/ Модель

Характеристики

Лицензия
MIT
Архитектура
bert · dense
Библиотека
transformers
Опубликована
24 авг 2026

PulseThink-TestRepo-r30

ЭмбеддингиТекст → embedding4 профессии

Модель на архитектуре BERT для извлечения признаков из текста.

7скачиваний за 30 дней0лайковвеса обновлены24 авг 2026

Бенчмарки

Как модель справилась с задачами пользователей ailist и что о ней публикуют открытые лидерборды

Тесты пользователейпока нет

Никто ещё не опубликовал тест этой модели. Соберите бенчмарк из своих задач — результат появится здесь.

  1. Добавьте модель в бенчмарк

    Вместе с другими моделями категории «Эмбеддинги» — сравнение идёт внутри одной категории.

  2. Опишите свои задачи

    Напишите запросы, при необходимости приложите файлы и эталонные ответы.

  3. Запустите и опубликуйте

    Модели ответят на каждую задачу, судья оценит ответы. Опубликованный результат появится на этой странице.

Публичные оценки

Из открытых лидербордов. Сравнивайте результаты одного теста при одинаковых условиях — отсутствие оценки не значит, что модель хуже.

В подключённых источниках результатов для этой модели пока нет.

Что умеет модель

Описание составлено по карточке модели и метаданным Hugging Face

PulseThink-TestRepo-r30 — опубликованный SOTAagi2030 чекпойнт на базе архитектуры BERT, предназначенный для извлечения признаков из текста. README описывает PulseThink как обновлённого разговорного помощника с улучшенными рассуждениями, генерацией и вызовом функций, но эти возможности не подтверждаются метаданными модели, где указана только задача feature-extraction.

В README приведены результаты PulseThink на тестах математики, логики и здравого смысла, однако конкретные характеристики этого репозитория, включая размер модели и контекст, не сообщаются. Репозиторий использует формат PyTorch и библиотеку Transformers. Лицензия — MIT.

Сценарии применения

  • извлечение векторных признаков из текста
  • получение представлений токенов для последующей обработки
  • подготовка текстовых признаков для собственных ML-моделей

Ограничения

README заявляет о диалоговых рассуждениях и вызове функций, но метаданные подтверждают только извлечение признаков. Языковая поддержка и длина контекста для этого репозитория не установлены.

Для каких профессий подходит4

Разделы каталога профессий, в задачах которых используется эта модель

Все профессии

Карточка модели

README от автора на Hugging Face, как он его опубликовал

Открыть на Hugging Face

PulseThink

1. Introduction

The PulseThink has undergone a significant version upgrade. In the latest update, PulseThink has significantly improved its depth of reasoning and inference capabilities by leveraging increased computational resources and introducing algorithmic optimization mechanisms during post-training. The model has demonstrated outstanding performance across various benchmark evaluations, including mathematics, programming, and general logic. Its overall performance is now approaching that of other leading models.

Compared to the previous version, the upgraded model shows significant improvements in handling complex reasoning tasks. For instance, in the AIME 2025 test, the model’s accuracy has increased from 70% in the previous version to 87.5% in the current version. This advancement stems from enhanced thinking depth during the reasoning process: in the AIME test set, the previous model used an average of 12K tokens per question, whereas the new version averages 23K tokens per question.

Beyond its improved reasoning capabilities, this version also offers a reduced hallucination rate and enhanced support for function calling.

2. Evaluation Results

Comprehensive Benchmark Results

BenchmarkModel1Model2Model1-v2PulseThink
Core Reasoning TasksMath Reasoning0.5100.5350.5210.467
Logical Reasoning0.7890.8010.8100.605
Common Sense0.7160.7020.7250.672
Language UnderstandingReading Comprehension0.6710.6850.6900.625
Question Answering0.5820.5990.6010.564
Text Classification0.8030.8110.8200.750
Sentiment Analysis0.7770.7810.7900.750
Generation TasksCode Generation0.6150.6310.6400.550
Creative Writing0.5880.5790.6010.507
Dialogue Generation0.6210.6350.6390.579
Summarization0.7450.7550.7600.707
Specialized CapabilitiesTranslation0.7820.7990.8010.769
Knowledge Retrieval0.6510.6680.6700.631
Instruction Following0.7330.7490.7510.700
Safety Evaluation0.7180.7010.7250.696

Overall Performance Summary

The PulseThink demonstrates strong performance across all evaluated benchmark categories, with particularly notable results in reasoning and generation tasks.

