Mistral7B_v2_inst_sharegpt_roleplay_chat_lora_small

Model Card for Model ID

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Install

pip install peft transformers bitsandbytes

Prompt Defination

import json
from dataclasses import dataclass
from enum import Enum
from typing import List, Dict, Tuple, Literal

class Roles(Enum):
    system = "system"
    user = "user"
    assistant = "assistant"
    tool = "tool"

class MessagesFormatterType(Enum):
    """
    Enum representing different types of predefined messages formatters.
    """

    MISTRAL = 1

@dataclass
class PromptMarkers:
    start: str
    end: str

class MessagesFormatter:
    def __init__(
            self,
            pre_prompt: str,
            prompt_markers: Dict[Roles, PromptMarkers],
            include_sys_prompt_in_first_user_message: bool,
            default_stop_sequences: List[str],
            use_user_role_for_function_call_result: bool = True,
            strip_prompt: bool = True,
            bos_token: str = "<s>",
            eos_token: str = "</s>"
    ):
        self.pre_prompt = pre_prompt
        self.prompt_markers = prompt_markers
        self.include_sys_prompt_in_first_user_message = include_sys_prompt_in_first_user_message
        self.default_stop_sequences = default_stop_sequences
        self.use_user_role_for_function_call_result = use_user_role_for_function_call_result
        self.strip_prompt = strip_prompt
        self.bos_token = bos_token
        self.eos_token = eos_token
        self.added_system_prompt = False

    def get_bos_token(self) -> str:
        return self.bos_token

    def format_conversation(
            self,
            messages: List[Dict[str, str]],
            response_role: Literal[Roles.user, Roles.assistant] | None = None,
    ) -> Tuple[str, Roles]:
        formatted_messages = self.pre_prompt
        last_role = Roles.assistant
        self.added_system_prompt = False
        for message in messages:
            role = Roles(message["role"])
            content = self._format_message_content(message["content"], role)

            if role == Roles.system:
                formatted_messages += self._format_system_message(content)
                last_role = Roles.system
            elif role == Roles.user:
                formatted_messages += self._format_user_message(content)
                last_role = Roles.user
            elif role == Roles.assistant:
                formatted_messages += self._format_assistant_message(content)
                last_role = Roles.assistant
            elif role == Roles.tool:
                formatted_messages += self._format_tool_message(content)
                last_role = Roles.tool

        return self._format_response(formatted_messages, last_role, response_role)

    def _format_message_content(self, content: str, role: Roles) -> str:
        if self.strip_prompt:
            return content.strip()
        return content

    def _format_system_message(self, content: str) -> str:
        formatted_message = self.prompt_markers[Roles.system].start + content + self.prompt_markers[Roles.system].end
        self.added_system_prompt = True
        if self.include_sys_prompt_in_first_user_message:
            formatted_message = self.prompt_markers[Roles.user].start + formatted_message
        return formatted_message

    def _format_user_message(self, content: str) -> str:
        if self.include_sys_prompt_in_first_user_message and self.added_system_prompt:
            self.added_system_prompt = False
            return content + self.prompt_markers[Roles.user].end
        return self.prompt_markers[Roles.user].start + content + self.prompt_markers[Roles.user].end

    def _format_assistant_message(self, content: str) -> str:
        return self.prompt_markers[Roles.assistant].start + content + self.prompt_markers[Roles.assistant].end

    def _format_tool_message(self, content: str) -> str:
        if isinstance(content, list):
            content = "\n".join(json.dumps(m, indent=2) for m in content)
        if self.use_user_role_for_function_call_result:
            return self._format_user_message(content)
        else:
            return self.prompt_markers[Roles.tool].start + content + self.prompt_markers[Roles.tool].end

    def _format_response(
            self,
            formatted_messages: str,
            last_role: Roles,
            response_role: Literal[Roles.user, Roles.assistant] | None = None,
    ) -> Tuple[str, Roles]:
        if response_role is None:
            response_role = Roles.assistant if last_role != Roles.assistant else Roles.user

        prompt_start = self.prompt_markers[response_role].start.strip() if self.strip_prompt else self.prompt_markers[
            response_role].start
        return formatted_messages + prompt_start, response_role

mixtral_prompt_markers = {
    Roles.system: PromptMarkers("", """\n\n"""),
    Roles.user: PromptMarkers("""[INST] """, """ [/INST]"""),
    Roles.assistant: PromptMarkers("""""", """</s>"""),
    Roles.tool: PromptMarkers("", ""),
}

mixtral_formatter = MessagesFormatter(
    "",
    mixtral_prompt_markers,
    True,
    ["</s>"],
)

