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Как ухватить LLM за хвост: эффективные стратегии тестирования AI-моделей

Как убедиться, что ваша LLM не выдаст полную чепуху в самый неподходящий момент? Как проверить, что она действительно понимает контекст, а не просто генерирует красивые, но бессмысленные фразы? И самое главное — как сделать это эффективно, не тратя недели на ручную проверку тысяч ответов?

Введение

Сегодня технологии развиваются с огромной скоростью. Классические методы разработки уходят на второй план. Им на смену приходят большие языковые модели (LLM) и их приложения.

LLM уже меняют многие сферы жизни: медицину, образование, бизнес. Эти модели становятся мощнее и умнее с каждым днём.

Но перед нами встают новые задачи. Как правильно разрабатывать такие системы? Как тестировать их, чтобы они работали стабильно? Чтобы справиться с этим, нужны новые подходы и глубокое понимание LLM.

Давайте разберёмся, как эти модели работают и что ждёт нас в будущем.

Есть три главных столпа тестирования LLM:

Пользовательский фидбэк: Самый очевидный, но запоздалый способ понять, работает ли ваше приложение. Ведь фидбэк возможен только после релиза. А если ошибки спрятаны до продакшна?
Ручное тестирование: Трудозатратное занятие, способное исчерпать даже самую энергичную команду. О плюсах и минусах применения на практике вы, вероятно, уже знаете: субъективность и, увы, тоже ошибки.
Автоматическое тестирование: Спасительная гавань для всех разработчиков LLM. Однако есть нюанс — оно все еще развивается, как и сами LLM, и всегда требует свежих подходов.

Для того, чтобы протестировать LLM автоматически, можно использовать разные инструменты:

Классические тесты
Готовые метрики
Кастомные метрики

Классические тесты

Когда мы только окунулись в разработку приложений с LLM, мы начали с изучения Pydantic классов в Python. Да, Pydantic — это как волшебная таблетка для превращения хаоса ответов модели в нечто более предсказуемое и детерминированное. С их помощью можно настроить весь набор атрибутов, которые мы ожидаем увидеть.

Вот что можно настроить для каждого атрибута:

Название
Тип
Обязательность
Описание: как краткая заметка — зачем этот атрибут нужен.
Значение по умолчанию: если атрибут не обязателен

Пример класса:

class Appointment(BaseModel):
    time: str = Field(..., description="Date and time of meeting in format YYYY-MM-DDTHH:MM:SS")
    duration: int = Field(description="Duration of the meeting in minutes", default=60)
    title: str = Field(..., description="Meeting title in format: Interview [Name Surname] (of candidate)")
    description: str = Field(..., description=f"Link to the meeting in format: meeting/[name_surname] (of candidate)")

Что мы здесь видим? Все, что внутри этого класса, — это как указания модели о том, на что она должна целиться. Более того, можно подключить валидатор, который опять же проверит, соответствует ли ответ модели всем требованиям по ключам и типам.

Теперь о тестах. Чтобы обычные assert-тесты могли подтвердить, что ответ модели верен, вам нужно заранее знать, какой именно ответ ожидается. Например:

Представьте, вы хотите, чтобы модель разобралась, сколько животных у бабушки в сказке про трех гусей. Тогда вы опишете ответ модели вот так:

class ModelOutput(BaseModel):
    pets: int = Field(..., description='Number of pets')

Предположим, вы даете модели ввод 'Жили у бабуси три веселых гуся', и тогда ваш assert будет выглядеть так:

assert actual_output == ModelOutput(pets=3)

Звучит просто, да? Так можно оценивать не только числа, но и строки, если у вас есть представление, каким должен быть шаблон. Регулярные выражения — отличный способ справляться с проверками конкретных шаблонов.

Этот тип тестирования — настоящая выручалочка для начала работы с ЛЛМ. Но не забывайте: успех тут зависит от вашего мастерства в написании четких и подробных инструкций для ваших задач.

