update chatllms

chatllms/configs/gen_args.py

@@ -4,19 +4,24 @@
 
 @dataclass
 class GenerationArguments:
+    """
+    Arguments pertaining to specify the model generation parameters.
+    """
     # generation parameters
+    # 是否使用cache
+    use_cache: Optional[bool] = field(default=True)
     # Length arguments
     # 最大的新生成的token数量
     max_new_tokens: Optional[int] = field(
-        default=512,
+        default=1024,
         metadata={
             'help':
             'Maximum number of new tokens to be generated in evaluation or prediction loops'
             'if predict_with_generate is set.'
         })
     # 最少的新生成的token数量
     min_new_tokens: Optional[int] = field(
-        default=None,
+        default=0,
         metadata={'help': 'Minimum number of new tokens to generate.'})
     # 最大的token数量，会被 max_new_tokens 覆盖
     max_length: Optional[int] = field(
@@ -27,31 +32,61 @@ class GenerationArguments:
         })
     # Generation strategy
     # 是否采样
-    do_sample: Optional[bool] = field(default=True)
+    do_sample: Optional[bool] = field(
+        default=True,
+        metadata={
+            'help':
+            'Whether or not to use sampling, use greedy decoding otherwise.'
+        })
     # 集束搜索的数量
-    num_beams: Optional[int] = field(default=1)
+    num_beams: Optional[int] = field(
+        default=1,
+        metadata={
+            'help': 'Number of beams for beam search. 1 means no beam search.'
+        })
     # 集束搜索的组数量
     num_beam_groups: Optional[int] = field(default=1)
     # 惩罚因子
     penalty_alpha: Optional[float] = field(default=None)
-    # 是否使用cache
-    use_cache: Optional[bool] = field(default=True)
-
     # Hyperparameters for logit manipulation
     # softmax 函数的温度因子，来调节输出token的分布
-    temperature: Optional[float] = field(default=1.0)
+    temperature: Optional[float] = field(
+        default=1.0,
+        metadata={
+            'help': 'The value used to modulate the next token probabilities.'
+        })
     # top_k随机搜索中的k个最高概率选择
-    top_k: Optional[int] = field(default=50)
+    top_k: Optional[int] = field(
+        default=50,
+        metadata={
+            'help':
+            'The number of highest probability vocabulary tokens to keep for top-k filtering.'
+        })
     # 核采样参数，top_p最高的前n个（n是变化）概率和为p，从这些n个候选token中随机采样
-    top_p: Optional[float] = field(default=1.0)
+    top_p: Optional[float] = field(
+        default=1.0,
+        metadata={
+            'help':
+            'The smallest set of most probable tokens with probabilities that add up to top_p or higher are kept.'
+        })
     # 典型p值
     typical_p: Optional[float] = field(default=1.0)
     # 丰富性惩罚因子
     diversity_penalty: Optional[float] = field(default=0.0)
     # 重复性惩罚因子
-    repetition_penalty: Optional[float] = field(default=1.0)
+    repetition_penalty: Optional[float] = field(
+        default=1.0,
+        metadata={
+            'help':
+            'The parameter for repetition penalty. 1.0 means no penalty.'
+        })
     # 长度惩罚因子
-    length_penalty: Optional[float] = field(default=1.0)
+    length_penalty: Optional[float] = field(
+        default=1.0,
+        metadata={
+            'help':
+            'Exponential penalty to the length that is used with beam-based generation.'
+        })
     # 没有ngram重复的尺度大小
     # 一般随机采样的丰富性够了，所以一般不会设置，如果重复很多则设置为2是比较好的选择
     no_repeat_ngram_size: Optional[int] = field(default=0)

chatllms/utils/template.py

@@ -20,6 +20,10 @@ class PromptTemplate(object):
     """
 
     name: str
+    prefix: str = ''
+    prompt: str = None
+    sep: str = None
+    use_history: bool = False
 
     def get_prompt(
         self,
@@ -88,12 +92,14 @@ def format_example(self,
                 convs.append(bot_resp)
         return convs[:-1]  # drop last
 
-    def register_template(self,
-                          name: str,
-                          prefix: str,
-                          prompt: str,
-                          sep: str,
-                          use_history: Optional[bool] = True) -> None:
+    def register_template(
+        self,
+        name: str,
+        prefix: str,
+        prompt: str,
+        sep: str,
+        use_history: Optional[bool] = True,
+    ) -> None:
         """
         Registers a new conversation template.
 

cli_demo.py

@@ -16,7 +16,7 @@
 def generate_response(
     query: str,
     history: List[Tuple[str, str]],
-    source_prefix: str,
+    prefix: str,
     prompt_template: PromptTemplate,
     tokenizer: PreTrainedTokenizer,
     model: PreTrainedModel,
@@ -28,7 +28,7 @@ def generate_response(
     Args:
         query (str): The input query for which a response is to be generated.
         history (List[Tuple[str, str]]): A list of previous queries and their responses.
-        source_prefix (str): The prefix string added to the beginning of each input sequence.
+        prefix (str): The prefix string added to the beginning of each input sequence.
         prompt_template (PromptTemplate): The prompt template used to generate the input sequence to the model.
         tokenizer (PreTrainedTokenizer): The tokenizer used to convert the raw text into input tokens.
         model (PreTrainedModel): The GPT-3.5 model used to generate the response.
@@ -39,7 +39,7 @@ def generate_response(
     """
 
     # Convert the query and history into input IDs
-    input_text = prompt_template.get_prompt(query, history, source_prefix)
+    input_text = prompt_template.get_prompt(query, history, prefix)
     inputs = tokenizer(input_text, return_tensors='pt')
     inputs = {k: v.to(model.device) for k, v in inputs.items()}
 
