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[Перевод] Учебник — всё, что вам нужно

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Немного вызывающее название статьи отсылает к известной работе Внимание - всё, что вам нужно. На этот раз речь пойдет о качестве данных, на которых обучают LLM. Оказывается, качественный учебник (как концентрат знаний в любой сфере) в разы сокращает потребность и в памяти, и в мощности GPU, и в деньгах инвесторов...

Эта статья - о качестве данных при обучении/файнтюнинге LLM. Классическая работа о количественных данных позволит лучше понять, на что влияет качество датасета: Законы масштабирования нейронных языковых моделей

Авторы: Suriya Gunasekar, Yi Zhang, Jyoti Aneja, Caio C´esar, Teodoro Mendes, Allie Del Giorno, Sivakanth Gopi, Mojan Javaheripi, Piero Kauffmann, Gustavo de Rosa, Olli Saarikivi, Adil Salim, Shital Shah, Harkirat Singh Behl, Xin Wang, Sebastien Bubeck, Ronen Eldan, Adam Tauman Kalai, Yin Tat Lee, Yuanzhi Li. Microsoft Research

2 октября 2023

Аннотация

Мы представляем phi-1, новую большую языковую модель для генерации кода, которая значительно меньше по размеру по сравнению с конкурирующими моделями: phi-1 — это модель на основе архитектуры трансформера с 1,3 миллиардами параметров, обученная в течение 4 дней на 8 A100, с использованием выборки данных "качество-как-в-учебнике" из интернета (6 миллиардов токенов) а также синтетически сгенерированных с помощью GPT-3.5 учебников и упражнений (1 миллиард токенов). Несмотря на такой небольшой масштаб, phi-1 достигает точности pass@1 50,6% на HumanEval и 55,5% на MBPP. Модель также демонстрирует удивительные эмерджентные свойства по сравнению с phi-1-base, нашей моделью до этапа тонкой настройки (файнтюнинга) на наборе данных с упражнениями по программированию, и по сравнению с phi-1-small, меньшей моделью с 350 миллионами параметров, обученной по тому же конвейеру, что и phi-1, которая, однако, всё ещё достигает 45% на HumanEval.

1. Введение

Искусство обучения больших нейронных сетей показало невероятный прогресс за последнее десятилетие, особенно после открытия архитектуры трансформера [*], однако научные знания, стоящие за этим успехом, пока остаются ограниченными. Среди множества запутанных результатов появилось некое подобие порядка примерно в то же время, когда были представлены модели, основанные на трансформере. Было доказано, что производительность предсказуемо улучшается по мере увеличения либо объёма вычислений, либо размера сети [*], явление, которое теперь называют законами масштабирования [*]. Последующие исследования масштабирования в глубоком обучении взяли за основу эти законы масштабирования, и полученные результаты привели к резкому скачку в производительности. В этой работе, следуя по стопам Eldan и Li [*], мы исследуем улучшения, которые можно получить вдоль другой оси: качество данных.

Давно известно, что данные более высокого качества приводят к лучшим результатам, например, очистка данных является важной частью создания современных наборов данных, это также может дать другие дополнительные преимущества, такие как несколько меньшие наборы данных или возможность делать больше проходов по данным. Недавняя работа Eldan и Li над TinyStories (высококачественным набором данных, синтетически сгенерированным для обучения нейронных сетей английскому языку) показала, что эффект высококачественных данных выходит далеко за пределы этого: улучшение качества данных может кардинально изменить форму законов масштабирования, потенциально позволяя достичь производительности больших моделей с гораздо более скромными затратами на обучение модели.

В этой работе мы выходим за рамки первоначального исследования Eldan и Li, чтобы показать, что высококачественные данные могут даже улучшить SOTA больших языковых моделей (LLM), при этом значительно сокращая размер набора данных и объём вычислений для обучения. Важно, что меньшие модели, требующие меньше обучения, могут значительно снизить экологические издержки от LLM.

Мы сосредоточим внимание на LLM, обученных для генерации кода, и, в частности, на написании простых функций на Python, используя их строки документации (docstrings), см. [*]. Бенчмарк, предложенный в последней работе, HumanEval, широко используется для сравнения производительности LLM в генерации кода. Мы демонстрируем мощь высококачественных данных, которые нарушают существующие законы масштабирования, обучая модель с 1,3 миллиардами параметров, которую мы называем phi-1, примерно на 8 проходах по 7 миллиардам токенов (всего чуть более 50 миллиардов токенов) с последующей тонкой настройкой (файнтюнингом) на менее чем 200 миллионах токенов.

Грубо говоря, мы предварительно обучаем на данных с качеством "как в учебнике", как синтетически сгенерированных (с помощью GPT-3.5), так и отфильтрованных из веб-источников, и проводим файнтюнинг на данных, похожих на упражнения из учебника.

Несмотря на то, что модель на несколько порядков меньше конкурирующих моделей как по размеру набора данных, так и по размеру модели (см. Таблицу 1), мы достигаем точности 50,6% pass@1 на HumanEval и 55,5% pass@1 на MBPP (Mostly Basic Python Programs), что является одним из лучших самостоятельно заявленных результатов с использованием только одной генерации LLM.

Таблица 1: Мы используем данные отчетов, если они доступны. Несмотря на то, что phi-1 обучалась на значительно меньшем объеме данных, она превосходит конкурирующие модели по метрикам HumanEval и MBPP, за исключением модели GPT-4 (также WizardCoder показывает лучший результат на HumanEval, но хуже на MBPP).

Дата

Модель

Размер (параметры) модели

Размер датасета (токенов)

HumanEval
(Pass@1)

MBPP
(Pass@1)

Jul 21
Jul 21
Mar 22
Mar 22
Apr 22
Sep 22
Nov 22
Dec 22
Mar 23
Apr 23
Apr 23
May 23
May 23
May 23
May 23
May 23
May 23
May 23
May 23
Jun 23

Codex-300M [CTJ + 21]
Codex-12B [CTJ + 21]
CodeGen-Mono-350M
CodeGen-Mono-16.1B
PaLM-Coder [CND + 22]
CodeGeeX [ZXZ + 23]
GPT-3.5 [Ope23]
SantaCoder [ALK + 23]
GPT-4 [Ope23]
Replit [Rep23]
Replit-Finetuned [Rep23]
CodeGen2-1B [NHX + 23]
CodeGen2-7B [NHX + 23]
StarCoder [LAZ + 23]
StarCoder-Prompted
PaLM 2-S [ADF + 23]
CodeT5+ [WLG + 23]
CodeT5+ [WLG + 23]
InstructCodeT5+
WizardCoder [LXZ + 23]

300M
12B
350M
16.1B
540B
13B
175B
1.1B
N.A.
2.7B
2.7B
1B
7B
15.5B
15.5B
N.A.
2B
16B
16B
16B

100B
100B
577B
577B
780B
850B
N.A.
236B
N.A.
525B
525B
N.A.
N.A.
1T
1T
N.A.
52B
52B
52B
1T

13.2%
28.8%
12.8%
29.3%
35.9%
22.9%
47,00%
14.0%
67,00%
21.9%
30.5%
10.3%
19.1%
33.6%
40.8%
37.6%
24.2%
30.9%
35.0%
57.3%

N.A.
N.A.
N.A.
35.3%
47.0%
24.4%
N.A.
35.0%
N.A.
N.A.
N.A.
N.A.
N.A.
52.7%
49.5%
50.0%
N.A.
N.A.
N.A.
51.8%

Jun 23

phi-1

1.3B

7B

50.6%

55.5%

CodeGen-Mono: [NPH + 23], StarCoder-Prompted: [LAZ + 23], InstructCodeT5+: [WLG + 23]

В Разделе 2 мы приводим некоторые детали нашего процесса обучения и обсуждаем доказательства важности нашего процесса отбора данных для достижения этого результата. Более того, несмотря на обучение на значительно меньшем количестве токенов по сравнению с существующими моделями, phi-1 всё же демонстрирует эмерджентные свойства.

Эмерджентность — это явление, когда в системе возникают новые свойства или поведение, которые не характерны для отдельных её компонентов. Т.е. это проявления феномена, когда целое больше, чем сумма его частей - прим. переводчика.

В Разделе 3 мы обсудим эти эмерджентные свойства и, в частности, подтвердим гипотезу о том, что количество параметров играет ключевую роль в эмерджентности (см., например, [WTB22]), сравнивая вывод модели phi-1 с выводом phi-1-small, модели, обученной по тому же конвейеру, но только с 350 миллионами параметров. Методология, используемая в этом разделе, напоминает работу Sparks of AGI, в которой утверждается, что необходимо отойти от статических бенчмарков для тестирования производительности LLM.

Наконец, в Разделе 4 мы обсудим альтернативные бенчмарки для оценки модели, а в Разделе 5 изучим возможное загрязнение наших обучающих данных в отношении HumanEval. Мы выкладываем модель для использования и оценки широким сообществом, но опускаем некоторые детали синтетической генерации данных по причинам наличия прав собственности.

Другие работы по теме

Наша работа является частью недавней программы использования LLM для синтеза программного кода, дополнительные ссылки на которую см. в [CTJ+21, NPH+22]. Наш подход также является частью тенденции использования существующих LLM для синтеза данных для обучения новых поколений LLM, [WKM+22, TGZ+23, MMJ+23, LGK+23, JWJ+23]. В настоящее время ведутся споры о том, что возможно такое “рекурсивное обучение” приведет к деградации моделей [SZ+23, GUESS+23], противоположную точку зрения смотрите в [MMJ+23].
Обратите внимание, что в этой статье мы фокусируемся на узкой задаче, аналогично [JWJ +23], при которой достижение лучшей производительности, чем у исходной LLM, кажется реалистичным (что и доказывается статье).

2. Детали обучения и важность высококачественных данных

Рисунок 2.1: Точность Pass@1 (%) на HumanEval. Группировка столбчатых диаграмм соответствует: 1)обычным параметрам масштабирования, 2)увеличению времени вычислений (больше проходов по данным, здесь от 26 млрд токенов до 76 млрд), 3)увеличению количества параметров модели (здесь от 350 млн до 1.3 млрд). Каждый столбец внутри группы соответствует различным обучающим наборам данных: (A) Первый (оранжевый) столбец представляет производительность моделей, обученных на стандартном наборе данных с Python-файлами из The Stack (плюс StackOverflow для модели с 1.3 млрд параметров); (B) Второй (светло-синий) столбец представляет производительность моделей, обученных на нашем новом наборе данных CodeTextbook; (C) Наконец, третий (темно-синий) столбец соответствует моделям второй колонки, дообученным на нашем новом наборе данных CodeExercises.
Рисунок 2.1: Точность Pass@1 (%) на HumanEval. Группировка столбчатых диаграмм соответствует: 1)обычным параметрам масштабирования, 2)увеличению времени вычислений (больше проходов по данным, здесь от 26 млрд токенов до 76 млрд), 3)увеличению количества параметров модели (здесь от 350 млн до 1.3 млрд). Каждый столбец внутри группы соответствует различным обучающим наборам данных: (A) Первый (оранжевый) столбец представляет производительность моделей, обученных на стандартном наборе данных с Python-файлами из The Stack (плюс StackOverflow для модели с 1.3 млрд параметров); (B) Второй (светло-синий) столбец представляет производительность моделей, обученных на нашем новом наборе данных CodeTextbook; (C) Наконец, третий (темно-синий) столбец соответствует моделям второй колонки, дообученным на нашем новом наборе данных CodeExercises.

Для моделей с 1.3 млрд параметров phi-1 и phi-1-base являются контрольными точками после обучения на 51 млрд токенов (770 GPU-часов), а модель The Stack+ была обучена на 76 млрд токенов и 1090 GPU-часов. Мы подчеркиваем, что даже без какого-либо дообучения наша модель phi-1-base, обученная на наборе данных CodeTextbook, достигает 29% производительности на HumanEval при всего 1.3 млрд параметров. Предыдущая самая маленькая модель, достигшая около 30% производительности на HumanEval, это Replit-Finetuned с 2.7 млрд параметров, которая была обучена на объеме токенов в 100 раз больше, чем наша [Rep23]. Кроме того, дообучение на нашем наборе данных CodeExercises для получения phi-1 не только дало нам максимальную производительность 51% на HumanEval, но и раскрыло дополнительные неожиданные возможности кодирования (см. Раздел 3).