Тестирование базовыми метриками

Базовые метрики

Хотя тестирование LLM — это еще, по сути, совсем новая стезя, уже есть целый арсенал текстовых метрик, которые помогают разобраться, насколько качественно ваша модель справляется с задачами. Почему именно текстовые метрики? Да потому что текст — основа ответов LLM. Вот самые основные метрики, с которыми мы имели дело:

Саммаризация
Релевантность ответа
Правдивость
Галлюцинация

На самом деле, метрик для оценки текста огромное множество, и при желании можно подобрать что-то под любую задачу, используя готовые ресурсы. Но вот загвоздка: как внедрить их в тесты? Для этого мы нашли два основных фреймворка:

DeepEval
LangSmith Testing

Эти фреймворки — ваши верные помощники в тестировании LLM. Каждый из них имеет свои особенности и подходит для разных нужд.

LangSmith тестирование

LangChain и его огромное сообщество — пожалуй, самый популярный фреймворк для создания LLM приложений. Он стал для нашей команды основным инструментом для создания языковых цепочек, поэтому мы и начали тестирование с него. Но, надо признать, что все оказалось не таким легким и удобным, как мы надеялись.

Сначала расскажем о плюсах, которые мы обнаружили:

Возможность создания датасетов под конкретные задачи
Простота написания экспериментов для конкретных датасетов
Все эксперименты и датасеты сохраняются, что позволяет отслеживать динамику изменений
Если вы регистрируете свои цепочки и агенты в LangSmith, можно привязать датасет и эксперимент именно к конкретной цепочке
Возможность версионирования датасетов - позволяет тестировать все сохраненные версии вашего приложения

В общем и целом, это неплохой инструмент для тестов, если ваши задачи не слишком специфичны. Однако, мы столкнулись с рядом трудностей, которые решили не обходить стороной.

Первая проблема — отсутствие возможности управлять датасетами из кода. Вы можете создавать датасеты и добавлять туда примеры, но вот удалять их или конкретные элементы автоматически не получится. Это значит, что если ваша команда работает с нескольких аккаунтов, каждому члену нужно вручную создавать или удалять датасеты перед запуском эксперимента. Если датасет уже существует, все элементы будут создаваться по-новой при запуске эксперимента, и статистика окажется искаженной.
Следующая проблема - не поддержка асинхронности. При создании эксперимента:

evaluators = [
    LangChainStringEvaluator("cot_qa")
]

results = evaluate(
    chain.invoke,
    data=data,
    evaluators=evaluators,
    experiment_prefix=experiment_prefix,
)

Мы обнаружили, что в методе evaluate нужно явным образом вызвать цепочку через invoke. А что если ваша цепочка асинхронная? Менять асинхронность на синхронность ради тестов — такое себе удовольствие. Нам пришлось написать специальный враппер, чтобы обернуть ainvoke в синхронную функцию. Но и тут не обошлось без приключений. LangChainStringEvaluator создает свой event loop, который нужно перехватить, чтобы запустить цепочку.

Вот как нам в итоге удалось внедрить нашу асинхронную цепочку:

nest_asyncio.apply()
event_loop = asyncio.get_event_loop()

def sync_invoke_wrapper(inputs):
    try:
        result = event_loop.run_until_complete(chain.ainvoke(inputs))
        return result
    except Exception as error:
        raise error

В LangSmith набор метрик достаточно ограничен. Полный список, конечно, можно найти в документации, но, по нашему мнению, этих метрик недостаточно, чтобы тестировать приложения с бизнесовой точки зрения. Можно протестировать качество текста, но это больше похоже на лингвистическую оценку и не более.

Если вам хватает представленных метрик, то этот метод вполне рабочий. Однако наша команда решила пока не использовать этот продукт на данном этапе его разработки.

DeepEval тестирование

Хотя DeepEval и не так популярен, как LangSmith, именно он стал нашим выбором в тестировании LLM.

