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Поиск данных, прокачанная тренировка и судейская оценка. Как с минимальными ресурсами улучшить качество дообучения LLM

Привет, Хабр! Меня зовут Анна Щеникова, я аналитик в Центре RnD в МТС Диджитал. Почти всегда при адаптации LLM-моделей встает вопрос нехватки ресурсов на проверку гипотез. Обычно у меня есть собственное рабочее время и две карточки GPU, а распределяются они на несколько задач. Бизнес же просит приемлемый результат как можно быстрее.

В прошлом посте я рассказала про разделение адаптации open-source-моделей на четыре уровня, а в этом раскрою работу с последним из них — дообучением. Под катом покажу, как быстро получить приемлемое качество, когда базовые подходы не помогают.

Исходная задача

Допустим, нужно обучить модель работать с технологией RAG или с помощью LLM генерировать инсайты по нашим данным. Как это сделать быстро, дешево и сердито?

Нужен подход, который легко применять локально. Чтобы его отработать и использовать на реальных кейсах, мы решили обучить модели русскому языку. Так мы не замкнемся на одной задаче и протестируем большое количество гипотез.

Да, логичнее было бы использовать подходы адаптации к новому языку. Например, обучить новый токенайзер и адаптировать эмбеддинги. Но с точки зрения бизнеса нам нужен успешный опыт, чтобы получить быстрое решение задач без проверки множества гипотез.

Для хорошего обучения требуется собрать данные, понять и оформить тренировочный пайплайн и, самое важное, определиться с оценкой качества. С этого и начнем.

Оценка качества

Есть разные подходы для оценки ответов моделей. Мы выделили для себя три группы:

Бенчмарки, отвечающие на вопрос «Есть ли ответ в сгенерированном тексте?» Пример: MMLU, Russian SuperGLEU (RSG), MERA. Они хорошо проверяют факты, но плохо оценивают сам язык. Например, не подсвечивают, что модель ошибается в правилах грамматики и пунктуации, путает слова, генерирует один и тот же текст без конца и много чего еще. Забавный факт: мы оценили модель с большими проблемами в грамматике, но она неожиданно была лучшей по RSG.
Бенчмарки типа LLM-судьи. Одна LLM сравнивает ответы других моделей по десятибалльной шкале. Так более или менее учитывается качество языка, и можно получить неплохую приближенную оценку ответов.
Ручная Side-by-side (SBS) оценка. Самый точный, но дорогой и долгий вариант. Берем заранее определенный тестовый набор данных, модели инференсят на нем и получают некоторые ответы. Люди вручную сравнивают их и выставляют перекрестные оценки: лучший/худший, одинаково хороший/плохой ответ.

При любом из этих подходов результаты своей модели можно подогнать так, что они будут лучше, чем на самом деле. Например, обучить модель на открытых тестовых данных. Также метрики станут нерепрезентативными при использовании данных для few-shot-инференса в качестве обучающих.

Самым надежным остается вариант, когда проводится самостоятельная SBS-оценка. От обычной SBS-оценки она отличается только отсутствием тестового набора данных для сравнения. Пользователи просто задают и сравнивают ответы. Так работает ChatBot Arena. С появлением детерминированного набора проверяемых данных возникает соблазн пойти нечестным путем и подогнать метрики.

Чтобы сравнивать ответы моделей на разных итерациях обучения, я использую оценку GPT-судьей. Давайте посмотрим на подход поближе: в качестве судьи выступает GPT-4o, которая показывает самые высокие метрики, в том числе и на арене, а еще это одна из самых дешевых моделей из семейства GPT.