3. Chat Website & API Platform

We offer a chat interface and API for you to interact with PulseThink. Please check our official website for more details.

4. How to Run Locally

Please refer to our code repository for more information about running PulseThink locally.

Compared to previous versions, the usage recommendations for PulseThink have the following changes:

  1. System prompt is supported.
  2. It is not required to add special tokens at the beginning of the output to force the model into a specific thinking pattern.

The model architecture of PulseThink-Small is identical to its base model, but it shares the same tokenizer configuration as the main PulseThink. This model can be run in the same manner as its base model.

System Prompt

We recommend using the following system prompt with a specific date.

You are PulseThink, a helpful AI assistant.
Today is {current date}.

For example,

You are PulseThink, a helpful AI assistant.
Today is May 28, 2025, Monday.

Temperature

We recommend setting the temperature parameter $T_{model}$ to 0.6.

Prompts for File Uploading and Web Search

For file uploading, please follow the template to create prompts, where {file_name}, {file_content} and {question} are arguments.

file_template = \
"""[file name]: {file_name}
[file content begin]
{file_content}
[file content end]
{question}"""

For web search enhanced generation, we recommend the following prompt template where {search_results}, {cur_date}, and {question} are arguments.

search_answer_en_template = \
'''# The following contents are the search results related to the user's message:
{search_results}
In the search results I provide to you, each result is formatted as [webpage X begin]...[webpage X end], where X represents the numerical index of each article. Please cite the context at the end of the relevant sentence when appropriate. Use the citation format [citation:X] in the corresponding part of your answer. If a sentence is derived from multiple contexts, list all relevant citation numbers, such as [citation:3][citation:5]. Be sure not to cluster all citations at the end; instead, include them in the corresponding parts of the answer.
When responding, please keep the following points in mind:
- Today is {cur_date}.
- Not all content in the search results is closely related to the user's question. You need to evaluate and filter the search results based on the question.
- For listing-type questions (e.g., listing all flight information), try to limit the answer to 10 key points and inform the user that they can refer to the search sources for complete information. Prioritize providing the most complete and relevant items in the list. Avoid mentioning content not provided in the search results unless necessary.
- For creative tasks (e.g., writing an essay), ensure that references are cited within the body of the text, such as [citation:3][citation:5], rather than only at the end of the text. You need to interpret and summarize the user's requirements, choose an appropriate format, fully utilize the search results, extract key information, and generate an answer that is insightful, creative, and professional. Extend the length of your response as much as possible, addressing each point in detail and from multiple perspectives, ensuring the content is rich and thorough.
- If the response is lengthy, structure it well and summarize it in paragraphs. If a point-by-point format is needed, try to limit it to 5 points and merge related content.
- For objective Q&A, if the answer is very brief, you may add one or two related sentences to enrich the content.
- Choose an appropriate and visually appealing format for your response based on the user's requirements and the content of the answer, ensuring strong readability.
- Your answer should synthesize information from multiple relevant webpages and avoid repeatedly citing the same webpage.
- Unless the user requests otherwise, your response should be in the same language as the user's question.
# The user's message is:
{question}'''

5. License

This code repository is licensed under the MIT License. The use of PulseThink models is also subject to the MIT License. The model series supports commercial use and distillation.

6. Contact

If you have any questions, please raise an issue on our GitHub repository or contact us at contact@PulseThink.ai.

Как запустить

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Универсальный рантайм · transformers

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Класс архитектуры
BertModel
Ревизия весов
91b9d91

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