Вот какие плюсы мы нашли в этом фреймворке:

Поддержка датасетов.
DeepEval, как и LangSmith, позволяет создавать датасеты. Но самое важное — в DeepEval вы можете полностью управлять ими прямо из кода. Все проблемы, о которых мы говорили в контексте LangSmith, здесь просто исчезают.
Обилие готовых метрик.
В DeepEval есть множество готовых метрик, что выгодно его отличает. В LangSmith метрики в основном оценивают текст с лингвистической точки зрения, что не всегда подходит для бизнес-задач. Метрики DeepEval больше ориентированы на оценку полезности ответа в конкретных кейсах.
Правда, они работают корректно только в классических сценариях. Например, метрика для саммаризации будет адекватно оценивать только традиционные краткие пересказы. Если в саммари добавится анализ или что-то специфическое, метрика может вести себя нестабильно или вовсе не сработать.
Простота написания тест-кейсов.
В DeepEval легко писать тест-кейсы, поскольку их можно просто позаимствовать из документации для конкретной метрики. Однако - обращайте внимание, какие параметры она принимает. Чтобы понять, что именно передавать, стоит изучить описание, как метрика работает. Для достижения максимально точного результата нужно предоставить тест-кейсу полный контекст. Если ваша модель отвечает на вопрос, учитывая информацию откуда-либо кроме юзер промпта, этот информацию тоже обязательно передать в тест-кейс. Иначе ответ будет оценен как содержащий лишнюю информацию.

Если при использовании метрик DeepEval вы получаете неожиданно низкие оценки, попробуйте следующее:

Внимательно изучите ризон, которую дает метрика вместе с оценкой.
Если ризон указывает на нехватку контекста, попробуйте изменить или добавить параметры.
Если контекст полный, но оценка все еще не оправдывает ожиданий, попробуйте использовать кастомную метрику GEval.

Более техническое сравнение двух фреймворков

(мы все погуглили за вас)

Фича	LangSmith	DeepEval
Мониторинг и отладка в продакшене	Поддерживает комплексно мониторинг и визуализацию данных в продакшене для оценки LLM. Предоставляет возможность отслеживать логи и добавлять тестовые данные	Не фокусируется на мониторинге продакшена, но интегрируется с существующими фреймворками для локального юнит-тестирования и анализа моделей
Создание и хранение датасетов	Поддерживает создание и хранение тестовых датасетов, что позволяет добавлять примеры из реального использования приложения	Работает с тестовыми датасетами для юнит-тестирования и гиперпараметрической оптимизации, но акцент больше на проверках конкретных случаев, чем на хранении
Метрики для оценки	Интеграция с Ragas для метрик верности, релевантности ответа и контекста. Помогает объяснять результаты этих метрик и делает их воспроизводимыми	Предоставляет набор метрик для оценки LLM-ответов, но с акцентом на гибкость и интеграцию с популярными ML-фреймворками для точечной оценки
Интеграция с другими системами	Хорошо интегрируется с LangChain для отладки и мониторинга цепочек, включая поддержку мультимодальных задач и сложных QA-пайплайнов	Открытая интеграция с популярными LLM и ML-фреймворками, позволяющая гибко подключать различные источники для проведения оценок и тестов
Автоматическое тестирование	Фокусируется на непрерывном автоматическом тестировании в продакшене с помощью платформы	Поддерживает автоматическое тестирование моделей и их конфигураций, но больше заточен на оффлайн-проверки и оптимизацию в разработке

Выбор за вами!

Тестирование кастомными метриками

GEval метрики

В процессе работы с LLM мы поняли, что тестирование моделей с точки зрения бизнес-ценности - не такое уж и очевидное дело. Классические тесты и готовые метрики не всегда подходят.

Вот, например, простой сценарий: проверка языка ответа модели. Если модели подается системный промпт и контекст на английском, а вопрос пользователя может быть на любом языке, есть вероятность, что модель ответит на английском, даже при явной инструкции "ответить на языке пользователя". Визуально это проверить легко, но что делать, если запросов тысячи? Здесь на помощь приходят кастомные метрики GEval.

