wehub-resource-sync
569 lines
21 KiB
Python
569 lines
21 KiB
Python
# Copyright (c) ModelScope Contributors. All rights reserved.
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from dataclasses import dataclass, field
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from datetime import datetime
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from typing import Optional
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from swift.utils import get_env_args
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from ..base import Template
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from ..constant import LLMTemplateType, MLLMTemplateType
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from ..register import TemplateMeta, register_template
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from ..template_inputs import StdTemplateInputs
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from ..utils import Prompt
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from .llama import Llama3_2TemplateMeta
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from .qwen import Qwen2VLTemplate, QwenTemplateMeta
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from .utils import DEFAULT_SYSTEM, ChatmlTemplateMeta
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register_template(
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TemplateMeta(
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LLMTemplateType.default,
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prefix=[],
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prompt=['### Human:\n{{QUERY}}\n\n### Assistant:\n'],
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chat_sep=['\n\n'],
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default_system=DEFAULT_SYSTEM,
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system_prefix=['{{SYSTEM}}\n\n'],
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auto_add_bos=True))
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register_template(
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TemplateMeta(
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LLMTemplateType.modelscope_agent,
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prefix=[],
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prompt=[' \n\n<|user|>:{{QUERY}} \n\n<|assistant|>:'],
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chat_sep=[],
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suffix=[' \n\n</s>'],
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system_prefix=[' \n\n<|system|>:{{SYSTEM}}'],
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default_system=DEFAULT_SYSTEM,
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))
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class GMETemplate(Qwen2VLTemplate):
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def _preprocess_inputs(self, inputs: StdTemplateInputs) -> None:
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super()._preprocess_inputs(inputs)
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if inputs.messages[-1]['role'] != 'assistant':
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inputs.messages.append({'role': 'assistant', 'content': ''})
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return inputs
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register_template(QwenTemplateMeta(MLLMTemplateType.qwen2_gme, template_cls=GMETemplate, suffix=['<|endoftext|>']))
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class JinaRerankerM0Template(Qwen2VLTemplate):
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def _preprocess_inputs(self, inputs: StdTemplateInputs) -> None:
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super()._preprocess_inputs(inputs)
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instruction = ''
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if inputs.system is not None:
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instruction = inputs.system
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inputs.system = None
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query = inputs.messages[0]['content']
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document = inputs.messages[1]['content']
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user_message = instruction + '\n' + '**Query**:\n' + query + '\n' + '**Document**:\n' + document
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inputs.messages = [{'role': 'user', 'content': user_message}]
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return inputs
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register_template(
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TemplateMeta(
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MLLMTemplateType.jina_reranker_m0,
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template_cls=JinaRerankerM0Template,
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prefix=[],
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chat_sep=[],
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prompt=['{{QUERY}}']))
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register_template(
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TemplateMeta(LLMTemplateType.baichuan, prefix=['{{SYSTEM}}'], prompt=[[195], '{{QUERY}}', [196]], chat_sep=[]))
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register_template(
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TemplateMeta(
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LLMTemplateType.baichuan_m1,
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prefix=[],
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prompt=['<C_Q>{{QUERY}}<C_A>'],
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chat_sep=[],
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suffix=['<C_A>'],
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system_prefix=['<B_SYS>{{SYSTEM}}'],
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default_system=DEFAULT_SYSTEM,
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))
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register_template(
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TemplateMeta(
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LLMTemplateType.numina,
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prefix=[['bos_token_id']],
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prompt=['### Problem: {{QUERY}}\n### Solution: '],
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chat_sep=['\n'],
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system_prefix=[['bos_token_id'], '{{SYSTEM}}']))
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register_template(
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TemplateMeta(
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LLMTemplateType.mistral_nemo,
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prefix=['<s>[INST] '],
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prompt=['{{SYSTEM}}\n\n', '{{QUERY}}[/INST]'],
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chat_sep=['</s>[INST] '],
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suffix=['</s>']))
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register_template(
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TemplateMeta(
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LLMTemplateType.xverse,
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prefix=['{{SYSTEM}}'],
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prompt=['Human: {{QUERY}}\n\nAssistant: '],
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chat_sep=[['eos_token_id']]))
