/ Модель

Характеристики

Лицензия
MIT
Архитектура
bert · dense
Библиотека
transformers
Опубликована
7 сен 2026

MyAwesomeModel-TestRepo

ЭмбеддингиТекст → embedding7 профессий

MyAwesomeModel — BERT-совместимая модель Transformers для извлечения признаков и получения векторных представлений текста.

293скачиваний за 30 дней0лайковвеса обновлены6 окт 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

MyAwesomeModel — модель из репозитория asdad456/MyAwesomeModel-TestRepo, доступная через библиотеку Transformers и предназначенная для извлечения признаков из входных данных. В метаданных Hugging Face для неё указан pipeline-тег feature-extraction, а также совместимость с PyTorch и семейством BERT.

README описывает обновлённую версию модели с улучшениями рассуждений, понимания текста, генерации кода, ответов на вопросы, классификации, анализа тональности, суммаризации и других задач. Однако эти заявления не согласуются с указанным в метаданных назначением feature-extraction, поэтому модель следует прежде всего рассматривать как инструмент получения векторных представлений для последующих NLP-задач.

Модель распространяется по лицензии MIT. Размер, длина контекста и поддерживаемые естественные языки в доступных данных не указаны.

Сценарии применения

  • получение эмбеддингов текста
  • семантический поиск
  • сравнение текстов по смыслу
  • подготовка признаков для классификатора
  • кластеризация документов

Ограничения

Заявленные в README возможности генерации и рассуждений не подтверждаются указанным pipeline-тегом feature-extraction. Данные о языках, размере модели и длине контекста отсутствуют.

Для каких профессий подходит7

Разделы каталога профессий, в задачах которых используется эта модель

Все профессии

Также подходит для

Карточка модели

README от автора на Hugging Face, как он его опубликовал

Открыть на Hugging Face

MyAwesomeModel

1. Introduction

The MyAwesomeModel has undergone a significant version upgrade. In the latest update, MyAwesomeModel 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-v2MyAwesomeModel
Core Reasoning TasksMath Reasoning0.5100.5350.5210.550
Logical Reasoning0.7890.8010.8100.819
Common Sense0.7160.7020.7250.736
Language UnderstandingReading Comprehension0.6710.6850.6900.700
Question Answering0.5820.5990.6010.607
Text Classification0.8030.8110.8200.828
Sentiment Analysis0.7770.7810.7900.792
Generation TasksCode Generation0.6150.6310.6400.650
Creative Writing0.5880.5790.6010.610
Dialogue Generation0.6210.6350.6390.644
Summarization0.7450.7550.7600.767
Specialized CapabilitiesTranslation0.7820.7990.8010.804
Knowledge Retrieval0.6510.6680.6700.676
Instruction Following0.7330.7490.7510.758
Safety Evaluation0.7180.7010.7250.739

Overall Performance Summary

The MyAwesomeModel 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 MyAwesomeModel. Please check our official website for more details.

4. How to Run Locally

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

Compared to previous versions, the usage recommendations for MyAwesomeModel 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 MyAwesomeModel-Small is identical to its base model, but it shares the same tokenizer configuration as the main MyAwesomeModel. 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 MyAwesomeModel, a helpful AI assistant.
Today is {current date}.

For example,

You are MyAwesomeModel, 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 MyAwesomeModel 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@MyAwesomeModel.ai.

Как запустить

На своей GPU в ailist. Цена — по текущим предложениям провайдеров

GPU в ailist

Универсальный рантайм · transformers

Можно запустить

Подбираем GPU…

Класс архитектуры
BertModel
Ревизия весов
0369309

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