Промпт для LLM-судьи выглядит так:

Please act as an impartial judge and evaluate the quality of the responses provided by two AI assistants to the user question displayed below. You should choose the assistant that follows the user's instructions and answers the user's question better. Your evaluation should consider factors such as the helpfulness, relevance, accuracy, depth, creativity, and level of detail of their responses. Begin your evaluation by comparing the two responses and provide a short explanation. Avoid any position biases and ensure that the order in which the responses were presented does not influence your decision. Do not allow the length of the responses to influence your evaluation. Do not favor certain names of the assistants. Be as objective as possible. After providing your explanation, output two values on a scale of 1 to 10 indicating the scores for Assistant A and B, respectively. Output your final verdict by strictly following this format: [[{{Assistant A score}} {{Assistant B score}}]].

[User Question]
{instruction}

[The Start of Assistant A's Answer]
{answer_a}
[The End of Assistant A's Answer]

[The Start of Assistant B's Answer]
{answer_b}
[The End of Assistant B's Answer]

Судье передается вопрос и два ответа: от эталонной модели A (gpt-4-turbo) и оцениваемой модели B (например, llama3-8b)

Важно учитывать несколько моментов:

Анонимность ответов. Судья не знает и не учитывает, от какой модели приходит ответ.
Судья и эталонная модель должны различаться.
Ответы нужно рандомно менять местами, чтобы избежать смещения в пользу какой-то одной модели. Существует гипотеза, что первый ответ выбирается чаще.
В промпте можно менять требования к ответам.
Можно смотреть долю соответствия с эталонной моделью и средние оценки. На основе промпта для LLM судьи ставят оценки по десятибалльной шкале и подробно объясняют, почему. Это гораздо интереснее, чем смотреть по четырем категориям: «ответ А лучше Б, ответ Б лучше А, оба хороши, оба плохи».

Такой способ не требует ручных действий и не отнимает мое время. Он оценивает не только качество языка, но и соответствие ответа требованиям. В результате оценки я получаю такую диаграмму:

Тут видно, что у модели есть проблема с чатом и советами

Теперь мы можем выбирать оценочный датасет и отрасли под конкретные задачи и мониторить состояние модели сразу по всем подзадачам.

Данные

Краеугольным камнем в общении с бизнесом становятся данные для обучения. Их нужно получить с учетом специфики задачи, и для этого есть два пути: оттолкнуться от чего-то готового или сделать все самому.

Где найти данные

При первом пути для прототипа (Proof-of-Concept) и теста гипотезы в большинстве случаев достаточно двух вариантов получения данных:

Поиск готовых датасетов. Их можно взять на huggingface и перевести, если они на другом языке. На этом ресурсе много различных датасетов по самым разным задачам. Их можно использовать для обобщения какого-то навыка модели.
Например, мы хотим качественно суммаризировать данные внутри компании по структуре, но нам необязательно собирать внутреннюю информацию, чтобы получить навык. Достаточно использовать датасеты для суммаризации и обработать их так, как нам надо.
Генерация синтетики по существующим данным: базам текстов, примеров запросов от пользователей и так далее. При этом подходе мы сами «генерируем» датасет, если у нас есть какой-то материал, но его мало или ему нужно преобразование. Главное для нас — возможность определять требования для генерации данных и уточнять нужные моменты.
Например, у вас есть десяток диалогов с пользователями и нужно получить их варианты с небольшими отличиями — их можно сгенерировать на основе имеющихся. Или есть база документов, и чат-бот должен отвечать по ней и иметь общие знания по теме. Мы можем с помощью ChatGPT или какой-то локальной модели (например, LLama 3.1 70b) сгенерировать пары «вопрос-ответ» по тексту так, чтобы использовать его как релевантный контекст в RAG-системе.

Второй путь — сделать все самому — подразумевает ручные сборку и оформление данных, написание текстов. Как-то раз редакторы заказчика сделали около восьмидесяти подписей к картинкам, чтобы обучить нейросеть. Даже этого набора хватило для дообучения модели и получения работающего результата.

В текущей задаче мы сфокусировались на поиске максимально готовых решений, которые даже не нужно переводить.