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register_template(TemplateMeta(LLMTemplateType.yuan, prefix=[], prompt=['{{QUERY}}<sep>'], chat_sep=None))
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register_template(
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TemplateMeta(
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LLMTemplateType.ziya,
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prefix=[['bos_token_id'], '{{SYSTEM}}'],
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prompt=['<human>:{{QUERY}}\n<bot>:'],
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chat_sep=['\n']))
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register_template(
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TemplateMeta(
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LLMTemplateType.skywork,
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prefix=['<s>{{SYSTEM}}'],
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prompt=['</s><s>[USER]{{QUERY}}[SEP][BOT]'],
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chat_sep=None,
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suffix=['[SEP]</s>']))
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register_template(
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Llama3_2TemplateMeta(
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LLMTemplateType.skywork_o1,
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default_system=(
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'You are Skywork-o1, a thinking model developed by Skywork AI, specializing in solving complex problems '
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"involving mathematics, coding, and logical reasoning through deep thought. When faced with a user's "
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'request, you first engage in a lengthy and in-depth thinking process to explore possible solutions to '
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'the problem. After completing your thoughts, you then provide a detailed explanation of the solution '
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'process in your response.'),
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))
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register_template(
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TemplateMeta(
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LLMTemplateType.bluelm,
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prefix=[['bos_token_id'], '{{SYSTEM}}'],
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prompt=['[|Human|]:{{QUERY}}[|AI|]:'],
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chat_sep=[]))
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register_template(
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TemplateMeta(
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LLMTemplateType.codefuse_codellama,
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prefix=['{{SYSTEM}}'],
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prompt=['<|role_start|>human<|role_end|>{{QUERY}}<|role_start|>bot<|role_end|>'],
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chat_sep=[]))
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register_template(
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TemplateMeta(
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LLMTemplateType.codefuse,
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prefix=[],
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prompt=['<s>human\n{{QUERY}}\n<s>bot\n'],
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chat_sep=[['eos_token_id'], '\n'],
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system_prefix=['<s>system\n{{SYSTEM}}\n']))
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register_template(
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TemplateMeta(
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LLMTemplateType.zephyr,
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prefix=[],
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prompt=['<|user|>\n{{QUERY}}</s>\n<|assistant|>\n'],
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chat_sep=['</s>\n'],
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suffix=['</s>'],
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system_prefix=['<|system|>\n{{SYSTEM}}</s>\n']))
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register_template(
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TemplateMeta(
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LLMTemplateType.sus,
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prefix=['{{SYSTEM}}'],
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prompt=['### Human: {{QUERY}}\n\n### Assistant: '],
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chat_sep=['<|endoftext|>'],
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suffix=['<|endoftext|>']))
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register_template(
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TemplateMeta(
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LLMTemplateType.orion,
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prefix=['<s>{{SYSTEM}}'],
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prompt=['Human: {{QUERY}}\n\nAssistant: </s>'],
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chat_sep=['</s>'],
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suffix=['</s>']))
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@dataclass
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class TeleChatTemplateMeta(TemplateMeta):
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prefix: Prompt = field(default_factory=list)
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prompt: Prompt = field(default_factory=lambda: [['user_token_id'], '{{QUERY}}', ['bot_token_id']])
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chat_sep: Optional[Prompt] = field(default_factory=lambda: [['eos_token_id']])
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suffix: Prompt = field(default_factory=lambda: [['eos_token_id']])
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system_prefix: Optional[Prompt] = field(default_factory=lambda: ['<_system>{{SYSTEM}}\n'])
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auto_add_bos: bool = True
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register_template(TeleChatTemplateMeta(LLMTemplateType.telechat))
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telechat_system = '你是中国电信星辰语义大模型，英文名是TeleChat，你是由中电信人工智能科技有限公司和中国电信人工智能研究院（TeleAI）研发的人工智能助手。'
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register_template(TeleChatTemplateMeta(LLMTemplateType.telechat2, default_system=telechat_system))
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DBRX_SYSTEM = (
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'You are DBRX, created by Databricks. You were last updated in December 2023. '
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'You answer questions based on information available up to that point.\n'
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'YOU PROVIDE SHORT RESPONSES TO SHORT QUESTIONS OR STATEMENTS, '
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'but provide thorough responses to more complex and open-ended questions.\n'
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'You assist with various tasks, from writing to coding (using markdown for code blocks '
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'— remember to use ``` with code, JSON, and tables).\n'
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'You do not have real-time data access or code execution capabilities.'