Как намекает название статьи, ключевым ингредиентом нашей модели являются данные обучения "качества как в учебнике". В отличие от предыдущих работ, которые использовали стандартные источники текстовых данных для генерации кода, такие как The Stack (содержащий исходный код из репозиториев с разрешающими лицензиями) и другие веб-наборы данных (например, StackOverflow и CodeContest [*]), мы утверждаем, что эти источники не оптимальны для обучения модели алгоритмическому мышлению и планированию. С другой стороны, архитектура нашей модели и методы обучения довольно традиционны (Раздел 2.3), поэтому этот раздел мы посвящаем в основном объяснению того, как мы отбирали наши данные.

Стандартные наборы данных для кода [*, *] представляют собой большой и разнообразный корпус, охватывающий широкий спектр тем и случаев использования. Однако, основываясь на ручном анализе случайных выборок, мы можем сказать, что многие из этих фрагментов кода далеко не очень полезны для изучения основ программирования и страдают от нескольких недостатков:

  • Многие примеры не самодостаточны, то есть они зависят от других модулей или файлов, внешних по отношению к фрагменту, что делает их трудными для понимания без дополнительного контекста.

  • Типичные примеры не содержат значимых вычислений, а скорее состоят из тривиального или шаблонного кода, такого как определение констант, установка параметров или настройка элементов графического интерфейса.

  • Примеры, содержащие алгоритмическую логику, часто скрыты внутри сложных или плохо документированных функций, что затрудняет их понимание или изучение.

  • Примеры смещены в сторону определённых тем или случаев использования, что приводит к несбалансированному распределению концепций и навыков программирования в наборе данных.

Можно представить, насколько неэффективной и полной разочарований стала бы попытка человека освоить навыки программирования по таким наборам данных, так как ему пришлось бы иметь дело с большим количеством шума, неоднозначности и неполноты данных.

Мы предполагаем, что эти проблемы также влияют на производительность языковых моделей, поскольку они снижают качество и количество сигналов, которые связывают естественный язык с кодом. Мы предполагаем, что языковые модели выиграют от обучающего набора данных, который обладает теми же качествами, что и хороший "учебник": он должен быть ясным, самодостаточным, поучительным и сбалансированным.

В этой работе мы напрямую решаем эту проблему и показываем, что целенаправленно отбирая и генерируя высококачественные данные можно достичь современных результатов в задачах генерации кода с гораздо меньшим размером модели и меньшими вычислительными затратами, чем существующие подходы. Наше обучение основывается на трёх основных наборах данных:

  • Отфильтрованный набор данных "код-язык", который представляет собой подмножество The Stack и StackOverflow, полученное с использованием классификатора на основе языковой модели (состоящий из около 6 миллиардов токенов).

  • Синтетический датасет "учебников", состоящий из менее чем 1 миллиарда токенов учебников по Python, сгенерированных GPT-3.5.

  • Небольшой датасет "упражнения", состоящий из ~180 миллионов токенов упражнений и решений на Python.

Мы подробно описываем эти наборы данных в следующих подразделах. В совокупности вышеуказанные наборы данных содержат менее 7 миллиардов токенов. Мы называем "CodeTextbook" комбинацию отфильтрованного набора данных "код-язык" и синтетического набора данных "учебников" и используем его на этапе предварительного обучения для получения нашей базовой модели phi-1-base — эта модель уже достигает конкурентоспособной производительности на HumanEval в 29%.

Затем мы используем набор данных "упражнения" из 180 миллионов токенов, называемый "CodeExercises", для файнтюнинга нашей модели phi-1-base для создания phi-1. Несмотря на небольшой размер набора данных "CodeExercises", файнтюнинг с этим набором данных имеет решающее значение не только для значительного улучшения генерации простых функций на Python, как показано на Рисунке 2.1, но и для разблокировки многих интересных эмерджентных возможностей в нашей модели phi-1, которые не наблюдаются в phi-1-base (см. Раздел 3).

2.1 Фильтрация существующих наборов данных с использованием классификатора на основе трансформера

Мы начинаем с общедоступных наборов данных на Python: используем подмножество Python из дедуплицированной версии The Stack и StackOverflow, которые вместе содержат более 35 миллионов файлов/примеров, всего более 35 миллиардов токенов.

Мы аннотируем качество небольшого подмножества этих файлов (около 100 тысяч примеров) с помощью GPT-4: для каждого фрагмента кода у GPT-4 запрашивается: "определить образовательную ценность для студента, цель которого — изучить основные концепции программирования".

Затем мы используем этот аннотированный набор данных для обучения классификатора на основе случайного леса, который предсказывает качество файла/примера, используя выдачу из предварительно обученной codegen-модели в виде соответствующих признаков. Мы отмечаем, что, в отличие от GPT-3.5, который мы активно используем для генерации синтетического контента (обсуждается ниже), мы используем GPT-4 минимально только для аннотирования качества небольшого подмножества примеров из The Stack и StackOverflow. Таким образом, мы рассматриваем использование GPT-4 как способ избежать утомительных усилий по ручному аннотированию [*].

Образовательная ценность, определенная фильтром / Высокая ценность (слева) / Низкая ценность (справа)
Образовательная ценность, определенная фильтром / Высокая ценность (слева) / Низкая ценность (справа)
Код выше в формате python:
# Высокая ценность

import torch
import torch.nn.functional as F

def normalize(x, axis=-1):
    """Performs L2-Norm."""
    num = x
    denom = torch.norm(x, 2, axis, keepdim=True)
    .expand_as(x) + 1e-12
    return num / denom

def euclidean_dist(x, y):
    """Computes Euclidean distance."""
    m, n = x.size(0), y.size(0)
    xx = torch.pow(x, 2).sum(1, keepdim=True).
    expand(m, n)
    yy = torch.pow(x, 2).sum(1, keepdim=True).
    expand(m, m).t()
    dist = xx + yy - 2 * torch.matmul(x, y.t())
    dist = dist.clamp(min=1e-12).sqrt()
    return dist

def cosine_dist(x, y):
    """Computes Cosine Distance."""
    x = F.normalize(x, dim=1)
    y = F.normalize(y, dim=1)
    dist = 2 - 2 * torch.mm(x, y.t())
    return dist
# Низкая ценность

import re
import typing
...

class Default(object):
    def __init__(self, vim: Nvim) -> None:
        self._vim = vim
        self._denite: typing.Optional[SyncParent] = None
        self._selected_candidates: typing.List[int] = []
        self._candidates: Candidates = []
        self._cursor = 0
        self._entire_len = 0
        self._result: typing.List[typing.Any] = []
        self._context: UserContext = {}
        self._bufnr = -1
        self._winid = -1
        self._winrestcmd = ''
        self._initialized = False
        self._winheight = 0
        self._winwidth = 0
        self._winminheight = -1
        self._is_multi = False
        self._is_async = False
        self._matched_pattern = ''
        ...

Наш метод фильтрации значительно повышает производительность модели даже без использования синтетических наборов данных, описанных ниже: для моделей с 350 миллионами параметров, обученных на неотфильтрованном Stack (дедуплицированный Python) и StackOverflow, производительность на HumanEval достигает предела на уровне 12,19%, даже после 96 тысяч шагов обучения (~200 млрд токенов). При этом обучение на отфильтрованном подмножестве данных даёт результат 17,68% на HumanEval уже после 36 тысяч шагов. Мы дополнительно улучшили этот показатель до 20,12% (см. рисунок 2.1), обучая модель на комбинации отфильтрованного набора данных и синтетического набора данных из учебников, описанного ниже.

2.2 Создание синтетических наборов данных с "качеством учебника"

Одной из основных проблем при создании высококачественного набора данных для генерации кода является обеспечение того, чтобы примеры были разнообразными и не повторялись.

Под разнообразием мы понимаем, что примеры должны охватывать широкий спектр концепций, навыков и сценариев программирования, а также варьироваться по уровню сложности, стилю и структуре. Разнообразие важно по нескольким причинам: оно знакомит языковую модель с различными способами выражения и решения задач в коде, снижает риск переобучения или запоминания конкретных шаблонов или решений, а также повышает возможности обобщения и устойчивость модели к новым или незнакомым задачам. Однако достижение разнообразия — нетривиальная задача, особенно при использовании синтетических данных, сгенерированных другой языковой моделью. Простое указание модели создать учебник по программированию или набор упражнений, даже с некоторыми вариациями в инструкциях или параметрах, скорее всего, приведёт к созданию однородного и избыточного набора данных, где одни и те же концепции и решения повторяются с незначительными изменениями. Это происходит потому, что языковые модели склонны следовать наиболее вероятным или распространённым путям, исходя из их обучающих данных и априорных знаний, и у них нет стимула или творческого подхода для исследования альтернативных или новых способов генерации кода. Поэтому необходимо найти правильный "трюк", который заставит языковую модель быть более креативной и разнообразной в своих выводах, сохраняя при этом качество и связность примеров.

Вдохновлённые работой [*], в которой путём включения случайного подмножества слов из фиксированного словаря в запрос и требования их комбинирования в сгенерированном тексте был создан разнообразный набор коротких историй, мы ищем способы внедрения случайности в запрос таким образом, чтобы это способствовало созданию набора данных с высоким разнообразием.

Синтетический набор данных "учебников"

Этот набор данных состоит из менее чем 1 миллиарда токенов учебников по Python, сгенерированных GPT-3.5, и предназначен для предоставления высококачественного источника текста с естественным языком, перемежающегося соответствующими фрагментами кода. Мы также ориентировали содержание этих учебников на темы, которые способствуют развитию логического мышления и базовых алгоритмических навыков. Разнообразие достигается за счёт наложения ограничений на темы и целевую аудиторию генерируемого учебника. Вот пример текста из синтетического учебника:

Для начала давайте определим, что такое сингулярные и несингулярные матрицы. Матрица называется сингулярной, если её определитель равен нулю. С другой стороны, матрица называется несингулярной, если её определитель не равен нулю. Теперь давайте рассмотрим эти концепции на примерах.

Пример 1: Рассмотрим матрицу A = np.array([[1, 2], [2, 4]]). Мы можем проверить, является ли эта матрица сингулярной или несингулярной, используя функцию определителя. Мы можем определить функцию на Python, is_singular(A), которая возвращает True, если определитель A равен нулю, и False в противном случае.

import numpy as np
def is_singular(A):
    det = np.linalg.det(A)
    if det == 0:
        return True
    else:
        return False

A = np.array([[1, 2], [2, 4]])
print(is_singular(A)) # True

Набор данных "CodeExercises"

Это небольшой синтетический набор данных "упражнения", состоящий из менее чем 180 миллионов токенов упражнений и решений на Python. Каждое упражнение представляет собой строку документации функции, которую необходимо завершить. Цель этого набора данных — настроить модель на выполнение задач по завершению функций на основе инструкций на естественном языке. Этот набор данных также был сгенерирован GPT-3.5, где основным способом достижения разнообразия является ограничение на имена функций. Для этого набора данных мы проводим явную деконтаминацию и альтернативные оценки в следующих разделах, чтобы убедиться, что задачи, похожие на те, что представлены в бенчмарке HumanEval, не встречаются во время файнтюнинга. Следующий фрагмент иллюстрирует синтетически сгенерированное упражнение:

def valid_guessing_letters(word: str, guesses: List[str]) -> List[str]:
    """
    Returns a list of valid guessing letters, which are letters that have not been guessed yet and
    are present in the word.
    Parameters:
    word (str): The word to guess.
    guesses (List[str]): A list of letters that have already been guessed.
    Returns:
    List[str]: A list of valid guessing letters.
    """
    valid_letters = []
    for letter in word:
        if letter not in guesses and letter not in valid_letters:
            valid_letters.append(letter)
    return valid_letters

2.3. Архитектура модели и обучение

Мы использовали трансформер с декодером [*], использующий реализацию FlashAttention для многоголового внимания (MHA) [*]. Также мы применили MHA и слои MLP в параллельной конфигурации, следуя недавним моделям, таким как CodeGen [*], PaLM [*] и GPT-NeoX [*].

Архитектура нашей модели phi-1 с 1.3 миллиардами параметров состоит из 24 слоев, скрытой размерности 2048, внутренней размерности MLP 8192 и 32 голов внимания размерностью 64 каждая.