Как должны выглядеть данные для обучения

Если мы говорим про LLM, то есть два типа моделей:

Базовая — это продвинутый T9, предсказывающий только следующий токен. На вход подается какой-то текст, и она его просто продолжает. Пример: meta-llama/Meta-Llama-3.1-8B.
Instruct-версия — это базовая модель, которая была дообучена общаться в чате или в instruct-формате. Она поддерживает роли пользователя и ассистента, и мы можем ей задавать вопросы и получать на них ответ. Наши данные должны выглядеть как-то так:

Базовой модели на вход для обучения нужен просто текст. Для instruct-версии определяем роли, а данные должны представлять собой список сообщений для каждой из них.

Но не все так просто с instruct-версией. На самом деле для модели методы Prompt (base) и Instruct кардинально ничем не отличаются. Instruct-версия — это модель, обученная определенной структуре текста. Мы специфично оформляем промпт, а Instruct так же, как и базовая, предсказывает только следующий токен, но в определенной структуре. И эту структуру мы можем распарсить и выделить ответ от ассистента. Она называется темплейт и различается у каждой модели:

Желтым помечен вход модели, а все остальное — то, что она генерирует

Да, это такой же текст, просто оформленный с разными дополнительными специальными токенами. Он красиво парсится в UI и преобразуется в привычный нам вид:

В процессе дообучения модели важно оформлять данные в предназначенном для нее виде. Базовой достаточно отдать просто текст, а для instruct все данные оформляются в ее чат-темплейте, а уже потом используются в обучении.

С чего мы начали

Мы нашли на Hugging Face датасеты Saiga, нагенерировали запросов в ChatGPT, взяли переведенный на русский язык d0rj/OpenOrca-ru, все в instruct-версии.

Так у нас появилась база, на которой мы обучили модель и оценили ее с помощью LLM-судьи:

Дальше мы добавили русскоязычных данных на специфические задачи:

spellcheck — проверяет и исправляет грамматику русского языка;
wikiomnia — отвечает на вопросы по тексту из «Википедии» (QCA);
gsm8k — базовые задачи по математике.

После обучения и замеров мы получили такие результаты:

Видно, что добавление данных по отдельности повышает соответствие с GPT-4, но есть две проблемы:

Хороших русскоязычных данных действительно мало, зато много переводов, которые могут быть плохого качества. Мы замечали, что плохо переводится код — где-то даже был переведен на русский синтаксис Python. Нужна либо проверка на адекватность, либо умная фильтрация. А на них, увы, времени нет.
Чем больше данных, тем хуже качество. Например:

Задача: Перепиши текст, исправь грамматические, орфографические и пунктуационные ошибки.
«Если бы сказали мне год назад сегодня я бежать марафон, я бы смеяться. Ваша поддержка иметь мне огромное влияние!»
base и base + gsm8k:
Если бы сказали мне год назад, что сегодня я бегу марафон, я бы смеялся. Ваша поддержка имеет огромное значение для меня!
base + spellcheck + wikiomnia + gsm8k:
Если бы сказали мне год назад сегодня я бежать марафон, я бы смеяться. Ваша поддержка имеет мне огромное влияние!

Мы добавляем больше данных, чтобы улучшить качество языка, но оно только падает.

Тут мы подумали, что проблема может быть в разнообразии данных, и собрали еще больше двадцати датасетов по специфическим задачам на русском и английском языках. Отревьюили их на адекватность и стали искать минимальную комбинацию, которая даст лучшее качество. Для нас идеальным датасетом оказался микс из базы, задач на код, логику, математику, грамматику, QCA. Все они были русскоязычными.

А что, если пытаться подтянуть одну отрасль?

Мы увидели, что на графике просаживается roleplay, и решили в наши данные добавить датасет на эту задачу. Мы надеялись улучшить метрики по этой области. Но нет, метрики упали, а по этой отрасли изменения практически не было.