Что вообще такое GEval?

GEval — это метрика из набора DeepEval.

Она оценивает ответ модели по заданным вами критериям, выставляя скор от 0 до 1. В отличие от готовых метрик, где критерии уже прописаны, здесь вы можете задавать свою логику оценки. Хочется проверить язык ответа? Или количество предложений? А может, стиль и тон общения? GEval даст вам такую возможность.

Как создать такую метрику?

Чтобы создать свою метрику, реализуйте её через класс GEval:

GEval(
    name="Имя вашей метрики",
    criteria="Критерии для оценки"
    evaluation_steps=[
        "Степ 1",
        "Степ 2",
        "Степ 3"
    ],
    verbose_mode=True,
    threshold=0.7,
    evaluation_params=[
        LLMTestCaseParams.INPUT,
        LLMTestCaseParams.ACTUAL_OUTPUT,
    ],
)

Можно использовать или критерий, или шаги (степы):

Критерий: модель сама распишет шаги оценки, исходя из критерия.
Шаги: вы прописываете последовательные действия, которые модель выполнит для выставления скора.

Что выбрать?

Когда мы только начали использовать GEval, мы использовали степы. Нам казалось, что это более чистый и последовательный подход. НО! Мы были не совсем правы. Изучив подробно официальную статью по GEval, мы поняли, что степы написать не так то просто, как кажется. Степы - это не просто последовательность действий, а четкая инструкция для модели, заставляющая ее действовать в рамках Chain of Thought.

Поэтому вот как мы рекомендуем работать с GEval:

Начните с критерия.
Посмотрите, как модель интерпретирует ваши требования в логах, и при необходимости корректируйте критерий.
Когда вы увидете степы, идеально подходящие под ваш кейс, просто скопируйте их и вставьте в evaluation_steps.

Такой подход даст вам наибольшую стабильность метрики, так как степы не будут генерироваться но-новому каждый запуск.

Как написать хороший критерий?

Определите границы метрики:
Что именно должно оцениваться? Не смешивайте проверку нескольких параметров в одной метрике.
Определите идеальный ответ:
Перечислите по пунктам, какой ответ вы ждете.
Например, для рецепта: список продуктов, подготовка, шаги приготовления, варианты подачи.
Пропишите критерий.
Критерий должен описывать, каким должен быть эталонный ответ. Например:
"Ответ написан на русском языке. Ответ связанный, последовательный, не содержит лишних предложений."

Какие параметры передавать в метрику?

Каждая метрика обязательно принимает input и output, чтобы установить связь вопрос-ответ. Дополнительные параметры могут включать контекст, ожидаемый ответ и так далее, исходя из вашего кейса. Это нужно для полного контекста оценки.

Что такое Threshold и как его выставлять

Threshold — это минимальный скор метрики, который считается успешным при прохождении теста. Проще говоря, это пороговое значение от 0 до 1, где ближе к 1 — значит лучше ответ модели соответствует заданным критериям.

Честно говоря, мы сами все еще разбираемся с этими значениями. Все дело в том, что одна нестабильная модель оценивает другую нестабильную модель. Так что, даже если ваш ответ идеален, не стоит рассчитывать на то, что он всегда будет получать заветную единицу.

Как же мы поступаем с этим threshold'ом?

Модель, кроме скора, всегда дает пояснение (ризон), где перечислены плюсы и минусы ответа. Если в ризоне нет указаний на недостатки, то и скор может считаться положительным.

Вот как мы определяем threshold:

Создаем заведомо плохой ответ
Пропускаем его через метрику и анализируем скор и ризон
Постепенно улучшаем плохой ответ, следя за повышением скора
Как только ответ достигает минимально приемлемого уровня для нашего кейса, фиксируем его скор

Этот скор и станет вашим ориентировочным threshold'ом.

Нам пока не удалось выработать четкое понимание того, как именно планировалось работать с threshold по задумке создателей фреймворка. Возможно, эта информация появится в документации со временем. Пока же, мы идем путем проб и ошибок и учимся на собственном опыте.