Меньшая модель phi-1-small с 350 миллионами параметров имеет 20 слоев, скрытую размерность 1024, внутреннюю размерность MLP 4096 и 16 голов внимания размерностью 64 каждая.

Мы также использовали вращающийся позиционный эмбеддинг (rotary position embedding) [*] с размерностью вращения 32. Эти архитектурные решения были заимствованы из [*]. Мы использовали тот же токенизатор, что и в codegen-350M-mono [*]. Помимо FlashAttention, наши модели не используют другие техники, такие как Fill-In-the-Middle (FIM) [*] или Multi-Query-Attention (MQA) [*], которые могли бы дополнительно повысить производительность и эффективность [*].

Для предобучения и дообучения (файнтюнинга) мы объединили соответствующие наборы данных в одномерный массив, используя токен «⟨∣endoftext∣⟩» для разделения файлов. Мы обучали наши модели на последовательностях длиной 2048, взятых из нашего массива данных с функцией потерь прогнозирования следующего токена. Обучение проводилось с использованием fp16, оптимизатора AdamW, линейного графика обучения с разогревом и затуханием, а также внимания и остаточного dropout на уровне 0,1. Обучение выполнялось на 8 GPU Nvidia A100 с использованием deepspeed. Наша базовая предобученная модель phi-1-base была получена менее чем за 4 дня обучения. Дообучение (файнтюнинг) для получения phi-1 заняло дополнительные 7 часов на том же оборудовании.

Предобучение (Pretraining)

phi-1-base был обучен на датасете CodeTextbook (отфильтрованный корпус кода и синтетические учебники). Мы использовали эффективный размер батча 1024 (включая параллелизм данных и накопление градиентов), максимальную скорость обучения 1e-3 с разогревом в течение 750 шагов и коэффициентом регуляризации весов 0,1 всего за 36,000 шагов. Мы использовали контрольную точку на 24,000 шаге в качестве нашей phi-1-base — это эквивалентно примерно 8 эпохам на нашем наборе данных CodeTextbook, что составляет чуть более 50 миллиардов тренировочных токенов. Несмотря на малый размер и вычислительные ресурсы, эта модель уже достигает точности 29% на HumanEval.

Файнтюнинг

phi-1 получена путем файнтюнинга phi-1-base на наборе данных CodeExercises. Для файнтюнинга мы использовали ту же настройку, что и для предобучения, но с другими гиперпараметрами: эффективный размер батча 256, максимальная скорость обучения 1e-4 с разогревом в течение 50 шагов и коэффициентом регуляризации весов 0.01. Мы обучали модель в течение общего количества шагов 6000 и выбирали лучшую контрольную точку (сохраняемую каждые 1000 шагов).

3. Скачок возможностей модели после файнтюнинга на наборе CodeExercises

На Рисунке 2.1 показано, что наибольшее улучшение в результатах на HumanEval было достигнуто благодаря файнтюнингу на небольшом наборе данных CodeExercises (<200 млн токенов). CodeExercises состоит исключительно из коротких задач на Python с использованием только базовых библиотек. В этом разделе мы продемонстрируем интересный факт: модель после дообучения показывает значительное улучшение также в выполнении задач, которые не представлены в наборе данных для дообучения. В том числе управление сложными алгоритмическими задачами и использование внешних библиотек.

Это позволяет предположить, что процесс файнтюнинга мог помочь модели реорганизовать и закрепить знания, полученные во время предварительного обучения, даже если такие знания явно не присутствуют в нашем наборе данных CodeExercises. В этом разделе мы сосредоточимся на качественном сравнении возможностей нашей дообученной модели phi-1 и её базовой модели phi-1-base с 1,3 млрд параметров.

3.1 Файнтюнинг улучшает понимание модели

Используя простую функцию Python, которую мы создали самостоятельно, мы наблюдаем, что модель демонстрирует значительно более высокий уровень понимания и соответствия инструкциям после файнтюнинга. В частности, мы видим, что phi-1-base испытывает трудности с логическими связями в запросах, в то время как phi-1 может интерпретировать вопрос и сгенерировать правильный ответ. В этом примере даже наша 350-миллионная модель phi-1-small показывает некоторый уровень понимания проблемы, хотя итоговое решение оказывается неверным. Мы наблюдаем такие тенденции последовательно в процессе наших взаимодействий с моделью, см. другой пример в Приложении A.

Промпт:    Алиса, Боб и Чарльз играют в игру. Сначала Алиса выбирает случайное число от 333 до 678, затем Боб выбирает число от числа Алисы до 888, а затем Чарльз выбирает число от 123 до числа Боба. Алиса получает одно очко, если разность числа Алисы и числа Чарльза делится на целую часть квадратного корня из числа Боба. В противном случае Боб получает одно очко. Смоделируйте очки Алисы и Боба за 30 итераций.
Промпт: Алиса, Боб и Чарльз играют в игру. Сначала Алиса выбирает случайное число от 333 до 678, затем Боб выбирает число от числа Алисы до 888, а затем Чарльз выбирает число от 123 до числа Боба. Алиса получает одно очко, если разность числа Алисы и числа Чарльза делится на целую часть квадратного корня из числа Боба. В противном случае Боб получает одно очко. Смоделируйте очки Алисы и Боба за 30 итераций.
Код предыдущего примера:
# phi-1

points = [0, 0]
for i in range(30):
    alice_num = random.randint(333, 678)
    bob_num = random.randint(alice_num, 888)
    charles_num = random.randint(123, bob_num)
    if (alice_num - charles_num) % int(bob_num ** 0.5) == 0:
        points[0] += 1
    else:
        points[1] += 1
    return f"Alice: {points[0]}, Bob: {points[1]}"
# phi-1-base

self.points = 0
self.alice = 333
self.bob = 678
self.charles = 888
self.bob_points = 0
self.alice_points = 0
self.iterations = 30
self.alice_turn = True
self.bob_turn = True
self.alice_turn_points = 0
self.bob_turn_points = 0
self.alice_turn_points_history = []
self.bob_turn_points_history = []
self.alice_turn_points_history.append(self.alice_points)
self.bob_turn_points_history.append(self.bob_points)
# phi-1-small

indices = []
import random
alice_points = 0
bob_points = 0
for i in range(30):
    alice_num = random.randint(333, 678)
    bob_num = random.randint(123, bob_num)
    if (alice_num - bob_num) % math.floor(math.sqrt(bob_num)) == 0:
        alice_points += 1
    if (i % 2) == 0:
        bob_points += 1
print(f"Alice gets {alice_points} points, while Bob gets {bob_points} points.")

3.2. Файнтюнинг улучшает способность модели использовать внешние библиотеки

Мы продемонстрируем, что дообучение на наборе CodeExercises неожиданно улучшает способность модели использовать внешние библиотеки, такие как Pygame и Tkinter, несмотря на то, что наши упражнения не содержат этих библиотек. Это позволяет предположить, что наше дообучение не только улучшает выполнение целевых задач, но также облегчает извлечение знаний для несвязанных задач, полученных в ходе предварительного обучения. Для справки, на Рисунке 3.1 показано распределение импортов пакетов в нашем наборе данных CodeExercises.

Рисунок 3.1: Количество импортов среди ∼880 тыс. упражнений в CodeExercises (мы игнорируем библиотеки, импортированные менее 10 раз). Код для построения графика был сгенерирован моделью phi-1 по запросу:«У меня есть словарь, сначала отсортируйте его по значениям от наибольшего к наименьшему. Затем постройте столбчатую диаграмму с помощью pyplot. Сначала установите размер шрифта 7, затем поверните метки на оси X на 90 градусов. Ось X — это ключи, ось Y — значения словаря. Используйте логарифмическую шкалу для оси Y. Кроме того, установите метку оси Y как «Log Number of Times», а метку оси X как «Imports». Установите dpi на 1000.»
Рисунок 3.1: Количество импортов среди ∼880 тыс. упражнений в CodeExercises (мы игнорируем библиотеки, импортированные менее 10 раз). Код для построения графика был сгенерирован моделью phi-1 по запросу:
«У меня есть словарь, сначала отсортируйте его по значениям от наибольшего к наименьшему. Затем постройте столбчатую диаграмму с помощью pyplot. Сначала установите размер шрифта 7, затем поверните метки на оси X на 90 градусов. Ось X — это ключи, ось Y — значения словаря. Используйте логарифмическую шкалу для оси Y. Кроме того, установите метку оси Y как «Log Number of Times», а метку оси X как «Imports». Установите dpi на 1000.»

Пример с PyGame

Мы начинаем с запроса к модели, в котором просим сгенерировать код для перемещения мяча в PyGame:

Промпт:   Напишите код на PyGame. Есть мяч. На каждой итерации:  1. Координата X мяча увеличивается на случайное целое число в диапазоне (-10, 10).  2. Координата Y мяча увеличивается на случайное целое число в диапазоне (-20, 20).  Координата X мяча должна оставаться в пределах от 0 до 400, а координата Y — в пределах от 0 до 600.  Когда пользователь нажимает пробел, установите координату X мяча на 200, а координату Y — на 400.
Промпт: Напишите код на PyGame. Есть мяч. На каждой итерации: 1. Координата X мяча увеличивается на случайное целое число в диапазоне (-10, 10). 2. Координата Y мяча увеличивается на случайное целое число в диапазоне (-20, 20). Координата X мяча должна оставаться в пределах от 0 до 400, а координата Y — в пределах от 0 до 600. Когда пользователь нажимает пробел, установите координату X мяча на 200, а координату Y — на 400.
Код примеров:
# phi-1
...
screen = pygame.display.set_mode((400, 600))
x, y = 0, 0
while True:
    for event in pygame.event.get():
    ...
        elif event.type == pygame.KEYDOWN:
            if event.key == pygame.K_SPACE:
                x = 200
                y = 400
    x += random.randint(-10, 10)
    y += random.randint(-20, 20)
    if x < 0:
        x = 0
    elif x > 400:
        x = 400
    if y < 0:
        y = 0
    elif y > 600:
        y = 600
    screen.fill((255, 255, 255))
    pygame.draw.circle(screen, (255, 0, 0), (x, y), 10)
    pygame.display.update()
# phi-1-base
...
ball_x = random.randint(-10, 10)
ball_y = random.randint(-20, 20)
ball_vx = random.randint(-5, 5)
ball_vy = random.randint(-5, 5)
...
screen = pygame.display.set_mode((800, 600))
...
while not done:
    for event in pygame.event.get():
    ...
    elif event.type == pygame.KEYDOWN:
        if event.key == pygame.K_SPACE:
            ball_x = 200
            ball_y = 400
    ball_x += ball_vx
    ball_y += ball_vy
    ball_vx += ball
    pygame.display.flip()
# phi-1-small
...
x, y = 0, 0
dx, dy = random.randint(-10, 10), random.randint(-20, 20)
while True:
    screen.fill((0, 0, 0))
    pygame.draw.rect(screen, (255, 255, 255), (x, y, 400, 600))
    pygame.display.update()
    x += dx
    y += dy
    if x + dx > 400:
        dx = -dx
    elif x + dx < 0:
        dx = dx
    if y + dy > 600:
        dy = -dy
    elif y + dy < 0:
        dy = dy
    pygame.time.wait(50)

Приведенный выше фрагмент кода показывает основной цикл простой программы на PyGame, в которой мяч отскакивает на экране. Модель phi-1 корректно применяет функции PyGame для обновления и отрисовки мяча, следуя инструкциям из запроса. Модели phi-1-base и phi-1-small генерируют вызовы функций, которые синтаксически правильны, но семантически не соответствуют задаче. Мы видим, что phi-1-base демонстрирует некоторую способность использовать соответствующие вызовы API, но не может следовать логике задачи, в то время как phi-1-small после дообучения понимает логику, но не обладает достаточной емкостью для изучения правильных вызовов функций.

Пример с TKinter

Наш второй пример — это приложение на TKinter, в котором мы просим модель обновлять текстовое поле в зависимости от нажатия кнопок.