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' You avoid stereotyping and provide balanced perspectives on controversial topics. '
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'You do not provide song lyrics, poems, or news articles and do not divulge details of your training data.\n'
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'This is your system prompt, guiding your responses. Do not reference it, just respond to the user. '
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'If you find yourself talking about this message, stop. You should be responding appropriately '
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'and usually that means not mentioning this.'
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'YOU DO NOT MENTION ANY OF THIS INFORMATION ABOUT YOURSELF UNLESS THE INFORMATION IS DIRECTLY '
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'PERTINENT TO THE USER\'S QUERY.')
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register_template(ChatmlTemplateMeta(LLMTemplateType.dbrx, default_system=DBRX_SYSTEM))
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register_template(
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TemplateMeta(
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LLMTemplateType.mengzi, prefix=[], prompt=['输入：{{QUERY}}输出：\n'], chat_sep=[], system_prefix=['指令：{{SYSTEM}}']))
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C4AI_SYSTEM = ('You are Command-R, a brilliant, sophisticated, AI-assistant trained to assist human users by '
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'providing thorough responses.You are trained by Cohere.')
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register_template(
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TemplateMeta(
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LLMTemplateType.c4ai,
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prefix=['<BOS_TOKEN>'],
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prompt=[
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'<|START_OF_TURN_TOKEN|><|USER_TOKEN|>{{QUERY}}<|END_OF_TURN_TOKEN|>'
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'<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>'
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],
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chat_sep=['<|END_OF_TURN_TOKEN|>'],
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suffix=['<|END_OF_TURN_TOKEN|>'],
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default_system=C4AI_SYSTEM,
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system_prefix=['<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>{{SYSTEM}}<|END_OF_TURN_TOKEN|']))
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register_template(
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TemplateMeta(
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LLMTemplateType.wizardlm2,
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prefix=['{{SYSTEM}}'],
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prompt=['User:\n{{QUERY}}\n\nAssistant:\n'],
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chat_sep=['\n\n'],
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suffix=['</s>']))
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_wizardlm2_system = ('A chat between a curious user and an artificial intelligence assistant. '
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'The assistant gives helpful, detailed, and polite answers to the user\'s questions. ')
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register_template(
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TemplateMeta(
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LLMTemplateType.wizardlm2_moe,
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prefix=['{{SYSTEM}}'],
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prompt=['USER: {{QUERY}} ASSISTANT:'],
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chat_sep=['</s>'],
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suffix=['</s>'],
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default_system=_wizardlm2_system))
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register_template(
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TemplateMeta(
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LLMTemplateType.atom,
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prefix=['{{SYSTEM}}'],
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prompt=['<s>Human: {{QUERY}}\n</s><s>Assistant: '],
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chat_sep=['</s>'],
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suffix=['</s>']))
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AYA_SYSTEM = ('You are Aya, a brilliant, sophisticated, multilingual AI-assistant trained to assist human users by '
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'providing thorough responses. You are able to interact and respond to questions in 23 languages and '
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'you are powered by a multilingual model built by Cohere For AI.')