Как выбрать подход

Выбор подхода — это, пожалуй, самая сложная и важная задача при тестировании LLM. Вот что мы обычно делаем, чтобы определиться:

Пересматриваем accepts критерии:
Возвращаемся к основным критериям, разработанным на этапе планирования продукта или истории.
Выписываем конкретные требования:
Определяем, каким стандартам должен соответствовать ответ модели, цепочки или агента.
Прописываем способы проверки для каждого требования:
Например, если требования к ответу:
- json с ключами name, age, hobbies
- имя должно быть в им. падеже с заглавной буквы
- возраст должен быть целым положительным числом
- хобби должны быть листом со строками
- все пункты должны быть правдивыми и соответствующими поданному на вход тексту
- хобби должны включать все перечисленные к тексте
- содержание ключей должно быть на языке запроса
То способы тестирования каждого пункта будут:
- json с ключами name, age, hobbies - простой тест на соответствие объектов
- имя должно быть в им. падеже с заглавной буквы - кастомная метрика
- возраст должен быть целым положительным числом - простой тест на тип
- хобби должны быть листом со строками - простой тест на тип
- все пункты должны быть правдивыми и соответствующими поданному на вход тексту - галлюцинация
- хобби должны включать все перечисленные к тексте - кастомная метрика
- содержание ключей должно быть на языке запроса - кастомная метрика
После этого мы приступаем к реализации всех необходимых тестов, следуя правилам, описанным в предыдущих разделах.

Разбор реального примера

Мы хотим разработать цепочку, которая будет:

Принимать на вход детскую сказку
Анализировать сказу
Возвращать нам имя главного персонажа и его описание

И так, начнем с критериев, которые мы ожидаем от ответа:

В ответе есть описание характера персонажа
Описание характера пишется моделью на основе анализа сказки, оно не обязательно есть в самой сказке прямым текстом
В ответе есть имя персонажа
Имя персонажа пишется с большой буквы в им. падеже

Мы написали какой-то промпт модели и составили цепочку chain. Сначала пишем класс для формата ответа модели:

class Character(BaseModel):
    name: str = Field(..., description="Character's name in nom. case and capitalized")
    description: str = Field(..., description="Character description")

Теперь решим, какими методами будем оценивать каждый критерий ответа:

классический тест на тип и не пустое значение
кастомная метрика
классический тест на тип и не пустое значение
классический тест на соответствие значений

Напишем тесты для каждого критерия:

fairy_tale = "There is a goose Ivan. Ivan is a very communicative goose"


#Check that class has NAME and DESCRIPTION present and not empty
def test_output():
    result: Character = chain.invoke({'fairytale': fairy_tale})
    assert result.name
    assert result.description


#Check that NAME and DESCRIPTION are strings
def test_output_types():
    result: Character = chain.invoke({'fairytale': fairy_tale})
    assert isinstance(result.name, str)
    assert isinstance(result.description, str)


#Check that character description is relevant with fairy tale
def test_character_description():
    result: Character = chain.invoke({'fairytale': fairy_tale})
    description = result.description

    metric = GEval(
        name="Character description corectness",
        criteria="Check that the character description is correct and matches the text of the fairy tale",
        verbose_mode=True,
        threshold=0.7,
        evaluation_params=[
            LLMTestCaseParams.INPUT,
            LLMTestCaseParams.ACTUAL_OUTPUT,
        ],
    )

    assert_test(
        test_case=LLMTestCase(
            input=f"Fairy tail: {fairy_tale}",
            actual_output=description
        ),
        metrics=[metric]
    )

#Check that name is correct and in nom. case and capitalized
def test_name():
    result: Character = chain.invoke({'fairytale': fairy_tale})
    name = result.name
    assert name == 'Ivan'

Ссылка на колаб с кодом

Threshold тут выставлен 0.7 - для примера. В реальных тестах используйте логику выставления, о которой мы рассказывали, или же пробуйте свою!

