, ignore below 3 arguments)",
-    local_path: # "the name of the dataset file in the this directory. (required if above are not specified)",
-    multi_turn:  # "whether the dataset is multi-turn. (default: False)"
+If you are using a custom dataset, please provide your dataset definition in  `dataset_info.yaml`.
+
+### hf_hub_url and local_path
+
+By defaullt, the framework will load the datasets from the HuggingFace hub. If you want to use the datasets from local files, please specify the `local_path`  field.
+
+### dataset_format
+
+As for the dataset_format field, which is used to specify the format of the dataset, will be used to determine the dataset processing method. Currently, we support the following dataset formats.
+
+- `alpaca`: Alpaca dataset
+- `dolly`: Dolly dataset
+- `gpt4`: GPT-4 generated dataset
+- `alpaca_cot`: Alpaca CoT dataset
+- `oasst1`: OpenAssistant/oasst1 dataset
+- `sharegpt`: Multi-turn ShareGPT dataset
+
+If your dataset is not in the above format, there are two ways to use it.
+
+- The first way, implement the `format_dataset` function in [data_utils](./chatllms/data/data_utils.py).
+
+For example, the following is the `_format_dolly15k` function for the Dolly dataset.
+
+```python
+def _format_dolly15k(dataset: Dataset) -> Dataset:
+    """Format Dolly-15k dataset."""
+    dataset = dataset.rename_column('context', 'input')
+    dataset = dataset.rename_column('response', 'output')
+    return dataset
+```
+
+- The second way, convert your dataset to the above format.
+
+For example, the flowing code is used to convert the [databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) to the Alpaca format.
+
+```python
+import json
+def convert_dolly_alpaca(in_file, out_file):
+    with open(in_file, 'r') as file:
+        contents = json.load(file)
+        new_content = []
+        for i, content in enumerate(contents):
+            new_content.append({
+              'instruction': content['instruction'],
+              'input': content['text'],
+              'output': content['text'],
+            })
+
+    print(f'#out: {len(new_content)}')
+    with open(out_file, 'w') as file:
+        json.dump(new_content, file, indent=2, ensure_ascii=False)
 ```
 
-where the `prompt` and `response` columns should contain non-empty values. The `query` column will be concatenated with the `prompt` column and used as input for the model. The `history` column should contain a list where each element is a string tuple representing a query-response pair.
+### multi_turn
+
+If your dataset is multi-turn, pleas set the `multi_turn: True` in `dataset_info.yaml`. The framework will automatically process the multi-turn dataset.
+
+Flowing is an example to show the format of multi-turn dataset.
+
+```json
+[
+  {
+    "id": "identity_0",
+    "conversations": [
+      {
+        "from": "human",
+        "value": "Who are you?"
+      },
+      {
+        "from": "gpt",
+        "value": "I am Vicuna, a language model trained by researchers from Large Model Systems Organization (LMSYS)."
+      },
+      {
+        "from": "human",
+        "value": "What can you do?"
+      },
+      {
+        "from": "gpt",
+        "value": "I can chat with you."
+      }
+    ]
+  },
+  {
+    "id": "identity_1",
+    "conversations": [
+      {
+        "from": "human",
+        "value": "Who are you?"
+      },
+      {
+        "from": "gpt",
+        "value": "My name is Vicuna, and I'm a language model developed by Large Model Systems Organization (LMSYS)."
+      }
+    ]
+  },
+]
+```
+
+For now, we only support the multi-turn dataset in the above format. If your dataset is not in the above format, please convert it. We also provide the following code to convert the Dolly dataset to the above format. You can find the code in [convert_alpaca](`./chatllms/data/utils/convert_alpaca.py`).