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register_template(
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TemplateMeta(
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LLMTemplateType.aya,
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prefix=['<BOS_TOKEN>'],
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prompt=[
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'<|START_OF_TURN_TOKEN|><|USER_TOKEN|>{{QUERY}}<|END_OF_TURN_TOKEN|>'
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'<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>'
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],
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chat_sep=['<|END_OF_TURN_TOKEN|>'],
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suffix=['<|END_OF_TURN_TOKEN|>'],
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default_system=AYA_SYSTEM,
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system_prefix=['<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>{{SYSTEM}}<|END_OF_TURN_TOKEN|']))
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register_template(
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TemplateMeta(
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LLMTemplateType.ling,
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prefix=[],
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system_prefix=['<role>SYSTEM</role>{{SYSTEM}}'],
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prompt=['<role>HUMAN</role>{{QUERY}}<role>ASSISTANT</role>'],
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chat_sep=[],
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suffix=['<|endoftext|>'],
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))
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register_template(
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QwenTemplateMeta(
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LLMTemplateType.mimo_rl,
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default_system='You are MiMo, an AI assistant developed by Xiaomi.',
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))
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register_template(
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TemplateMeta(
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LLMTemplateType.dots1,
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prefix=['<|system|>{{SYSTEM}}<|endofsystem|>'],
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prompt=['<|userprompt|>{{QUERY}}<|endofuserprompt|><|response|>'],
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chat_sep=['<|endofresponse|>'],
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suffix=['<|endofresponse|>'],
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default_system='You are a helpful assistant.',
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))
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register_template(
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TemplateMeta(
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LLMTemplateType.hunyuan_moe,
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prefix=['<|startoftext|>'],
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system_prefix=['<|startoftext|>{{SYSTEM}}<|extra_4|>'],
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prompt=['{{QUERY}}<|extra_0|>'],
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chat_sep=['<|eos|><|startoftext|>'],
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suffix=['<|eos|>'],
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))
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class HunyuanTemplate(Template):
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def _remove_thinking_content(self, content: str) -> str:
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content = content.split('<answer>')[-1].rstrip()
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if content.endswith('</answer>'):
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content = content[:-len('</answer>')]
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return self.template_meta.history_thinking_prefix + content.strip()
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register_template(
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TemplateMeta(
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LLMTemplateType.hunyuan,
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prefix=['<｜hy_begin▁of▁sentence｜>'],
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system_prefix=['<｜hy_begin▁of▁sentence｜>{{SYSTEM}}<｜hy_place▁holder▁no▁3｜>'],
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prompt=['<｜hy_User｜>{{QUERY}}<｜hy_Assistant｜>'],
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chat_sep=['<｜hy_place▁holder▁no▁2｜>'],
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suffix=['<｜hy_place▁holder▁no▁2｜>'],
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template_cls=HunyuanTemplate,
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is_thinking=True,
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non_thinking_prefix='<think>\n\n</think>\n',
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agent_template='hunyuan_hermes'))
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class HyV3PreviewTemplate(Template):
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HYTK = ''
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def init_env_args(self):
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super().init_env_args()
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# reasoning_effort: "no_think", "low", "high" (deep chain-of-thought)
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# TODO: sample level
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self.reasoning_effort = get_env_args('reasoning_effort', str, None)
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if self.reasoning_effort is None:
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self.reasoning_effort = 'high' if self.enable_thinking else 'no_think'
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self.enable_thinking = self.reasoning_effort != 'no_think'
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self.chat_template_kwargs['reasoning_effort'] = self.reasoning_effort
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def _get_enable_thinking(self, inputs=None):
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reasoning_effort = None if inputs is None else inputs.chat_template_kwargs.get('reasoning_effort')
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if reasoning_effort is not None:
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return reasoning_effort != 'no_think'
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return super()._get_enable_thinking(inputs)
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def _get_system(self, inputs):
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system = super()._get_system(inputs)
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reasoning_effort = inputs.chat_template_kwargs.get('reasoning_effort')
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if reasoning_effort is None:
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reasoning_effort = self.reasoning_effort
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if inputs.tools:
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# For tool calls, append reasoning_mode after </tool_calls> in the tool instruction
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system = system.replace(
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f'you should print </tool_calls{self.HYTK}>',
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f'you should print </tool_calls{self.HYTK}><｜reasoning_mode{self.HYTK}｜>'
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f'reasoning_effort:{reasoning_effort}')
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else:
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# For non-tool calls, append reasoning_mode to the system/prefix area
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mode_str = f'<｜reasoning_mode{self.HYTK}｜>reasoning_effort:{reasoning_effort}'
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system = (system or '') + mode_str
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return system
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register_template(
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TemplateMeta(
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LLMTemplateType.hy_v3_preview,
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prefix=['<｜hy_begin▁of▁sentence｜>'],
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system_prefix=['<｜hy_begin▁of▁sentence｜>{{SYSTEM}}'],
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prompt=['<｜hy_User｜>{{QUERY}}<｜hy_Assistant｜>'],
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chat_sep=['<｜hy_eos｜>'],
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suffix=['<｜hy_eos｜>'],
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template_cls=HyV3PreviewTemplate,
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is_thinking=True,
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thinking_prefix='<think>',
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non_thinking_prefix='<think></think>',
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history_thinking_prefix='<think></think>',
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agent_template='hy_v3_preview'))
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class HyV3Template(HyV3PreviewTemplate):
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HYTK = ':opensource'
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register_template(
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TemplateMeta(
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LLMTemplateType.hy_v3,
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prefix=['<｜hy_begin_of_sentence:opensource｜>'],
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system_prefix=['<｜hy_begin_of_sentence:opensource｜>{{SYSTEM}}'],
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prompt=['<｜hy_User:opensource｜>{{QUERY}}<｜hy_Assistant:opensource｜>'],
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chat_sep=['<｜hy_eos:opensource｜>'],
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suffix=['<｜hy_eos:opensource｜>'],
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template_cls=HyV3Template,
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||
is_thinking=True,
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thinking_prefix='<think:opensource>',
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non_thinking_prefix='<think:opensource></think:opensource>',
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history_thinking_prefix='<think:opensource></think:opensource>',
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agent_template='hy_v3'))
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|
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class GptTemplate(Template):
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support_padding_free = False
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|
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def _get_gpt_oss_prefix(self):
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today = datetime.now().strftime('%Y-%m-%d')
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return ('<|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.\n'
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f'Knowledge cutoff: 2024-06\nCurrent date: {today}\n\nReasoning: medium\n\n'
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'# Valid channels: analysis, commentary, final. '
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'Channel must be included for every message.<|end|>')