Промпт:    Напиши приложение на tkinter. В верхней части находится текстовое поле, внизу — кнопка "Submit", а рядом с ней — кнопка "Recover". При нажатии на кнопку "Submit" получи текст из текстового поля и сохрани его в глобальную строку s. Если текст содержит слово "pew", обновите текст в текстовом поле на "pewpewpew?". В противном случае добавьте в конец текста "no pew...". При нажатии на кнопку "Recover" замените текст в текстовом поле на значение строки s.
Промпт: Напиши приложение на tkinter. В верхней части находится текстовое поле, внизу — кнопка "Submit", а рядом с ней — кнопка "Recover". При нажатии на кнопку "Submit" получи текст из текстового поля и сохрани его в глобальную строку s. Если текст содержит слово "pew", обновите текст в текстовом поле на "pewpewpew?". В противном случае добавьте в конец текста "no pew...". При нажатии на кнопку "Recover" замените текст в текстовом поле на значение строки s.
Код выше:
# phi-1

...
global s
s = ""
def submit():
    global s
    text = textfield.get()
    if "pew" in text:
        textfield.delete(0, tk.END)
        textfield.insert(0, "pewpewpew...")
    else:
        textfield.delete(0, tk.END)
        textfield.insert(0, text + "no pew...")
    s = text

def recover():
    global s
    textfield.delete(0, tk.END)
    textfield.insert(0, s)

textfield = tk.Entry(root)
textfield.pack()
submit_button = tk.Button(root, text="Submit", command=submit)
submit_button.pack()
recover_button = tk.Button(root, text="Recover", command=recover)
recover_button.pack()
root.mainloop()
# phi-1-base

global s
s = s.replace("pew", "pewpewpew")
if "pew" in s:
    s = s.replace("pew", "pewpewpew")
else:
    s = s + "no pew..."
text.delete(0.0, END)
text.insert(0.0, s)
def main():
    global s
    s = "pewpewpew"
    text.delete(0.0, END)
    text.insert(0.0, s)
    text.tag_add("button", "<Button-1>", "<Button-3>")
    text.tag_add("button", "<Button-2>", "<Button-4>")
    # The model keeps adding buttons from here
# phi-1-small

import tkinter as tk
root = tk.Tk()
global s
text = ""
while True:
    event, values = tk.popup()
    if event == "submit":
        text += values.split()[0] + " "
    elif event == "textfield":
        s += values.split()[0] + " "
    elif event == "submit-button-text":
        text = values.split()[0] + " "
    elif event == "text-button-text":
        s = text
    if "pew" in text:
        text = text.replace("pew", "pewpewpew")
    if "no pew" in text:
        break
tk.destroy()

Результаты выполнения трех моделей демонстрируют значительный разрыв в их понимании запросов. И phi-1-base, и phi-1-small не смогли использовать правильные API Tkinter и создали бессмысленные вызовы функций. С другой стороны, phi-1 корректно реализовала графический интерфейс и все функции (за исключением небольшой ошибки в копировании строки "pewpewpew?"). В Приложении A мы приводим два дополнительных примера для pytorch и pyplot.

Пример в режиме чата.

Наконец, мы покажем, что phi-1 обладает лучшими способностями к ведению диалога по сравнению с phi-1-base, несмотря на то, что данные для чата присутствовали только на этапе предварительного обучения, а не файнтюнинга.

Промпт:   Студент: У меня есть график на Python pyplot, я хочу увеличить его разрешение и повернуть. Что мне делать?  Ассистент:
Промпт: Студент: У меня есть график на Python pyplot, я хочу увеличить его разрешение и повернуть. Что мне делать? Ассистент:

Ответ phi-1:

  1. Установите параметр dpi на желаемое разрешение (например, 300 для 300 DPI).

  2. Используйте функцию rotate из модуля matplotlib.pyplot, чтобы повернуть график на 90 градусов по часовой стрелке.

Пример:

import matplotlib.pyplot as plt  

fig, ax = plt.subplots()  
ax.plot([1, 2, 3], [4, 5, 6])  
# Установите разрешение на 300 DPI  
ax.set_dpi(300)  
# Поверните график на 90 градусов по часовой стрелке  
ax.rotate(90)  

Ответ phi-1-base:
Вы можете использовать функцию setp() для установки разрешения и функцию set_rotation() для поворота графика. <|endoftext|>

Ответ phi-1-small:
Я — график на Python pyplot, и я хочу увеличить его разрешение и повернуть. Что мне делать?
ВТОРОЙ:
Я — график на Python pyplot, и я хочу увеличить его разрешение и повернуть. Что мне делать?
ТРЕТИЙ:...

4. Оценка на нестандартных задачах с использованием LLM-оценки (LLM grading)

Одной из потенциальных проблем, связанных с удивительно высокой производительностью модели phi-1 на HumanEval (см. Таблицу 1 и Рисунок 2.1), может быть запоминание данных из-за возможного загрязнения синтетического набора данных CodeExercises. Мы изучим это потенциальное загрязнение напрямую в Разделе 5, а в этом разделе рассматрим эту проблему с помощью новой оценки, которая была разработана достаточно нестандартно, чтобы её появление в нашем обучающем наборе данных было маловероятно.

Чтобы минимизировать предвзятость и утечку данных, новые задачи для оценки были созданы специальной командой, которая не имела доступа к набору данных CodeExercises или финальной модели. Они создали 50 новых задач в том же формате, что и HumanEval, с инструкциями разработать задачи, которые вряд ли появятся в реальных базах кода или в качестве учебных упражнений. Вот пример такой задачи:

"""      Эта функция принимает два списка целых чисел, сортирует каждый из них по возрастанию,      объединяет их, возводит в квадрат элементы на четных индексах, фильтрует элементы, меньшие чем my_threshold, и затем удаляет дубликаты. Результирующий список возвращается.      """
""" Эта функция принимает два списка целых чисел, сортирует каждый из них по возрастанию, объединяет их, возводит в квадрат элементы на четных индексах, фильтрует элементы, меньшие чем my_threshold, и затем удаляет дубликаты. Результирующий список возвращается. """

Одной из сложностей оценки языковых моделей на задачах по программированию является то, что вывод модели часто бинарный: либо код проходит все unit-тесты, либо не проходит. Однако это не отражает нюансов производительности модели, так как она может сгенерировать код, который почти правильный, но содержит небольшую ошибку, или код, который полностью неверен, но случайно проходит некоторые тесты. Более информативным способом оценки навыков модели может быть сравнение её вывода с правильным решением и выставление оценки на основе того, насколько хорошо оно соответствует ожидаемой логике. Это похоже на то, как люди оцениваются на собеседованиях по программированию, где интервьюер не только запускает код, но и анализирует рассуждения и качество решения.

Для оценки решений мы используем подход, при котором GPT-4 выступает в роли оценщика (как, например, в [*]). Этот подход имеет два преимущества:

(1) используя GPT-4 в качестве оценщика, мы можем использовать его знания и генеративные способности для получения более детального и осмысленного сигнала о способностях модели к программированию, и

(2) это устраняет необходимость в тестах.

Наш запрос инструктирует LLM оценить решение студента сначала в краткой вербальной оценке, а затем выставить оценку от 0 до 10.

Таблица 2: Оценки понимания (Understanding scores) на 50 новых нестандартных задачах по программированию, выставленные с использованием LLM.

Модель

Размер

Токенов обучения

Оценка

Human-Eval

CodeGen-Mono-350M [NPH+23]

CodeGen-Mono-16.1B [NPH+23]

Replit [Rep23]

StarCoder [LAZ+23]350M

350M

16.1B

2.7B

15.5B

577B

577B

525B

1T

19%

38%

37%

51%

13%

29%

22%

34%

phi-1-base

phi-1-small

phi-1

1.3B

350M

1.3B

7B

7B

7B

37%

45%

52%

29%

45%

51%

См. Таблицу 2 для наших результатов с phi-1 и конкурирующими моделями. Оценки на наших новых нестандартных задачах дают тот же рейтинг, что и HumanEval (см. Таблицу 1). phi-1 снова демонстрирует результат значительно выше, чем StarCoder, как и на HumanEval. Учитывая, что новые задачи не могли загрязнить обучающие данные и, более того, были разработаны так, чтобы находиться за пределами распределения обучающих данных, эти результаты значительно повышают нашу уверенность в достоверности производительности phi-1.

5. Удаление (очистка) данных для объективной оценки производительности

На Рисунке 2.1 мы видим, что обучение на наборе данных CodeExercises приводит к значительному улучшению производительности модели на бенчмарке HumanEval. Чтобы исследовать это улучшение, мы предлагаем "очистить" набор данных CodeExercises, удалив файлы, которые "похожи" на те, что содержатся в HumanEval. Этот процесс можно рассматривать как "строгую форму" дезактивации данных. Затем мы повторно обучаем нашу модель на таких очищенных данных и всё ещё наблюдаем высокую производительность на HumanEval. В частности, даже после агрессивного удаления более 40% набора данных CodeExercises (это включает удаление файлов, которые лишь отдалённо похожи на HumanEval, см. Приложение C), повторно обученная модель phi-1 всё равно превосходит StarCoder.

Мы считаем, что такой эксперимент с удалением данных является справедливым способом оценки производительности и более информативным, чем стандартные исследования "загрязнения" в литературе, которые обычно основаны на измерении пересечения между обучающими и тестовыми данными (например, Раздел 4.8 в [*]). Для полноты картины мы начинаем этот раздел с проведения стандартного эксперимента на загрязнение, который показывает, что CodeExercises не загрязнён данными из HumanEval в этом стандартном смысле.

5.1 Перекрытие N-грамм

N-граммы измеряют схожесть текстовых сегментов на основе общих последовательностей из n слов. Мы вычисляем перекрытие n-грамм между строками документации (docstrings) каждого вопроса из HumanEval и каждого упражнения из набора данных CodeExercises, который был нами сгенерирован. Мы обнаружили 4 вопроса из HumanEval с перекрытием 13-грамм хотя бы с одним элементом в нашем наборе данных. После дальнейшего исследования выяснилось, что все 4 случая перекрытия в 13-граммах являются ложными срабатываниями, как в примере ниже. Наш анализ перекрытия n-грамм показывает, что наш набор данных имеет минимальное буквальное совпадение с HumanEval.

HumanEval:
Вам дан непустой список положительных целых чисел. Верните наибольшее целое число, которое больше нуля и имеет частоту, превышающую или равную значению самого числа. Частота числа — это количество раз, которое оно встречается в списке.

CodeExercises:
Вычисляет сумму анализа частот степеней для списка целых чисел. Сумма анализа частот степеней вычисляется путем суммирования квадратов частот каждого уникального числа в списке. Частота числа — это количество раз, которое оно встречается в списке.

5.2 Анализ схожести на основе эмбеддингов и синтаксиса

Как мы только что увидели, анализ n-грамм недостаточно точен для поиска схожих фрагментов кода между HumanEval и CodeExercises. Вместо этого мы используем комбинацию эмбеддингов и синтаксических расстояний. Для эмбеддингового расстояния мы вычисляем L2-расстояние между эмбеддингами фрагментов кода, где эмбеддинг получен из предобученной модели CodeGen-Mono 350M [NPH+23]. Мы наблюдаем, что эмбеддинговое расстояние успешно захватывает пары кода, где общая семантика кода схожа, что можно вывести из Python Docstring, имен функций/классов, а также структуры кода. Для синтаксического расстояния мы вычисляем строковое расстояние редактирования между абстрактными синтаксическими деревьями (AST) двух заданных фрагментов кода. Расстояние AST успешно идентифицирует перекрывающиеся секции между парами кода, оставаясь независимым от несинтаксического текста, такого как имена переменных/функций, комментарии и Python Docstrings. Для отбора в CodeExercises мы устанавливаем порог для эмбеддингового расстояния и тестируем несколько значений коэффициента совпадения τ для расстояния AST. См. Приложение C для примеров пар кода, захваченных с помощью эмбеддингового расстояния и различных значений τ для AST. Мы варьируем τ от 0,95 до 0,8, что соответствует удалению от 42,5K до 354K из 879,5K общих задач в CodeExercises.

Таблица 3: Процент схожих и не схожих задач из HumanEval, правильно решенных различными моделями. Схожесть определяется наличием или отсутствием близких соответствий для соответствующей задачи HumanEval в наборе данных CodeExercises (для заданного τ). Количество задач обозначает число задач HumanEval в каждом подмножестве. Здесь τ — порог для коэффициента совпадения на основе AST между кодами для проверки схожести.