+
+```python
+import argparse
+import json
+from typing import Any, Dict, List
+
+from datasets import load_dataset
+
+def convert_dolly_vicuna(raw_data: List[Dict[str, Any]]):
+    collect_data = []
+    for i, content in enumerate(raw_data):
+        if len(content['context'].strip()) > 1:
+            q, a = content['instruction'] + '\nInput:\n' + content[
+                'context'], content['response']
+        else:
+            q, a = content['instruction'], content['response']
+
+        collect_data.append({
+            'id':
+            f'alpaca_{i}',
+            'conversations': [
+                {
+                    'from': 'human',
+                    'value': q
+                },
+                {
+                    'from': 'gpt',
+                    'value': a
+                },
+            ],
+        })
+    print(f'Original: {len(raw_data)}, Converted: {len(collect_data)}')
+    return collect_data
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--in-file', type=str)
+    parser.add_argument('--out-file', type=str)
+    args = parser.parse_args()
+
+    raw_data = load_dataset('json', data_files=args.in_file)['train']
+    new_data = convert_dolly_vicuna(raw_data)
+    json_dump(new_data, args.out_file)
+
+
+if __name__ == '__main__':
+    main()
+```
diff --git a/data/dataset_info.yaml b/data/dataset_info.yaml
index 086d863..47dc433 100644
--- a/data/dataset_info.yaml
+++ b/data/dataset_info.yaml
@@ -2,97 +2,104 @@
 alpaca:
   hf_hub_url: tatsu-lab/alpaca
   local_path: tatsu-lab/alpaca/alpaca.json
+  dataset_format: alpaca
   multi_turn: False
 
 alpaca-clean:
   hf_hub_url: yahma/alpaca-cleaned
   local_path: ''
+  dataset_format: alpaca
   multi_turn: False
 
 chip2:
   hf_hub_url: laion/OIG
   local_path: ''
+  dataset_format: chip2
   multi_turn: False
 
 self-instruct:
   hf_hub_url: yizhongw/self_instruct
   local_path: ''
+  dataset_format: self-instruct
   multi_turn: False
 
 guanaco:
   hf_hub_url: JosephusCheung/GuanacoDataset
   local_path: ''
+  dataset_format: guanaco
   multi_turn: False
 
 hh-rlhf:
   hf_hub_url: Anthropic/hh-rlhf
   local_path: ''
+  dataset_format: hh-rlhf
   multi_turn: False
 
 longformer:
   hf_hub_url: akoksal/LongForm
   local_path: ''
+  dataset_format: longformer
   multi_turn: False
 
 openassistant-guanaco:
   hf_hub_url: timdettmers/openassistant-guanaco
   local_path: timdettmers/openassistant-guanaco
-  multi_turn: False
-
-evol_instruct:
-  hf_hub_url: WizardLM/WizardLM_evol_instruct_V2_196k
-  local_path: WizardLM/WizardLM_evol_instruct_V2_196k/WizardLM_evol_instruct_V2_143k.json
+  dataset_format: alpaca
   multi_turn: False
 
 dolly-15k:
   hf_hub_url: databricks/databricks-dolly-15k
   local_path: databricks/databricks-dolly-15k
+  dataset_format: dolly
   multi_turn: False
 
 olcc:
   hf_hub_url: ''
   local_path: /home/robin/prompt_data/olcc/olcc_alpaca.json
+  dataset_format: alpaca
   multi_turn: False
 
-share_gpt:
-  hf_hub_url: ''
-  local_path: /home/robin/prompt_data/sharegpt/sharegpt_split.json
-  multi_turn: True
-
 100PoisonMpts:
   hf_hub_url: ''
   local_path: /home/robin/prompt_data/100PoisonMpts/train.jsonl
+  dataset_format: 100PoisonMpts
   multi_turn: False
 
 # Belle Group
 belle_0.5m:
   hf_hub_url: BelleGroup/train_0.5M_CN
   local_path: ''
+  dataset_format: alpaca
   multi_turn: False
 
 belle_1m:
   hf_hub_url: BelleGroup/train_1M_CN
   local_path: ''
+  dataset_format: alpaca
   multi_turn: False
 
 belle_2m:
   hf_hub_url: BelleGroup/train_2M_CN
   local_path: ''
+  dataset_format: alpaca
   multi_turn: False
 
 belle_dialog:
   hf_hub_url: BelleGroup/generated_chat_0.4M
   local_path: ''
+  dataset_format: belle_dialog
   multi_turn: False
 
 belle_math:
   hf_hub_url: BelleGroup/school_math_0.25M
   local_path: ''
+  dataset_format: alpaca
   multi_turn: False
 
 belle_multiturn:
   hf_hub_url: BelleGroup/multi_turn_0.5M
   local_path: ''
+  dataset_format: belle_multiturn
   multi_turn: True
   columns:
     prompt: instruction
@@ -104,6 +111,7 @@ belle_multiturn:
 firefly:
   hf_hub_url: YeungNLP/firefly-train-1.1M
   local_path: ''
+  dataset_format: alpaca
   multi_turn: False
   columns:
     prompt: input
@@ -115,6 +123,7 @@ firefly:
 codealpaca:
   hf_hub_url: sahil2801/CodeAlpaca-20k
   local_path: ''
+  dataset_format: codealpaca
   multi_turn: False
 
 # alpacacot
@@ -126,6 +135,7 @@ alpaca_cot:
 webqa:
   hf_hub_url: suolyer/webqa
   local_path: ''
+  dataset_format: webqa
   multi_turn: False
   columns:
     prompt: input
@@ -133,7 +143,15 @@ webqa:
     response: output
     history: ''
 
-novel_tokens512_50k:
-  hf_hub_url: zxbsmk/webnovel_cn
-  local_path: ''
-  multi_turn: False
+# mutli-turn datasets
+evol_instruct:
+  hf_hub_url: WizardLM/WizardLM_evol_instruct_V2_196k
+  local_path: WizardLM/WizardLM_evol_instruct_V2_196k/WizardLM_evol_instruct_V2_143k.json
+  dataset_format: sharegpt
+  multi_turn: True
+
+share_gpt:
+  hf_hub_url: ''
+  local_path: /home/robin/prompt_data/sharegpt/sharegpt_split.json
+  dataset_format: sharegpt
+  multi_turn: True