Разнообразие данных помогло нам чуть улучшить метрики, но не сильно. Главным препятствием оставалось падение качества при увеличении объема данных. Мы поняли, что нам пора изменить подход.

Тренировочный пайплайн

Чтобы разобраться со снижением качества, мы решили глубже взглянуть на наш подход. Начали свое исследование мы, конечно, с Low-Rank Adaptation (LoRA):

В этой статье описано, что такое LoRA и как применить ее к конкретным задачам. А тут есть код использования LoRA для LLM

Мы делаем копию весов некоторых слоев обученной модели, например Mistral. Чтобы уменьшить количество обучаемых параметров, копия разбивается на две матрицы A и В. Они перемножаются для получения оригинальной размерности, а исходные веса замораживаются. Обучаемая копия и оригинальные веса конкатенируются на выходе, а в процессе инференса мержатся по этой конкатенации.

Понятный пример: у нас есть дом, и мы хотим выделить место для хранения инструментов. Чтобы не лезть в конструкцию и не переделывать все внутри, мы пристраиваем к нему сарай (LoRa adapter) и получаем нужную функциональность, ничего не сломав.

Изначально мы использовали не только LoRA на float16 (обучение полноразмерной модели), но и обучение на основе квантизованных моделей, например LoRA 8 bit. Такой подход требует меньше памяти и сразу адаптируется к условиям инференса в квантизации. Но он увеличивает время обучения и ухудшает грамматику.

Чтобы улучшить качество ответов, мы решили потюнить гиперпараметры, но толку было мало:

Практически все итерации с большим количеством данных хуже, чем без них

К тому же мы заметили, что при обучении ровно одной эпохи лучшее качество получается в ее середине:

Наша валидационная лосс-функция имела форму колокола. Качество было выше, когда использовалось ровно определенное количество данных: чем больше мы учили модель, тем хуже она показывала результаты.

Меняем подход

Мы много тестировали и остановились на LoRA с префикс-тюнингом:

Префикс-тюнинг заключается в добавлении LoRa-весов к соответствующим матрицам W в механизме внимания и виртуальных эмбеддингов к матрицам К и V. Так мы чуть расширяем пространство эмбеддингов внутри внимания. Подробности есть в статье «Parameter-Efficient Fine-Tuning (PEFT): методы LoRA, Prefix tuning, Prompt tuning и Adapters».

Мы сразу заметили улучшение метрики:

У нас чуть старые GPU, поэтому в квантизации все становится сииильно долго по времени

Увы, памяти стало требоваться больше, но проблему «больше данных — хуже качество» мы пофиксили. Ура!

При тюнинге подхода к обучению мы еще обращали внимание на:

Гиперпараметры LoRA. Даже с интеграцией префикс-тюнинга мы искали хорошие гиперпараметры rank и rank_alpha для LoRA.
Learning rate. А куда без него? С его помощью мы хорошо контролировали момент переобучения.
Время обучения. Чем быстрее гипотезы проходят проверку, тем лучше.

Улучшаем подход к дообучению

Direct Preference Optimization

В процессе работы мы с коллегами увидели статью про DirectPreferenceOptimization (DPO) и решили попробовать внедрить его к себе.

Если мы используем какую-то готовую модель, то знаем, что она прошла множество разных этапов обучения. Instruct-версии обычно проходят несколько после претрейна:

Подробнее про каждый из этапов я рассказала в прошлой статье

Мы заметили, что наш подход LoRA + Prefix tuning можно обозначить как своего рода SFT. Значит, для повышения качества можно использовать обучение с подкреплением. Таких подходов много, и один из самых последних — это ORPO, но нам хватило DPO:

Допустим, у нас есть запрос от пользователя write me a poem about the history of jazz и ответы $y_1$ и $y_2$, где $y_1$ лучше $y_2$. Мы выбираем победителем первый ответ.