Run by transformers

from transformers import TextStreamer, AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2",)
mis_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2", load_in_4bit = True)
mis_model = PeftModel.from_pretrained(mis_model, "svjack/Mistral7B_v2_inst_sharegpt_roleplay_chat_lora_small")
mis_model = mis_model.eval()

streamer = TextStreamer(tokenizer)

def mistral_hf_predict(messages, mis_model = mis_model,
    tokenizer = tokenizer, streamer = streamer,
    do_sample = True,
    top_p = 0.95,
    top_k = 40,
    max_new_tokens = 512,
    max_input_length = 3500,
    temperature = 0.9,
    repetition_penalty = 1.0,
    device = "cuda"):

    #encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
    #model_inputs = encodeds.to(device)
    prompt, _ = mixtral_formatter.format_conversation(messages)
    model_inputs = tokenizer.encode(prompt, return_tensors="pt").to(device)

    generated_ids = mis_model.generate(model_inputs, max_new_tokens=max_new_tokens,
                                do_sample=do_sample,
                                  streamer = streamer,
                                  top_p = top_p,
                                  top_k = top_k,
                                  temperature = temperature,
                                  repetition_penalty = repetition_penalty,
                                  )
    out = tokenizer.batch_decode(generated_ids)[0].split("[/INST]")[-1].replace("</s>", "").strip()
    return out

out = mistral_hf_predict([
            {
                "role": "system",
                "content": "Jack是一名数理统计学生，Sho是一名经济统计学生，他们两个人在课间讨论两个学科的关系。你扮演Sho."
            },
            {
                "role": "user",
                "content": "你如何看待我的专业学科？"
            },
            {
                "role": "assistant",
                "content": "我认为你们的专业学科很有用，因为它可以帮助我们更好地了解数据和模型。"
            },
            {
                "role": "user",
                "content": "是的，你能举一些数理统计模型在经济统计学科中的应用吗？"
            },
            {
                "role": "assistant",
                "content": "当然，例如回归分析、时间序列分析等都是经常使用的数理统计模型。"
            },
            {
                "role": "user",
                "content": "能结合具体涉及变量的场景，谈谈上面的每个模型给出一个对应的实际的研究问题吗，比如用到了什么具体的实际变量？"
            }
        ],
        repetition_penalty = 1.1,
        temperature = 0.01,
        max_new_tokens=1024
)
print(out)

Output

比如说，回归分析可以用来分析某个变量对另一个变量的影响，例如研究人口增长对经济增长的影响。而时间序列分析则可以用来预测未来的值，例如股票价格的预测。

out = mistral_hf_predict([
            {
                "role": "system",
                "content": "Jack是一个比较古板的老学究，Sho是一个喜欢新型事物、充满阳光的小可爱，Sho正通过语言逗弄Jack，享受惬意的二人娱乐时光。你扮演Sho."
            },
            {
                "role": "user",
                "content": "最近有哪些好玩的或可以放松的事物吗？"
            },
            {
                "role": "assistant",
                "content": "哦，我最近发现了一种叫做VR游戏的东西，非常有趣！"
            },
            {
                "role": "user",
                "content": "是的，我听说射击游戏半条命2正好出来一个可以用VR进行游玩的DLC，视频里面主角可以用手直接接触物体，很有真实感。"
            },
            {
                "role": "assistant",
                "content": "哇，这个游戏看起来真的很有趣啊！我们可以一起试试看呢？"
            },
            {
                "role": "user",
                "content": "我会去超市买一些东西，你要些什么？我们可以边玩边吃。"
            }
        ],
        repetition_penalty = 1.1,
        temperature = 0.01,
        max_new_tokens=1024
)
print(out)

Output

我想要一盒薯片和一瓶可乐，还有一支纸巧克力。

Model Details

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Framework versions

• PEFT 0.11.0