Вот и все! Теперь ваши приложения надежно защищены тестами, которые можно обновлять и добавлять, и при этом - всегда следить за их зеленым цветом!

Эта статья была подготовлена под руководством @pletinsky. Выражаем особую благодарность за помощь в создании материала.

English version of this article is available here.

Если хотите погрузиться поглубже

Официальные документации фреймворков: LangSmith testing, DeepEval

Документация GEval: GEval

Статьи про тестирование: Testing of LLM models, Evaluating Outputs , How to Test LLM Applications

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26 нояб. 2024 г.

07.09.23 16:24 CherryTeam

Cherry Team atlyginimų skaičiavimo programa yra labai naudingas įrankis įmonėms, kai reikia efektyviai valdyti ir skaičiuoti darbuotojų atlyginimus. Ši programinė įranga, turinti išsamias funkcijas ir patogią naudotojo sąsają, suteikia daug privalumų, kurie padeda supaprastinti darbo užmokesčio skaičiavimo procesus ir pagerinti finansų valdymą. Štai keletas pagrindinių priežasčių, kodėl Cherry Team atlyginimų skaičiavimo programa yra naudinga įmonėms: Automatizuoti ir tikslūs skaičiavimai: Atlyginimų skaičiavimai rankiniu būdu gali būti klaidingi ir reikalauti daug laiko. Programinė įranga Cherry Team automatizuoja visą atlyginimų skaičiavimo procesą, todėl nebereikia atlikti skaičiavimų rankiniu būdu ir sumažėja klaidų rizika. Tiksliai apskaičiuodama atlyginimus, įskaitant tokius veiksnius, kaip pagrindinis atlyginimas, viršvalandžiai, premijos, išskaitos ir mokesčiai, programa užtikrina tikslius ir be klaidų darbo užmokesčio skaičiavimo rezultatus. Sutaupoma laiko ir išlaidų: Darbo užmokesčio valdymas gali būti daug darbo jėgos reikalaujanti užduotis, reikalaujanti daug laiko ir išteklių. Programa Cherry Team supaprastina ir pagreitina darbo užmokesčio skaičiavimo procesą, nes automatizuoja skaičiavimus, generuoja darbo užmokesčio žiniaraščius ir tvarko išskaičiuojamus mokesčius. Šis automatizavimas padeda įmonėms sutaupyti daug laiko ir pastangų, todėl žmogiškųjų išteklių ir finansų komandos gali sutelkti dėmesį į strategiškai svarbesnę veiklą. Be to, racionalizuodamos darbo užmokesčio operacijas, įmonės gali sumažinti administracines išlaidas, susijusias su rankiniu darbo užmokesčio tvarkymu. Mokesčių ir darbo teisės aktų laikymasis: Įmonėms labai svarbu laikytis mokesčių ir darbo teisės aktų, kad išvengtų baudų ir teisinių problemų. Programinė įranga Cherry Team seka besikeičiančius mokesčių įstatymus ir darbo reglamentus, užtikrindama tikslius skaičiavimus ir teisinių reikalavimų laikymąsi. Programa gali dirbti su sudėtingais mokesčių scenarijais, pavyzdžiui, keliomis mokesčių grupėmis ir įvairių rūšių atskaitymais, todėl užtikrina atitiktį reikalavimams ir kartu sumažina klaidų riziką. Ataskaitų rengimas ir analizė: Programa Cherry Team siūlo patikimas ataskaitų teikimo ir analizės galimybes, suteikiančias įmonėms vertingų įžvalgų apie darbo užmokesčio duomenis. Ji gali generuoti ataskaitas apie įvairius aspektus, pavyzdžiui, darbo užmokesčio paskirstymą, išskaičiuojamus mokesčius ir darbo sąnaudas. Šios ataskaitos leidžia įmonėms analizuoti darbo užmokesčio tendencijas, nustatyti tobulintinas sritis ir priimti pagrįstus finansinius sprendimus. Pasinaudodamos duomenimis pagrįstomis įžvalgomis, įmonės gali optimizuoti savo darbo užmokesčio strategijas ir veiksmingai kontroliuoti išlaidas. Integracija su kitomis sistemomis: Cherry Team programinė įranga dažnai sklandžiai integruojama su kitomis personalo ir apskaitos sistemomis. Tokia integracija leidžia automatiškai perkelti atitinkamus duomenis, pavyzdžiui, informaciją apie darbuotojus ir finansinius įrašus, todėl nebereikia dubliuoti duomenų. Supaprastintas duomenų srautas tarp sistemų padidina bendrą efektyvumą ir sumažina duomenų klaidų ar neatitikimų riziką. Cherry Team atlyginimų apskaičiavimo programa įmonėms teikia didelę naudą - automatiniai ir tikslūs skaičiavimai, laiko ir sąnaudų taupymas, atitiktis mokesčių ir darbo teisės aktų reikalavimams, ataskaitų teikimo ir analizės galimybės bei integracija su kitomis sistemomis. Naudodamos šią programinę įrangą įmonės gali supaprastinti darbo užmokesčio skaičiavimo procesus, užtikrinti tikslumą ir atitiktį reikalavimams, padidinti darbuotojų pasitenkinimą ir gauti vertingų įžvalgų apie savo finansinius duomenis. Programa Cherry Team pasirodo esanti nepakeičiamas įrankis įmonėms, siekiančioms efektyviai ir veiksmingai valdyti darbo užmokestį. https://cherryteam.lt/lt/
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The "Shibarium for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH. The Plot Thickens. Someone posted the actual transactions!!!! https://bscscan.com/tx/0xa846ea0367c89c3f0bbfcc221cceea4c90d8f56ead2eb479d4cee41c75e02c97 It seems the article is true!!!! And it's also FUD. Let me explain. Check this link: https://bscscan.com/token/0x5a752c9fe3520522ea88f37a41c3ddd97c022c2f So there really is a "Shibarium" token. And somebody did a rug pull with it. CONFIRMED. But the "Shibarium" token for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH.
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01.10.24 14:54 Sinewclaudia