τ

Кол-во задач

phi-1

phi-1 retrained on pruned data

StarCoder Prompted [*]

0.95

похожие
не похожие
всего

71
93
164

81.7%
26.9%
50.6%

74.6%
32.3%
50.6%

57.7%
29.0%
41.5%

0.9

похожие
не похожие
всего

93
71
164

63.4%
33.8%
50.6%

51.6%
36.6%
45.1%

48.4%
32.4%
41.5%

0.85

похожие
не похожие
всего

106
58
164

62.3%
29.3%
50.6%

52.8%
34.5%
46.3%

47.2%
31.0%
41.5%

0.8

похожие
не похожие
всего

116
48
164

59.5%
29.2%
50.6%

52.6%
27.1%
45.1%

45.7%
31.2%
41.5%

Таблица 3 суммирует производительность нашей переобученной модели phi-1 на сокращенных наборах данных (с τ = 0,95, 0,9, 0,85 и 0,8) по сравнению с оригинальной phi-1, обученной на полном наборе CodeExercises, и моделью StarCoder-prompted с 15.5 миллиардами параметров. Мы разделяем задачи HumanEval на два подмножества («схожие» и «не схожие») в зависимости от наличия хотя бы одного близкого соответствия (для данного τ) в исходном наборе данных CodeExercises. Затем мы сообщаем точность моделей для каждого подмножества HumanEval отдельно. Как видно, даже после значительного сокращения нашего набора данных, phi-1 по-прежнему значительно опережает StarCoder-Prompted с отрывом в 13 пунктов, что подтверждает, что наш прирост производительности не связан с «загрязнением» набора данных, даже если этот термин понимать в широком смысле. Также стоит отметить, что точность всех моделей ниже на подмножестве не схожих задач HumanEval по сравнению с подмножеством схожих задач.

6. Заключение

Качественный и хорошо составленный учебник может предоставить студенту необходимые знания для освоения нового предмета, и наша работа показывает значительное влияние высококачественных данных на повышение мастерства языковой модели в задачах генерации кода. Создавая данные «качества учебника», мы смогли обучить модель, которая превосходит почти все модели с открытым исходным кодом на тестах по программированию, таких как HumanEval и MBPP, несмотря на то, что наша модель в 10 раз меньше по размеру и в 100 раз меньше по объему данных. Мы предполагаем, что такие высококачественные данные значительно повышают эффективность обучения языковых моделей для программирования, поскольку они предоставляют четкие, самодостаточные, обучающие и сбалансированные примеры концепций и навыков программирования.

Однако наша модель имеет ряд ограничений по сравнению с более крупными моделями для программирования.

Во-первых, phi-1 специализируется на программировании на Python, что ограничивает ее универсальность по сравнению с моделями, поддерживающими несколько языков.

Во-вторых, phi-1 не обладает специализированными знаниями, которые есть у более крупных моделей, такими как работа с определенными API или использование менее распространенных пакетов.

Наконец, из-за структурированного характера наборов данных и недостатка разнообразия в терминах языка и стиля, phi-1 менее устойчива к стилистическим вариациям или ошибкам в запросе (например, ее производительность значительно снижается, если в запросе есть грамматические ошибки).

Мы подробнее рассматриваем эти ограничения и приводим примеры случаев, когда phi-1 дает сбой, в Приложении B.

Ни одно из этих ограничений не кажется фундаментальным, и при дальнейшей работе наш подход может быть использован для решения каждого из них, хотя пока неясно, какое масштабирование потребуется для их преодоления (как для размера модели, так и для объема данных). Мы также считаем, что значительного прогресса можно достичь, используя GPT-4 для генерации синтетических данных вместо GPT-3.5, поскольку мы заметили, что данные GPT-3.5 содержат много ошибок. Интересно, что phi-1 способна достичь такого высокого уровня мастерства в программировании, несмотря на эти ошибки (подобное явление наблюдалось в [*], где языковая модель может обучаться на данных с 100% ошибок и все равно генерировать правильные ответы во время тестирования).

В итоге, наша работа подтверждает, что разработка хорошей методологии создания высококачественных наборов данных является ключевым направлением исследований для продвижения в области обработки естественного языка и смежных областях (см. также [*]). Однако создание высококачественных наборов данных — это нетривиальная задача, которая ставит несколько вызовов, требующих решения.

Один из вызовов — обеспечить, чтобы набор данных охватывал все соответствующие содержания и концепции, которые модель должна изучить, и делал это сбалансированно и репрезентативно.

Другой вызов — обеспечить, чтобы набор данных был действительно разнообразным и не повторяющимся, чтобы модель не переобучалась на данных или не запоминала конкретные шаблоны или решения. Это требует поиска способов внедрения случайности и творчества в процесс генерации данных, сохраняя при этом качество и связность примеров.

Более того, даже после создания таких наборов данных у нас нет хорошей методологии для измерения и оценки уровня разнообразия и избыточности в данных. Например, если у нас есть набор данных с упражнениями по программированию, сложно определить, сколько различных вариаций каждого упражнения существует и как они распределены по набору данных. Наконец, поскольку сами языковые модели будут использоваться для генерации данных для будущих языковых моделей, это повышает актуальность этических и социальных последствий обучения таких моделей, таких как подотчетность, прозрачность и предвзятость данных и моделей, участвующих в этом процессе.

Ссылки

[ADF+ 23] Rohan Anil, Andrew M Dai, Orhan Firat, Melvin Johnson, Dmitry Lepikhin, Alexandre Passos, Siamak Shakeri, Emanuel Taropa, Paige Bailey, Zhifeng Chen, et al. Palm 2 technical report. arXiv preprint arXiv:2305.10403, 2023.

[ALK+ 23] Loubna Ben Allal, Raymond Li, Denis Kocetkov, Chenghao Mou, Christopher Akiki, Carlos Munoz Ferrandis, Niklas Muennighoff, Mayank Mishra, Alex Gu, Manan Dey, et al. Santacoder: don’t reach for the stars! arXiv preprint arXiv:2301.03988, 2023.

[AON+ 21] Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, et al. Program synthesis with large language models. arXiv preprint arXiv:2108.07732, 2021.

[AZL23] Zeyuan Allen-Zhu and Yuanzhi Li. Physics of language models: Part 1, context-free grammar. arXiv preprint arXiv:2305.13673, 2023.

[BBH+ 22] Sid Black, Stella Biderman, Eric Hallahan, Quentin Anthony, Leo Gao, Laurence Golding, Horace He, Connor Leahy, Kyle McDonell, Jason Phang, Michael Pieler, USVSN Sai Prashanth, Shivanshu Purohit, Laria Reynolds, Jonathan Tow, Ben Wang, and Samuel Weinbach. GPT-NeoX-20B: An open-source autoregressive language model. In Proceedings of the ACL Workshop on Challenges & Perspectives in Creating Large Language Models, 2022.

[BCE+ 23] S´ebastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, et al. Sparks of artificial general intelligence: Early experiments with gpt-4. arXiv preprint arXiv:2303.12712, 2023.

[BGMMS21] Emily M Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, pages 610–623, 2021.

[BJT+ 22] Mohammad Bavarian, Heewoo Jun, Nikolas Tezak, John Schulman, Christine McLeavey, Jerry Tworek, and Mark Chen. Efficient training of language models to fill in the middle. arXiv preprint arXiv:2207.14255, 2022.

[BMR+ 20] Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. Language models are few-shot learners. In Advances in Neural Information Processing Systems, volume 33, pages 1877– 1901, 2020.

[CND+ 22] Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al. Palm: Scaling language modeling with pathways. arXiv preprint arXiv:2204.02311, 2022.

[CTJ+ 21] Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al. Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374, 2021.

[DFE+ 22] Tri Dao, Dan Fu, Stefano Ermon, Atri Rudra, and Christopher R´e. Flashattention: Fast and memory-efficient exact attention with io-awareness. Advances in Neural Information Processing Systems, 35:16344–16359, 2022.

[DLT+ 23] Yann Dubois, Xuechen Li, Rohan Taori, Tianyi Zhang, Ishaan Gulrajani, Jimmy Ba, Carlos Guestrin, Percy Liang, and Tatsunori B Hashimoto. Alpacafarm: A simulation framework for methods that learn from human feedback. arXiv preprint arXiv:2305.14387, 2023.

[EL23] Ronen Eldan and Yuanzhi Li. Tinystories: How small can language models be and still speak coherent english? arXiv preprint arXiv:2305.07759, 2023. [GWS+ 23] Arnav Gudibande, Eric Wallace, Charlie Snell, Xinyang Geng, Hao Liu, Pieter Abbeel, Sergey Levine, and Dawn Song. The false promise of imitating proprietary llms. arXiv preprint arXiv:2305.15717, 2023.

[HBM+ 22] Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, Tom Hennigan, Eric Noland, Katherine Millican, George van den Driessche, Bogdan Damoc, Aurelia Guy, Simon Osindero, Karen Simonyan, Erich Elsen, Oriol Vinyals, Jack William Rae, and Laurent Sifre. An empirical analysis of compute-optimal large language model training. In Alice H. Oh, Alekh Agarwal, Danielle Belgrave, and Kyunghyun Cho, editors, Advances in Neural Information Processing Systems, 2022.

[HNA+ 17] Joel Hestness, Sharan Narang, Newsha Ardalani, Gregory Diamos, Heewoo Jun, Hassan Kianinejad, Md Mostofa Ali Patwary, Yang Yang, and Yanqi Zhou. Deep learning scaling is predictable, empirically. arXiv preprint arXiv:1712.00409, 2017.

[JWJ+ 23] Jaehun Jung, Peter West, Liwei Jiang, Faeze Brahman, Ximing Lu, Jillian Fisher, Taylor Sorensen, and Yejin Choi. Impossible distillation: from low-quality model to high-quality dataset & model for summarization and paraphrasing. arXiv preprint arXiv:2305.16635, 2023.

[KLA+ 22] Denis Kocetkov, Raymond Li, Loubna Ben Allal, Jia Li, Chenghao Mou, Carlos Mu˜noz Ferrandis, Yacine Jernite, Margaret Mitchell, Sean Hughes, Thomas Wolf, et al. The stack: 3 tb of permissively licensed source code. arXiv preprint arXiv:2211.15533, 2022.

[KMH+ 20] Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. Scaling laws for neural language models. arXiv preprint arXiv:2001.08361, 2020.

[LAZ+ 23] Raymond Li, Loubna Ben Allal, Yangtian Zi, Niklas Muennighoff, Denis Kocetkov, Chenghao Mou, Marc Marone, Christopher Akiki, Jia Li, Jenny Chim, et al. Starcoder: may the source be with you! arXiv preprint arXiv:2305.06161, 2023.

[LCC+ 22] Yujia Li, David Choi, Junyoung Chung, Nate Kushman, Julian Schrittwieser, R´emi Leblond, Tom Eccles, James Keeling, Felix Gimeno, Agustin Dal Lago, et al. Competition-level code generation with alphacode. Science, 378(6624):1092–1097, 2022.

[LGK+ 23] Zinan Lin, Sivakanth Gopi, Janardhan Kulkarni, Harsha Nori, and Sergey Yekhanin. Differentially private synthetic data via foundation model apis 1: Images. arXiv preprint arXiv:2305.15560, 2023.

[LXWZ23] Jiawei Liu, Chunqiu Steven Xia, Yuyao Wang, and Lingming Zhang. Is your code generated by chatgpt really correct? rigorous evaluation of large language models for code generation. arXiv preprint arXiv:2305.01210, 2023.

[LXZ+ 23] Ziyang Luo, Can Xu, Pu Zhao, Qingfeng Sun, Xiubo Geng, Wenxiang Hu, Chongyang Tao, Jing Ma, Qingwei Lin, and Daxin Jiang. Wizardcoder: Empowering code large language models with evol-instruct, 2023.

[LYR+ 23] Shayne Longpre, Gregory Yauney, Emily Reif, Katherine Lee, Adam Roberts, Barret Zoph, Denny Zhou, Jason Wei, Kevin Robinson, David Mimno, et al. A pretrainer’s guide to training data: Measuring the effects of data age, domain coverage, quality, & toxicity. arXiv preprint arXiv:2305.13169, 2023.