Такие данные мы можем использовать для обучения с подкреплением на основании человеческого фидбэка (RLHF). Здесь мы одновременно тренируем модель вознаграждения, которая будет оценивать генерируемые ответы и саму LLM.

Или можно применить DPO, где напрямую, без модели вознаграждения, будем учить LLM в соответствии с данными, размеченными на хорошие и плохие. Подробнее об этом методе можно почитать в этой статье.

После интеграции DPO в наш пайплайн обучения мы получили такие результаты:

Сначала качество выросло при интеграции LoRa с префикс-тюнингом, а затем метрики улучшились с DPO на 9-13%

У этого подхода есть свои особенности:

DPO просто реализовать. Он дает более быструю сходимость, больший контроль над LLM и эффективность вычислений, чем RLHF. При этом ему нужно меньше ресурсов, чем RLHF.
Для DPO нужно много памяти (160 Гб GPU на 2к контекста для 7B-модели) и больше времени (добавляется от 12 до 24 часов на обучение).
Интересно, что DPO задает ограничения, а RLFH показывает возможности. На примере родителей и детей: DPO скажет «а ну не суй пальцы в розетку», а RLFH — «время порисовать». C помощью DPO мы можем лучше контролировать поведение модели и ограничивать ее, а RLHF создает более творческий, но менее управляемый процесс. Не факт, что обучение им приведет к качественному результату.
Для DPO подойдут как русско-, так и англоязычные датасеты. Мы брали с Huggingface.

Комбинации способов адаптации

После обучения решили потюнить системный промпт у модели и получили интересные результаты:

Несмотря на долю среднего соответствия в 1.21, модель оказалась где-то лучше, а где-то хуже GPT-4. Например, в подзадачах открытых и закрытых вопросов, чате и переводе оценки у нее в среднем выше, чем у GPT-4. Однако у них сильный разбег доверительных интервалов. Есть большое количество оценок соответствия меньше 1.0, то есть наша модель часто отвечает хуже GPT-4. Около 18 пунктов среднего соответствия модели с GPT-4 нам дало обучение, а еще 20 — модификация системного промпта. Это значительный прирост показателей.

Другой график также показывает, что модель работает на уровне GPT-4:

Ответы распределились примерно поровну: половина лучше GPT-4 (зеленый цвет), а половина хуже (оранжевый).

Выводы

На этом у меня все. Если вы столкнетесь с похожей задачей, то выбирайте подход к оценке качества во время тюнинга крайне аккуратно. От него зависит, как хорошо вы увидите недостатки модели и сформулируете гипотезы для ее улучшения.

В нашем решении мы использовали качественного LLM-судью в виде GPT-4o. При работе с данными оказался эффективным микс open-source русскоязычных и англоязычных данных, которые мы проверяли на адекватность перевода. Хорошо показал себя тренировочный пайплайн из LoRa c префикс-тюнингом и DPO.

Повысить качество можно с помощью комбинации разных подходов адаптации, тюнинга промптов, внедрения агентов. Об этом я рассказывала в своей прошлой статье. Если у вас возникли вопросы, пишите в комментариях.

Полезные ссылки

Github-репозиторий, где описана вся методология работы LLM-судьи.
Github-репозиторий Ильи Гусева (@Takagi), который много сделал для open-source русскоязычных моделей.
Статья про LoRa и Prefix tuning.
OpenRLHF — github-репозиторий с методами RLHF и DPO.
DPO paper — описание метода Direct Preference Optimization.
Prefix tuning paper — описание метода префикс-тюнинга.
Lora paper — описание подхода Low-Rank Adaptation.