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02.10.24 22:27 Emily Hunter

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31.10.24 00:13 ytre89

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I never could have imagined the nightmare of losing access to my digital wallet. All of my cryptocurrency holdings were abruptly imprisoned, inaccessible, and appeared to be lost forever following a catastrophic hardware breakdown. Years of meticulous investment and careful saving were reduced to nothing more than strings of code that I could no longer control, and I could feel the dread and sorrow that swept through me at that very instant. Thankfully, during my worst moment, I came into (TRUST GEEKS HACK EXPERT), a professional service devoted to recovering lost or inaccessible digital data. With optimism, I went out to their team of skilled technologists, laying bare the full nature of my issue. What followed was a laborious, multi-step process that required an almost surgical level of digital forensics and Bitcoin skill. In order to create a thorough profile of my wallet's contents and activities, the (TRUST GEEKS HACK EXPERT) team first thoroughly examined the transaction history and metadata connected to it. Next, they implemented a series of advanced recovery techniques, using cutting-edge software tools to bypass the access barriers that had left me locked out. The entire process was shrouded in secrecy and discretion, with the (TRUST GEEKS HACK EXPERT) team working tirelessly to protect the confidentiality of my sensitive financial information. After what felt like an eternity of nervous anticipation, the day finally arrived when I received the triumphant notification – my wallet had been successfully restored, and all of my precious digital assets had been returned to my control. The sense of relief was indescribable, as I could finally breathe easy knowing that the fruits of my financial discipline had been safeguarded. While the experience of losing access to my wallet was undoubtedly traumatic, (TRUST GEEKS HACK EXPERT) intervention allowed me to emerge from the ordeal with my cryptocurrency holdings intact, and a renewed appreciation for the importance of proactive digital asset management. You can contact Them through EMAIL: [email protected] - TELEGRAM: TRUSTGEEKSHACKEXPERT
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