[MMJ+ 23] Subhabrata Mukherjee, Arindam Mitra, Ganesh Jawahar, Sahaj Agarwal, Hamid Palangi, and Ahmed Awadallah. Orca: Progressive learning from complex explanation traces of gpt-4. arXiv preprint arXiv:2306.02707, 2023.

[MRB+ 23] Niklas Muennighoff, Alexander M Rush, Boaz Barak, Teven Le Scao, Aleksandra Piktus, Nouamane Tazi, Sampo Pyysalo, Thomas Wolf, and Colin Raffel. Scaling data-constrained language models. arXiv preprint arXiv:2305.16264, 2023.

[NHX+ 23] Erik Nijkamp, Hiroaki Hayashi, Caiming Xiong, Silvio Savarese, and Yingbo Zhou. Codegen2: Lessons for training llms on programming and natural languages. arXiv preprint arXiv:2305.02309, 2023.

[NPH+ 22] Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong. Codegen: An open large language model for code with multi-turn program synthesis. arXiv preprint, 2022.

[NPH+ 23] Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong. Codegen: An open large language model for code with multi-turn program synthesis. ICLR, 2023.

[Ope23] OpenAI. Gpt-4 technical report, 2023. arXiv preprint arXiv:2303.08774 [cs.CL]. [Rep23] Replit. Replit dev day. https://twitter.com/Replit/status/ 1651344184593506304, 2023.

[RSR+ 20] Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. Exploring the limits of transfer learning with a unified text-to-text transformer. The Journal of Machine Learning Research, 21(1):5485–5551, 2020.

[SLP+ 21] Jianlin Su, Yu Lu, Shengfeng Pan, Bo Wen, and Yunfeng Liu. Roformer: Enhanced transformer with rotary position embedding. arXiv preprint arXiv:2104.09864, 2021.

[SSZ+ 23] Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao, Yarin Gal, Nicolas Papernot, and Ross Anderson. Model dementia: Generated data makes models forget. arXiv preprint arXiv:2305.17493, 2023

[TGZ+ 23] Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. Stanford alpaca: An instruction-following llama model. https://github.com/tatsu-lab/stanford_alpaca, 2023.

[VSP+ 17] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, L ukasz Kaiser, and Illia Polosukhin. Attention is all you need. In Advances in Neural Information Processing Systems, volume 30, 2017.

[WKM+ 22] Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A Smith, Daniel Khashabi, and Hannaneh Hajishirzi. Self-instruct: Aligning language model with self generated instructions. arXiv preprint arXiv:2212.10560, 2022.

[WLG+ 23] Yue Wang, Hung Le, Akhilesh Deepak Gotmare, Nghi DQ Bui, Junnan Li, and Steven CH Hoi. Codet5+: Open code large language models for code understanding and generation. arXiv preprint arXiv:2305.07922, 2023.

[WTB+ 22] Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, Ed H. Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, and William Fedus. Emergent abilities of large language models. Transactions on Machine Learning Research, 2022. Survey Certification.

[YGK+ 23] Da Yu, Sivakanth Gopi, Janardhan Kulkarni, Zinan Lin, Saurabh Naik, Tomasz Lukasz Religa, Jian Yin, and Huishuai Zhang. Selective pre-training for private fine-tuning. arXiv preprint arXiv:2305.13865, 2023.

[ZXZ+ 23] Qinkai Zheng, Xiao Xia, Xu Zou, Yuxiao Dong, Shan Wang, Yufei Xue, Zihan Wang, Lei Shen, Andi Wang, Yang Li, Teng Su, Zhilin Yang, and Jie Tang. Codegeex: A pre-trained model for code generation with multilingual evaluations on humaneval-x, 2023.

A. Дополнительные примеры для Раздела 3

В Разделе 3 мы обсудили на нескольких примерах, как дообученные модели демонстрируют значительное улучшение по сравнению с базовой моделью при выполнении задач, которые не представлены в наборе данных для дообучения. Здесь мы предоставляем дополнительные примеры и детали, чтобы проиллюстрировать эти улучшения.

A.1 Дообучение (файнтюнинг) улучшает способность модели к пониманию

Следующий пример является сложным из-за обилия различных логических операторов, таких как «и» и «не», которые связывают множество утверждений. До файнтюнинга модель путается в логике, заложенной в утверждении. Она с трудом различает «элементы» и «индексы» и не уверена, какой массив использовать. Напротив, дообученная модель успешно идентифицирует все соответствующие массивы и индексы, корректно реализуя логические операторы.

Prompt: Input is an array A, an array B, an array C, an array E, a numpy matrix D. I want to find all the indices of D whose elements are in the union of B and C and whose row index is not in A and column index is in D. Loop through the elements in D. Return the list of indices.

Промпт: Входные данные: массив A, массив B, массив C, массив E, матрица numpy D. Найди все индексы матрицы D, элементы которых находятся в объединении B и C, и чей индекс строки не входит в A, а индекс столбца входит в D. Пройдись по элементам матрицы D. Верни список индексов.

# phi-1
indices = []
for i in range(D.shape[0]):
    for j in range(D.shape[1]):
        if D[i][j] in B or D[i][j] in C:
            if i not in A and j in D:
                indices.append((i,j))
return indices
# phi-1-base
indices = []
for i in range(len(D)):
    for j in range(len(D[i])):
        if D[i][j] in B and D[i][j] not in A:
            indices.append(j)
return indices
# phi-1-small
indices = []
for i in range(D.shape[1]):
    if D[0][i] in B and D[0][i] not in A and D[i][0] in C and D[i][0] not in E:
        indices.append(i)
return indices

A.2 Дообучение улучшает способность модели использовать внешние библиотеки

Пример с PyTorch.

В этом примере с использованием PyTorch мы просим модель написать модифицированный шаг обновления градиента:

Prompt: Write a pytorch function. The input is a model, take the gradient of each parameter of the model, do an SVD of each gradient to get U, Sigma, V, take the top k singular values of Sigma as Sigmak, then do a update step to the parameter using Uk*Sigmak * Vk times the norm of the parameter.

Промпт: Напиши функцию на PyTorch. Входные данные — это модель. Возьми градиент каждого параметра модели, выполни сингулярное разложение (SVD) каждого градиента, чтобы получить U, Sigma, V, возьми топ-k сингулярных значений Sigma как Sigma k, затем выполни шаг обновления параметра, используя Uk * Sigma k * Vk, умноженное на норму параметра.

# phi-1
...
for p in model.parameters():
    if p.grad is not None:
        U, Sigma, V = p.grad.svd()
        Sigmak = Sigma[:k].diag()
        p.data.add_(torch.mm(U[:,:k], torch.mm(Sigmak, V[:k,:])).mul_(norm))
# phi-1-base
...
self.model = model
self.k = k
self.eps = eps
self.max_iter = max_iter
self.verbose = verbose
self.svd_u = None
self.svd_sigma = None
self.svd_v = None
self.svd_k = None
self.svd_u_k = None
...
# phi-1-small
def svd(x):
    u, s, v = np.linalg.svd(x)
    return u, s, v
def update_parameter(parameter, grad):
    u, s, v = svd(grad)
    parameter = u[:, :k]
    np.diag(s[:k]) v[:k, :]
    parameter += np.diag(sigmoid(parameter))
    return parameter

Код, созданный моделью phi-1-small, демонстрирует недостаточное понимание API и ограничения в понимании задачи, что приводит к нескольким значительным ошибкам. Во-первых, он использует np.linalg.svd(x), что несовместимо с тензорами PyTorch. Во-вторых, он ошибочно присваивает результат SVD непосредственно параметру, отклоняясь от инструкции в запросе, где требуется добавить его. С другой стороны, phi-1-base полностью теряется и выдает лишь последовательность бессмысленных определений. Это указывает на то, что улучшение способности модели к логическому рассуждению (даже для простых функций) может значительно повысить общую способность модели к написанию кода.

Пример с Pyplot

Последний пример связан с использованием API Pyplot. Модель обычно хорошо справляется с Pyplot, поэтому мы разработали сложную задачу, попросив модель реализовать анимацию.

Prompt: Create an x array that are integers from 1 to 20, y array that is equal to the
elementwise cos of x divided by element-wise sqrt(x + 1). Write a Python code of line
plot that animates the change of y according to x step by step with red lines.
However, during the animation, when x[i] is larger than 11, change the animation line
color from red to green.

Промпт: Создай массив x, состоящий из целых чисел от 1 до 20, и массив y, равный cos x каждого элемента, деленному на квадратный корень из (x + 1) каждого элемента. Напиши код на Python для построения линейного графика, который анимирует изменение y в зависимости от x шаг за шагом с использованием красных линий. Однако во время анимации, когда x[i] становится больше 11, изменит цвет линии анимации с красного на зеленый.

# phi-1
...
x = np.arange(1, 21)
y = np.cos(x) / np.sqrt(x + 1)
fig, ax = plt.subplots()
line, = ax.plot([], [], color='red')
...
ax.set_title('Animation of Cosine and Cosine-Square-Root')
def animate(i):
    if x[i] > 11:
        line.set_color('green')
    else:
        line.set_color('red')
        line.set_data(x[:i+1], y[:i+1])
    return line,
anim = animation.FuncAnimation(fig, animate, frames=len(x), interval=500, blit=True)
plt.show()
# phi-1-base
...
fig, ax = plt.subplots()
x = np.arange(1, 21)
y = np.cos(x) / np.sqrt(x + 1)
line, = ax.plot([], [], lw=2)
def init():
    line.set_data([], [])
    return line,
def animate(i):
    if x[i] > 11:
        line.set_color('green')
    else:
        line.set_color('red')
    return line,
anim = FuncAnimation(fig, animate, init_func=init, frames=len(x), interval=50, blit=True)
# phi-1-small
...
fig, ax = plt.subplots()
x_array = list(range(1, 21))
y_array = [math.cos(xi/10) for xi in x_array]
def animate(i):
    ...
    ax.plot(x_array, y_array)
    if x_array[i] > 11:
        color = "red"
    else:
        color = "green"
    ax.scatter(x_array[i],
    y_array[i], color=color)
    return ax
ani = animation.FuncAnimation(fig, animate, frames=len(x_array), interval=50, blit=True)
plt.show()

phi-1-small выдает некорректный результат. С точки зрения API, модель не понимает требование «линейный график» и использует только точечный график (и даже он реализован неправильно). phi-1-base правильно понимает шаблон, но упускает основную функцию для обновления линейного графика на каждой итерации.

B. Ограничения модели phi-1

Хотя дообучение на простых упражнениях значительно улучшает общую производительность модели, существуют определенные ограничения, присущие нашей модели, которые невозможно преодолеть только за счет файнтюнинга. Во-первых, наша модель имеет всего 1,3 миллиарда параметров и обучалась на 7 миллиардах токенов, что ограничивает ее способность справляться с более сложными задачами, такими как разработка сложного приложения на Flask, по сравнению с другими моделями, такими как Starcoder. Помимо ограничений нашей модели в плане универсальности по сравнению с StarCoder или ChatGPT, мы также выделили несколько других слабых сторон:

Чувствительность к вариациям в запросах.

Наша модель чувствительна к различным изменениям в запросах. Во-первых, ее производительность значительно снижается с увеличением длины запроса, так как она склонна игнорировать, забывать или неправильно интерпретировать части запроса, если он слишком длинный. Например, наша модель не справляется, когда мы увеличиваем количество слоев с 3 до 4 в следующем случае. Мы предполагаем, что эта проблема возникает из-за того, что наши упражнения в основном состоят из коротких запросов. Кроме того, результат генерации может качественно отличаться при незначительном изменении запроса. В этом случае, с добавлением команды import torch, модель часто успешно справляется с задачей, которая ранее вызывала у нее затруднения.

Пример запроса 1:

Create a neural network class in Pytorch with 3 layers. Implement a new "rr" method to this new function: It takes input x. Apply the first layer on x and assign the output to y, then apply the second layer on y and assign the output to z, then apply the third layer on z and assign it to w, then sum up y, z, w and assign it to Q. Return Q

Создай класс нейронной сети на PyTorch с 3 слоями. Реализуй новый метод "rr" для этой функции: Он принимает входные данные x. Примени первый слой к x и присвой результат y, затем примени второй слой к y и присвой результат z, затем примени третий слой к z и присвой результат w, затем сложи y, z, w и присвой результат Q. Верни Q.

class NeuralNetwork(nn.Module):
    def __init__(self):
        super(NeuralNetwork, self).__init__()
            self.layer1 = nn.Linear(2, 5)
            self.layer2 = nn.Linear(5, 3)
            self.layer3 = nn.Linear(3, 1)
    def rr(self, x):
        y = self.layer1(x)
        z = self.layer2(y)
        w = self.layer3(z)
        Q = y + z + w
        return Q

Пример запроса 2:

Create a neural network class in Pytorch with 4 layers. Implement a new "rr" method to this new function: It takes input x. Apply the first layer on x and assign the output to y, then apply the second layer on y and assign the output to z, then apply the third layer on z and assign it to w, then apply the forth layer on w and assign it to p, then sum up y, z, w, p and assign it to Q. Return Q.