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|
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def _swift_prepare_inputs(self, inputs: StdTemplateInputs):
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||
super()._swift_prepare_inputs(inputs)
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messages = inputs.messages
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||
if self.use_chat_template:
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if inputs.system is None:
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inputs.system = self._get_gpt_oss_prefix()
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elif not inputs.system.startswith('<|start|>'):
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||
inputs.system = self._get_gpt_oss_prefix() + (
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f'<|start|>developer<|message|># Instructions\n\n{inputs.system}<|end|>')
|
||
for i, message in enumerate(messages):
|
||
if message['role'] == 'assistant' and isinstance(message['content'], str):
|
||
if not message['content'].startswith('<|channel|>'):
|
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message['content'] = '<|channel|>final<|message|>' + message['content']
|
||
|
||
|
||
@dataclass
|
||
class GptOssTemplateMeta(TemplateMeta):
|
||
prefix: Prompt = field(default_factory=lambda: ['{{SYSTEM}}'])
|
||
prompt: Prompt = field(default_factory=lambda: ['<|start|>user<|message|>{{QUERY}}<|end|><|start|>assistant'])
|
||
chat_sep: Optional[Prompt] = field(default_factory=lambda: ['<|end|>'])
|
||
suffix: Prompt = field(default_factory=lambda: ['<|return|>'])
|
||
|
||
|
||
register_template(GptOssTemplateMeta(LLMTemplateType.gpt_oss, template_cls=GptTemplate))
|
||
|
||
register_template(
|
||
TemplateMeta(
|
||
LLMTemplateType.longchat,
|
||
prefix=[],
|
||
system_prefix=['SYSTEM:{{SYSTEM}}'],
|
||
prompt=[' [Round {{ROUND0}}] USER:{{QUERY}} ASSISTANT:'],
|
||
chat_sep=['</longcat_s>'],
|
||
suffix=['</longcat_s>'],
|
||
))
|
||
|
||
register_template(
|
||
TemplateMeta(
|
||
LLMTemplateType.ling2,
|
||
prefix=['<role>SYSTEM</role>detailed thinking off<|role_end|>'],
|
||
system_prefix=['<role>SYSTEM</role>{{SYSTEM}}\ndetailed thinking off<|role_end|>'],
|
||
prompt=['<role>HUMAN</role>{{QUERY}}<|role_end|><role>ASSISTANT</role>'],
|
||
chat_sep=['<|role_end|>'],
|
||
suffix=['<|role_end|>'],
|
||
))
|
||
|
||
register_template(
|
||
TemplateMeta(
|
||
LLMTemplateType.ring2,
|
||
prefix=[],
|
||
system_prefix=['<role>SYSTEM</role>{{SYSTEM}}'],
|
||
prompt=['<role>HUMAN</role>{{QUERY}}<role>ASSISTANT</role>'],
|
||
chat_sep=[],
|
||
suffix=['<|endoftext|>'],
|
||
is_thinking=True,
|
||
thinking_prefix='<think>\n',
|
||
))
|
||
|
||
register_template(
|
||
TemplateMeta(
|
||
LLMTemplateType.ring2_5,
|
||
prefix=[],
|
||
system_prefix=['<role>SYSTEM</role>\n{{SYSTEM}}\n\n'],
|
||
prompt=['<role>HUMAN</role>\n{{QUERY}}<|role_end|>\n\n<role>ASSISTANT</role>\n'],
|
||
chat_sep=['<|role_end|>\n\n'],
|
||
suffix=['<|role_end|>\n\n'],
|
||
is_thinking=True,
|
||
))
|
||
|
||
register_template(
|
||
QwenTemplateMeta(
|
||
LLMTemplateType.iquestcoder,
|
||
default_system='You are LoopCoder, a helpful assistant developed by IQuest.',
|
||
))
|
||
|
||
|
||
class YoutuLLMTemplate(Template):
|
||
|
||
def _remove_thinking_content(self, content: str) -> str:
|
||
if '</think>' in content:
|
||
content = content.rsplit('</think>', 1)[-1].lstrip('\n')
|
||
return self.template_meta.history_thinking_prefix + content.strip()
|
||
|
||
def _add_non_thinking_prefix(self, inputs) -> None:
|
||
messages = inputs.messages
|
||
non_thinking_prefix = self.template_meta.non_thinking_prefix
|
||
if non_thinking_prefix and messages:
|
||
# Find the last assistant message
|
||
for i in range(len(messages) - 1, -1, -1):
|
||
message = messages[i]
|
||
if message['role'] == 'assistant' and isinstance(message['content'], str):
|
||
if '<think>' not in message['content'] and '</think>' not in message['content']:
|
||
message['content'] = non_thinking_prefix + message['content']
|
||
break
|
||
|
||
def _remove_history_thinking(self, inputs) -> None:
|
||
messages = inputs.messages
|
||
first_tool_index = len(messages)
|
||
for i, message in enumerate(messages):
|
||
if message['role'] == 'tool' or (message['role'] == 'user' and isinstance(message.get('content'), str)
|
||
and message['content'].startswith('<tool_response>')
|
||
and message['content'].endswith('</tool_response>')):
|
||
first_tool_index = i
|
||
break
|
||
# Only remove thinking content for assistant messages before first_tool_index - 1
|
||
for i, message in enumerate(messages):
|
||
if message['role'] == 'assistant' and isinstance(message['content'], str):
|
||
is_last = (i == len(messages) - 1)
|
||
if not is_last and i < first_tool_index - 1:
|
||
message['content'] = self._remove_thinking_content(message['content'])
|
||
|
||
|
||
register_template(
|
||
TemplateMeta(
|
||
LLMTemplateType.youtu_llm,
|
||
template_cls=YoutuLLMTemplate,
|
||
prefix=[['bos_token_id']],
|
||
system_prefix=[['bos_token_id'], '{{SYSTEM}}'],
|
||
prompt=['<|User|>{{QUERY}}<|Assistant|>'],
|
||
chat_sep=['<|end_of_text|>'],
|
||
suffix=['<|end_of_text|>'],
|
||
is_thinking=True,
|
||
non_thinking_prefix='<think>\n\n</think>\n\n',
|
||
agent_template='youtu',
|
||
))
|
||
|
||
register_template(
|
||
TemplateMeta(
|
||
LLMTemplateType.olmoe,
|
||
prefix=['|||IP_ADDRESS|||'],
|
||
system_prefix=['|||IP_ADDRESS|||<|system|>\n{{SYSTEM}}\n'],
|
||
prompt=['<|user|>\n{{QUERY}}\n<|assistant|>\n'],
|
||
chat_sep=['|||IP_ADDRESS|||\n'],
|
||
suffix=['|||IP_ADDRESS|||'],
|
||
stop_words=['<|endoftext|>'],
|
||
))
|
||
|
||
register_template(
|
||
TemplateMeta(
|
||
LLMTemplateType.olmoe_0924,
|
||
prefix=['<|endoftext|>'],
|
||
system_prefix=['<|endoftext|><|system|>\n{{SYSTEM}}\n'],
|
||
prompt=['<|user|>\n{{QUERY}}\n<|assistant|>\n'],
|
||
chat_sep=['<|endoftext|>\n'],
|
||
suffix=['<|endoftext|>'],
|
||
stop_words=['<|endoftext|>'],
|
||
))
|