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19 сент. 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/
08.10.23 01:30 davec8080

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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02.10.24 22:27 Emily Hunter

Can those who have fallen victim to fraud get their money back? Yes, you might be able to get back what was taken from you if you fell prey to a fraud from an unregulated investing platform or any other scam, but only if you report it to the relevant authorities. With the right plan and supporting documentation, you can get back what you've lost. Most likely, the individuals in control of these unregulated platforms would attempt to convince you that what happened to your money was a sad accident when, in fact, it was a highly skilled heist. You should be aware that there are resources out there to help you if you or someone you know has experienced one of these circumstances. Do a search using (deftrecoup (.) c o m). Do not let the perpetrators of this hoaxes get away with ruining you mentally and financially.
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31.10.24 00:13 ytre89

Can those who have fallen victim to fraud get their money back? Yes, you might be able to get back what was taken from you if you fell prey to a fraud from an unregulated investing platform or any other scam, but only if you report it to the relevant authorities. With the right plan and supporting documentation, you can get back what you've lost. Most likely, the individuals in control of these unregulated platforms would attempt to convince you that what happened to your money was a sad accident when, in fact, it was a highly skilled heist. You should be aware that there are resources out there to help you if you or someone you know has experienced one of these circumstances. Do a search using (deftrecoup (.) c o m). Do not let the perpetrators of this hoaxes get away with ruining you mentally and financially.
02.11.24 14:44 diannamendoza732

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12.11.24 00:50 TERESA

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17.11.24 09:31 Vivianlocke223

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Being a parent is great until your toddler figures out how to use your devices. One afternoon, I left my phone unattended for just a few minutes rookie mistake of the century. I thought I’d take a quick break, but little did I know that my curious little genius was about to embark on a digital adventure. By the time I came back, I was greeted by two shocking revelations: my toddler had somehow managed to buy a $5 dinosaur toy online and, even more alarmingly, had locked me out of my cryptocurrency wallet holding a hefty $75,000. Yes, you heard that right a dinosaur toy was the least of my worries! At first, I laughed it off. I mean, what toddler doesn’t have a penchant for expensive toys? But then reality set in. I stared at my phone in disbelief, desperately trying to guess whatever random string of gibberish my toddler had typed as a new password. Was it “dinosaur”? Or perhaps “sippy cup”? I felt like I was in a bizarre game of Password Gone Wrong. Every attempt led to failure, and soon the laughter faded, replaced by sheer panic. I was in way over my head, and my heart raced as the countdown of time ticked away. That’s when I decided to take action and turned to Digital Tech Guard Recovery, hoping they could solve the mystery that was my toddler’s handiwork. I explained my predicament, half-expecting them to chuckle at my misfortune, but they were incredibly professional and empathetic. Their confidence put me at ease, and I knew I was in good hands. Contact With WhatsApp: +1 (443) 859 - 2886 Email digital tech guard . com Telegram: digital tech guard recovery . com website link :: https : // digital tech guard . com Their team took on the challenge like pros, employing their advanced techniques to unlock my wallet with a level of skill I can only describe as magical. As I paced around, anxiously waiting for updates, I imagined my toddler inadvertently locking away my life savings forever. But lo and behold, it didn’t take long for Digital Tech Guard Recovery to work their magic. Not only did they recover the $75,000, but they also gave me invaluable tips on securing my wallet better like not leaving it accessible to tiny fingers! Who knew parenting could lead to such dramatic situations? Crisis averted, and I learned my lesson: always keep my devices out of reach of little explorers. If you ever find yourself in a similar predicament whether it’s tech-savvy toddlers or other digital disasters don’t hesitate to reach out to Digital Tech Guard Recovery. They saved my funds and my sanity, proving that no challenge is too great, even when it involves a toddler’s mischievous fingers!
21.11.24 08:02 Emily Hunter

If I hadn't found a review online and filed a complaint via email to support@deftrecoup. com , the people behind this unregulated scheme would have gotten away with leaving me in financial ruins. It was truly the most difficult period of my life.
22.11.24 04:41 [email protected]

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
22.11.24 15:26 cliftonhandyman

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22.11.24 23:43 teresaborja

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24.11.24 02:21 [email protected]

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
02:19 briankennedy

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02:20 briankennedy

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