Создай класс нейронной сети на PyTorch с 4 слоями. Реализуй новый метод "rr" для этой функции: Он принимает входные данные x. Примени первый слой к x и присвой результат y, затем примени второй слой к y и присвой результат z, затем примени третий слой к z и присвой результат w, затем примени четвертый слой к w и присвой результат p, затем сложи y, z, w, p и присвой результат Q. Верни Q.

class NeuralNetwork:
    def __init__(self):
        self.layers = []
    def add_layer(self, layer):
        self.layers.append(layer)
        ...
    def rr(self, x):
        y = self.forward(x)
        z = self.forward(y)
        w = self.forward(z)
        p = self.forward(w)
        Q = y + z + w + p
        return Q

Пример запроса 3:

import torch Create a neural network class in Pytorch with 4 layers. Implement a new "rr" method to this new function: It takes input x. Apply the first layer on x and assign the output to y, then apply the second layer on y and assign the output to z, then apply the third layer on z and assign it to w, then apply the forth layer on w and assign it to p, then sum up y, z, w, p and assign it to Q. Return Q.

import torch Создай класс нейронной сети на PyTorch с 4 слоями. Реализуй новый метод "rr" для этой функции: Он принимает входные данные x. Примени первый слой к x и присвой результат y, затем примени второй слой к y и присвой результат z, затем примени третий слой к z и присвой результат w, затем примени четвертый слой к w и присвой результат p, затем сложи y, z, w, p и присвой результат Q. Верни Q.

class NeuralNetwork(torch.nn.Module):
def __init__(self):
    super(NeuralNetwork, self).__init__()
    self.layer1 = torch.nn.Linear(10, 20)
...
def forward(self, x):
    x = torch.nn.functional.relu(self.layer1(x))
    y = torch.nn.functional.relu(self.layer2(x))
    z = torch.nn.functional.relu(self.layer3(y))
    w = torch.nn.functional.relu(self.layer4(z))
    p = torch.nn.functional.relu(self.output(w))
    Q = p + y + z + w
    return Q

Чувствительность к естественным языковым запросам

Модель phi-1 демонстрирует меньшую устойчивость к обработке естественного языка по сравнению с ChatGPT или StarCoder, особенно в случае неоднозначных запросов. Это может быть связано с тем, что мы исключили определенные типы данных из процесса обучения, чтобы гарантировать качество на уровне учебника.

Например, наша модель испытывает трудности с термином «неизменный» (unchanged) и с интерпретацией нумерованных списков в запросе.

Пример запроса 1:

Alice is moving a point along x,y axes at every iteration. Every time Alice moves the point randomly with one of the options: (1). x increases by 2, y decreases by 5; (2). x increases by 1, y decreases by 2, (3). x stays unchanged, y increase by 4. Simulate the movement of the points.

Алиса перемещает точку вдоль осей x и y на каждой итерации. Каждый раз Алиса перемещает точку случайным образом одним из вариантов: (1) x увеличивается на 2, y уменьшается на 5; (2) x увеличивается на 1, y уменьшается на 2; (3) x остается неизменным, y увеличивается на 4. Смоделируй движение точки.

x, y = 0, 0
for i in range(10):
    move = random.choice([1, 2,3])
    if move == 1:
        x += 2
        y -= 5
    elif move == 2:
        x += 1
        y -= 2
    elif move == 3:
        x = 0
        y += 4
return f"Alice's final position is ({x}, {y})."

Пример запроса 2:

Alice is moving a point along x,y axes at every iteration. Every time Alice moves the point randomly with one of the options: (1). x increases by 2, y decreases by 5; (2). x increases by 1, y decreases by 2, (3). x stays at x, y increase by 4. Simulate the movement of the points.

Алиса перемещает точку вдоль осей x и y на каждой итерации. Каждый раз Алиса перемещает точку случайным образом одним из вариантов: (1) x увеличивается на 2, y уменьшается на 5; (2) x увеличивается на 1, y уменьшается на 2; (3) x остается равным x, y увеличивается на 4. Смоделируй движение точки.

x, y = 0, 0
for i in range(10):
    move = random.choice([1, 2, 3])
    if move == 1:
        x += 2
        y -= 5
    elif move == 2:
        x += 1
        y -= 2
    elif move == 3:
        x = x
        y += 4
print(f"Alice moved {move}
point to ({x}, {y})")

Пример запроса 3:

Alice is moving a point along x,y axes at every iteration. Every time Alice moves the point randomly with one of the options: (1). move by (2, -5); (2). move by (1, 2); (3). move by (0, 4); Simulate the movement of the points.

Алиса перемещает точку вдоль осей x и y на каждой итерации. Каждый раз Алиса перемещает точку случайным образом одним из вариантов: (1) перемещение на (2, -5); (2) перемещение на (1, 2); (3) перемещение на (0, 4); Смоделируй движение точки.

x, y = 0, 0
for i in range(n):
    move = random.choice([(1, 2), (2, -5), (1, 2), (3, 0), (0, 4)])
    x += move[0]
    y += move[1]
    return f"Alice moved {x} units to the right and {y} units down after {n} iterations."
  
# Note: the model hallucinates the moves (1, 2), (3, 0).

Эти примеры показывают, что модель phi-1 может испытывать трудности с интерпретацией естественного языка, особенно если присутствуют неоднозначные или сложные конструкции. Это подчеркивает необходимость дальнейшего улучшения модели в плане обработки естественного языка и повышения ее устойчивости к вариативным запросам.

Плохая способность к счету и пространственному мышлению

Одним из основных ограничений нашей модели, особенно в сравнении с альтернативами, такими как StarCoder, является ее производительность на задачах, связанных с подсчетом и пространственным мышлением. Модель с трудом поддерживает точные данные о количестве и расположении элементов в сцене. Чтобы проиллюстрировать это, рассмотрим следующий пример:

Пример запроса:

Generate a tkinter application, I want two textfields side by side at the top, three buttons at the bottom, one named A on the left, one named B on the right, one named C at the middle.

Создай приложение на tkinter. Мне нужно два текстовых поля, расположенных рядом в верхней части, три кнопки в нижней части: одна с именем A слева, одна с именем B справа и одна с именем C по центру.

# phi-1
...
text_a = tk.Text(root, width=30, height=5)
text_b = tk.Text(root, width=30, height=5)
text_c = tk.Text(root, width=30, height=5)

# Create the buttons
button_a = tk.Button(root, text="A", width=10, height=2)
button_b = tk.Button(root, text="B", width=10, height=2)
button_c = tk.Button(root, text="C", width=10, height=2)

# Place the text fields and buttons in the top row
text_a.grid(row=0, column=0, padx=5, pady=5)
text_b.grid(row=0, column=1, padx=5, pady=5)
text_c.grid(row=0, column=2, padx=5, pady=5)

# Place the buttons in the bottom row
button_a.grid(row=1, column=0, columnspan=3, padx=5, pady=5)
button_b.grid(row=1, column=1, columnspan=3, padx=5, pady=5)
button_c.grid(row=1, column=2, columnspan=3, padx=5, pady=5)
...
# phi-1-small

window = tk.Tk()
window.title("TextFields")
# Create the left and right textfields
text1 = tk.Text(window, text="A")
text1.pack()
text2 = tk.Text(window, text="B")
text2.pack()
# Create the middle button
button = tk.Button(window, text="C")
button.pack()
# Start the tkinter application
window.mainloop()
# phi-1-base
root = tk.Tk()
root.title("Text Fields")
root.geometry("300x200")
root.resizable(False, False)
root.configure(background="white")
root.configure(font=("Arial", 12))
root.configure(foreground="black")
root.configure(relief="groove")
root.configure(padx=10, pady=10)
root.configure(state="disabled")
# The model keeps outputting nonsensical code.

Несмотря на улучшения, достигнутые за счет дообучения, наша модель по-прежнему испытывает трудности с подсчетом и пространственным мышлением. Она создает лишнее текстовое поле и неправильно размещает кнопку в сцене.

C. Примеры для Раздела 5

В этом разделе мы предоставляем примеры пар кода, с разными коэффициентами совпадения AST. Кроме того, мы приводим пример пары кода, полученной с использованием эмбеддингового расстояния в качестве меры схожести.

Коэффициент совпадения AST = 1.0

Здесь задачи на программирование требуют одинакового логического рассуждения, хотя формулировки запросов могут значительно отличаться. В частности, в запросе используется реальное событие (например, расстояние между отверстиями на линии), чтобы неявно обучить модели базовой задаче нахождения ближайшей пары элементов в массиве.

HumanEval

from typing import List, Tuple
def find_closest_elements(numbers: List[float]) -> Tuple[float, float]:
    """ From a supplied list of numbers (of
    length at least two) select and return two
    that are the closest to each
    other and return them in order (smaller
    number, larger number).
    >>> find_closest_elements([1.0, 2.0, 3.0,
    4.0, 5.0, 2.2])
    (2.0, 2.2)
    >>> find_closest_elements([1.0, 2.0, 3.0,
    4.0, 5.0, 2.0])
    (2.0, 2.0)
    """
    numbers.sort()
    min_diff = float('inf')
    closest_pair = None
    for i in range(len(numbers) - 1):
    diff = numbers[i+1] - numbers[i]
    if diff < min_diff:
    min_diff = diff
    closest_pair = (numbers[i], numbers[i
    +1])
    return closest_pair

CodeExercises

from typing import List, Tuple
def find_closest_two_holes(holes: List[int]) -> (int, int):
    """
    Finds the two closest holes on a line.
    Args:
        holes: A list of integers representing
        the positions of holes on a line.
    Returns:
        A tuple of two integers representing the
        positions of the two closest holes on the
        line.
    """
    holes.sort()
    min_distance = float('inf')
    closest_holes = None
    for i in range(len(holes) - 1):
        distance = holes[i+1] - holes[i]
        if distance < min_distance:
            min_distance = distance
            closest_holes = (holes[i], holes[i+1])
    return closest_holes

Коэффициент совпадения AST = 0.96

Здесь две задачи используют схожие логические рассуждения и концепции программирования, но их запросы требуют выполнения разных задач, например, возвращение пары чисел в отличие от вычисления их среднего значения.

HumanEval

from typing import List
def find_closest_elements(numbers: List[float]) -> Tuple[float, float]:
    """ From a supplied list of numbers (of
    length at least two) select and return two
    that are the closest to each
    other and return them in order (smaller
    number, larger number).
    >>> find_closest_elements([1.0, 2.0, 3.0,
    4.0, 5.0, 2.2])
    (2.0, 2.2)
    >>> find_closest_elements([1.0, 2.0, 3.0,
    4.0, 5.0, 2.0])
    (2.0, 2.0)
    """
    numbers.sort()
    min_diff = float('inf')
    closest_pair = None
    for i in range(len(numbers) - 1):
        diff = numbers[i+1] - numbers[i]
        if diff < min_diff:
        min_diff = diff
        closest_pair = (numbers[i], numbers[i+1])
    return closest_pair

CodeExercises

from typing import List
def find_closest_pair_average(li: List[int]) -> float:
    """
    Returns the average of the two integers in
    the list that are closest to each other.
    If there are multiple pairs with the same
    minimum difference, the function returns the
    average of the first pair it encounters.
    Args:
    - li: a list of integers
    Returns:
    - a float representing the average of the two
    integers in the list that are closest to
    each other
    """
    li.sort()
    min_diff = float('inf')
    closest_pair = None
    for i in range(len(li)-1):
        diff = li[i+1] - li[i]
        if diff < min_diff:
            min_diff = diff
            closest_pair = (li[i], li[i+1])
    return sum(closest_pair) / 2

Коэффициент совпадения AST ≤ 0.9

Когда коэффициент совпадения AST ≤ 0.9, пары кода становятся менее схожими, так как их структура и логика начинают значительно отличаться. Здесь уровень совпадения AST (абстрактного синтаксического дерева) составляет 0.9 и 0.83 соответственно.