@@ -100,7 +100,7 @@ def main():
     )
 
     prompt_template = PromptTemplate(model_server_args.prompt_template)
-    source_prefix = model_server_args.source_prefix if model_server_args.source_prefix else ''
+    prefix = model_server_args.source_prefix if model_server_args.source_prefix else ''
     history: List[str] = []
     print('欢迎使用 CLI 对话系统，输入内容即可对话，clear 清空对话历史，stop 终止程序')
     while True:
@@ -122,9 +122,8 @@ def main():
             continue
 
         # Perform prediction and printing
-        history = generate_response(query, history, source_prefix,
-                                    prompt_template, tokenizer, model,
-                                    generation_args)
+        history = generate_response(query, history, prefix, prompt_template,
+                                    tokenizer, model, generation_args)
 
 
 if __name__ == '__main__':

data/README.md

@@ -5,26 +5,47 @@
 
 We provide the following datasets for the experiments in this framework.
 
+### English Instruction Datasets
+
 - [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca)
-- [Stanford Alpaca (Chinese)](https://github.com/ymcui/Chinese-LLaMA-Alpaca)
 - [Hello-SimpleAI/HC3](https://huggingface.co/datasets/Hello-SimpleAI/HC3)
-- [BELLE 2M (zh)](https://huggingface.co/datasets/BelleGroup/train_2M_CN)
-- [BELLE 1M (zh)](https://huggingface.co/datasets/BelleGroup/train_1M_CN)
-- [BELLE 0.5M (zh)](https://huggingface.co/datasets/BelleGroup/train_0.5M_CN)
-- [BELLE Dialogue 0.4M (zh)](https://huggingface.co/datasets/BelleGroup/generated_chat_0.4M)
-- [BELLE School Math 0.25M (zh)](https://huggingface.co/datasets/BelleGroup/school_math_0.25M)
-- [BELLE Multiturn Chat 0.8M (zh)](https://huggingface.co/datasets/BelleGroup/multiturn_chat_0.8M)
 - [databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k)
 - [mosaicml/dolly_hhrlhf](https://huggingface.co/datasets/mosaicml/dolly_hhrlhf)
 - [GPT-4 Generated Data](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)
 - [Alpaca CoT](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT)
 - [UltraChat](https://github.com/thunlp/UltraChat)
 - [OpenAssistant/oasst1](https://huggingface.co/datasets/OpenAssistant/oasst1)
 - [ShareGPT_Vicuna_unfiltered](https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered)
-- [BIAI/OL-CC](https://data.baai.ac.cn/details/OL-CC)
 - [timdettmers/openassistant-guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco)
 - [Evol-Instruct](https://huggingface.co/datasets/victor123/evol_instruct_70k)
 
+### 中文指令数据集
+- [Stanford Alpaca (zh)](https://github.com/ymcui/Chinese-LLaMA-Alpaca)
+- [Alpaca-GPT-4 (zh)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)
+- [ShareChat(倡议大家一起翻译高质量 ShareGPT 数据的项目)](https://paratranz.cn/projects/6725)
+- [InstructionWild (zh)](https://github.com/XueFuzhao/InstructionWild)
+- [SmileConv(通过ChatGPT改写真实的心理互助 QA为多轮的心理健康支持多轮对话)](https://github.com/qiuhuachuan/smile )
+- [BELLE 2M (zh)](https://huggingface.co/datasets/BelleGroup/train_2M_CN)
+- [BELLE 1M (zh)](https://huggingface.co/datasets/BelleGroup/train_1M_CN)
+- [BELLE 0.5M (zh)](https://huggingface.co/datasets/BelleGroup/train_0.5M_CN)
+- [BELLE Dialogue 0.4M (zh)](https://huggingface.co/datasets/BelleGroup/generated_chat_0.4M)
+- [BELLE School Math 0.25M (zh)](https://huggingface.co/datasets/BelleGroup/school_math_0.25M)
+- [BELLE Multiturn Chat 0.8M (zh)](https://huggingface.co/datasets/BelleGroup/multiturn_chat_0.8M)
+- [OL-CC(OpenLabel-Chinese Conversations Dataset)以众包方式、人工生成的开源中文对话指令集](https://data.baai.ac.cn/details/OL-CC)
+- [CValues-Comparison中文大模型价值观比较数据集](https://modelscope.cn/datasets/damo/CValues-Comparison/summary)
+- [100PoisonMpts(给AI的100瓶毒药): 中文大模型治理数据集](https://modelscope.cn/datasets/damo/100PoisonMpts/summary)
+- [COIG(Chinese Open Instruction Generalist project)](https://huggingface.co/datasets/BAAI/COIG)
+- [COIG-PC（Prompt Collection) COIG 数据集二期](https://huggingface.co/datasets/BAAI/COIG-PC)
+- [中文医疗指令数据集-华陀](https://huggingface.co/datasets/FreedomIntelligence/HuatuoGPT-sft-data-v1)
+
+
+### RLHF Datasets
+
+- [CValues](https://github.com/X-PLUG/CValues)
+  数据集说明：开源了数据规模为145k的价值对齐数据集，该数据集对于每个prompt包括了拒绝&正向建议,(safe and reponsibility) > 拒绝为主(safe) > 风险回复(unsafe)三种类型，可用于增强SFT模型的安全性或用于训练reward模型。
+
+
+
 ## Dataset formation
 
 The `dataset_info.yaml` file contains the information of the datasets, main including the following fields.