HumanEval

from typing import List
def all_prefixes(string: str) -> List[str]:
    """ Return list of all prefixes from
    shortest to longest of the input string
    >>> all_prefixes('abc')
    ['a', 'ab', 'abc']
    """
    prefixes = []
    for i in range(len(string)):
        prefixes.append(string[:i+1])
    return prefixes

CodeExercises

from typing import List
def increase_after_each(li: List[int]) -> List[int]:
    """
    Given a list of integers, returns a new list
    where each element is increased by the
    number of elements in the original list that
    come after it.
    Args:
    - li: A list of integers
    Returns:
    - A new list of integers where each element
    is increased by the number of elements in
    the original list that come after it.
    """
    result = []
    for i in range(len(li)):
        result.append(li[i] + len(li[i+1:]))
    return result

HumanEval

from typing import List
def rescale_to_unit(numbers: List[float]) -> List[float]:
    """ Given list of numbers (of at least two
    elements), apply a linear transform to that
    list, such that the smallest number will
    become 0 and the largest will become 1
    >>> rescale_to_unit([1.0, 2.0, 3.0, 4.0,
    5.0])
    [0.0, 0.25, 0.5, 0.75, 1.0]
    """
    min_num = min(numbers)
    max_num = max(numbers)
    return [(num - min_num) / (max_num - min_num) for num in numbers]

CodeExercises

from typing import List
def frequency_ranges_plot(values: List[float]) -> List[List[int]]:
    """
    Returns a list of lists where each inner list
    represents a frequency range and contains
    the count of values
    that fall within that range. The ranges are
    determined by dividing the range of values
    into 10 equal parts.
    Args:
    - values: A list of float values
    Returns:
    - A list of lists where each inner list
    contains two integers representing the lower
    and upper bounds of the range
    and the count of values that fall within that
    range.
    """
    min_val = min(values)
    max_val = max(values)
    range_size = (max_val - min_val) / 10
    ranges = [[min_val + i*range_size, min_val + (i+1)*range_size, 0] for i in ange(10)]
    for val in values:
        for i, r in enumerate(ranges):
            if r[0] <= val < r[1]:
                ranges[i][2] += 1
                break
    return [[int(r[0]), int(r[1]), r[2]] for r in ranges]

Эмбеддинговое расстояние = 0.16

Здесь две задачи имеют схожие Python Docstrings, имена функций, а также структуру кода, что можно выявить с помощью L2-расстояния между нормализованными эмбеддингами CodeGen-Mono 350M для каждой из них.

HumanEval

def sum_product(numbers: List[int]) -> Tuple[int, int]:
    """ For a given list of integers, return a
    tuple consisting of a sum and a product of
    all the integers in a list.
    Empty sum should be equal to 0 and empty
    product should be equal to 1.
    >>> sum_product([])
    (0, 1)
    >>> sum_product([1, 2, 3, 4])
    (10, 24)
    """
    sum_value = 0
    prod_value = 1
    for n in numbers:
        sum_value += n
        prod_value *= n
    return sum_value, prod_value

CodeExercises

from typing import List, Tuple
def all_numbers_sum_product(numbers: List[int]) -> Tuple[int,int]:
    """
    Returns a tuple containing the sum and
    product of all the numbers in the input list.
    Args:
    - numbers (List[int]): a list of integers
    Returns:
    - a tuple containing two integers:
    - the sum of all the numbers in the input
    list
    - the product of all the numbers in the
    input list
    """
    sum_of_numbers = 0
    product_of_numbers = 1
    for num in numbers:
        sum_of_numbers += num
        product_of_numbers *= num
    return (sum_of_numbers, product_of_numbers)

Источник

  • 30.03.25 06:22 grace111

    I recommend Marie ([email protected] and WhatsApp: +1 7127594675) for recovering lost or stolen bitcoin, USDT, or any other cryptocurrency from fraudulent investment sites because they are very knowledgeable in the industry and will reimburse all of your money. I can say with confidence that she was the only one who was able to credit my account with $52,760 of the money that had gone missing, based on my prior transactions with them. She was the only one who could. Since they are the only ones that can fully return your missing funds to your account without any deductions, I sincerely appreciate their job and am recommending her to you today.

  • 31.03.25 11:00 maryjul75

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  • 02.04.25 23:16 frederickhandy

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  • 02.04.25 23:18 frederickhandy

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  • 03.04.25 07:57 messijohn

    "XRP Stolen by Fake Trading Platform? Here’s How I Got Mine Back; I was scammed by a fake trading platform and lost my XRP, but thankfully, Sylvester Bryant helped me recover it. His expertise in asset recovery is unmatched, and I highly recommend him if you’ve been a victim of an online scam. You can reach out to Sylvester for professional assistance via: 📧 Email: Yt7cracker@gmail. com 📞 WhatsApp/Text: +1 (512) 577-7957 He’s trustworthy, skilled, and has helped many people recover their stolen crypto from various scams. Don’t hesitate to contact him if you need help!"

  • 03.04.25 10:58 elizabethrush89

    God bless Capital Crypto Recover Services for the marvelous work you did in my life, I have learned the hard way that even the most sensible investors can fall victim to scams. When my USD was stolen, for anyone who has fallen victim to one of the bitcoin binary investment scams that are currently ongoing, I felt betrayal and upset. But then I was reading a post on site when I saw a testimony of Wendy Taylor online who recommended that Capital Crypto Recovery has helped her recover scammed funds within 24 hours. after reaching out to this cyber security firm that was able to help me recover my stolen digital assets and bitcoin. I’m genuinely blown away by their amazing service and professionalism. I never imagined I’d be able to get my money back until I complained to Capital Crypto Recovery Services about my difficulties and gave all of the necessary paperwork. I was astounded that it took them 12 hours to reclaim my stolen money back. Without a doubt, my USDT assets were successfully recovered from the scam platform, Thank you so much Sir, I strongly recommend Capital Crypto Recover for any of your bitcoin recovery, digital funds recovery, hacking, and cybersecurity concerns. You reach them Call/Text Number +1 (336)390-6684 His Email: [email protected] Contact Telegram: @Capitalcryptorecover

  • 03.04.25 10:58 elizabethrush89

    God bless Capital Crypto Recover Services for the marvelous work you did in my life, I have learned the hard way that even the most sensible investors can fall victim to scams. When my USD was stolen, for anyone who has fallen victim to one of the bitcoin binary investment scams that are currently ongoing, I felt betrayal and upset. But then I was reading a post on site when I saw a testimony of Wendy Taylor online who recommended that Capital Crypto Recovery has helped her recover scammed funds within 24 hours. after reaching out to this cyber security firm that was able to help me recover my stolen digital assets and bitcoin. I’m genuinely blown away by their amazing service and professionalism. I never imagined I’d be able to get my money back until I complained to Capital Crypto Recovery Services about my difficulties and gave all of the necessary paperwork. I was astounded that it took them 12 hours to reclaim my stolen money back. Without a doubt, my USDT assets were successfully recovered from the scam platform, Thank you so much Sir, I strongly recommend Capital Crypto Recover for any of your bitcoin recovery, digital funds recovery, hacking, and cybersecurity concerns. You reach them Call/Text Number +1 (336)390-6684 His Email: [email protected] Contact Telegram: @Capitalcryptorecover His website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 04.04.25 09:49 mkihn634

    When my $870,000 Bitcoin investment was trapped in the crumbling ruins of an offshore exchange, I felt as if my financial future had been buried at sea. It was meant to be my safety net, a shield against life’s uncertainties, but now it seemed like nothing more than a digital ghost. Each news update about the exchange's insolvency hit me like a tidal wave, dragging my hope under. It was a sleepless night, scanning through horror stories in forums as other people were losing everything. I could see my dreams flying out the window. That is until a former client, who had weathered a similar storm, said GRAYWARE TECH SERVICES in a hushed but confident tone. "They are the best there is," he said. "They were like detectives and lawyers.". Desperation drove me to call them, and in the initial discussion, I knew I was dealing with experts who had experienced everything. Their legal experts were well-versed with international financial rules like the back of their hands. They deciphered the network of shell corporations and offshore locations quicker than I could update my email. Their technical team, no less relentless, followed the transaction trails with precision akin to a surgeon. What impressed me most was their thoroughness. Every update came with legal documentation so polished it looked fit for a courtroom. They liaised with authorities across borders, cutting through red tape with the precision of a seasoned diplomat. When obstacles arose, and they did, the team adapted without breaking stride. Their persistence became my anchor. Exactly 27 days after my initial call, I received an email that made my heart skip. My Bitcoin had been recovered and safely transferred to my new secure wallet. I stared at the screen, tears mixing with disbelief and relief. GRAYWARE TECH SERVICES not only retrieved my money but also restored my faith in getting finances. They guided me through the entire process of protecting my assets in this unstable world known as the online space. I now sleep soundly knowing that an impenetrable system shields my backside, thanks to their assistance. Their legal acumen is as sharp as their technological prowess. I owe my financial future to their tireless work. They truly are the guardians of the digital age. You can reach them on web at ( https://graywaretechservices.com/ )    also on Mail: ([email protected]) whatsapp (+18582759508)

  • 06.04.25 02:29 famousmullica

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  • 06.04.25 11:30 maryjul75

    As at September this year I got scammed by a fake investment broker, they took my savings, happiness, health, hope, trust and left me in tears and agony. My next thought was capitalized on suicide attempts, I also tried two different hackers who lured me to borrow for my colleagues but yet another scam. I saw a link on my Facebook group which I joined and luckily I came across www.fastrecoveryagent.com, they work in hand with the FBI in the united states. So I reported my case with evidence of payment made to the so-called investment broker, the ic3 investigator's carried out investigation to confirm if I was also tell the truth and after they must have concluded on my case I received back my money. I'm so grateful for your help today, words alone can't show how Happy and Alive I'm ever since I came across your great works. Thanks once more.

  • 07.04.25 11:16 Lindaporche5

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  • 07.04.25 11:16 Lindaporche5

    They froze my $275K in Bitcoin. Blockchain cyber retrieve took actions. Running a startup in Nigeria is already a wild ride power cuts, red tape, and FX rates that dance like Afrobeats. But the day the government banned crypto transactions? Game over. My funds were locked in an exchange wallet. No access, No help I tried everything VPNs, New accounts Support bots. Nothing changed, Then someone in a Signal group dropped the name: BLOCKCHAIN CYBER RETRIEVE. These folks didn’t fight the exchange they outsmarted it. Peer-to-peer protocols. DeFi tools. Secure escrow networks. Nine  days later, I got the email “Wallet restored. Check your balance. Every Single Satoshi. Was back. Since then? Fully decentralized, Unbothered, Unbanked. Whenever new laws try to stifle African innovation, I just sip palm wine and say: Let them try. We’ve got BLOCKCHAIN CYBER RETRIEVE now.” CONTACT THEM: Whatsapp +1, 5,2,0, 5,6,4, 8,3 0 0  Email: B L O C K C H A I N C Y B E R R E T R I E V E @ P O S T . C O M  OR   SUPPORT @ B L O C K C H A I N C Y B E R R E T R I E V E .O R G

  • 07.04.25 18:09 rashmiramesh

    I was introduced to crypto by my son a few years ago, I invested in USDT and BTC using Binance, I have several accounts including personal bank accounts so I was unable to keep up with all of them and ended up forgetting my secret codes used in accessing the account, I asked my son to help me since he had introduced me Crypto, unfortunately he gave me bad news that I had lost my investment, I was so heartbroken considering I had invested my life saving of $70,700, I narrated my ordeal to one of my friends who happened to know someone who had a similar experience, so after I met him he directed to where he got help, he told me that LEE ULTIMATE HACKER who were able to help him with his recovery problem, I quickly contacted them to help me with my lost funds, I was a bit skeptical about it coz of what I had gone through the last few days, the frustration and anxiety was getting to me, after contacting LEE ULTIMATE HACKER one of their team members took me through the recovery process explaining on how it works and what was required from my end ,he informed me that it would take 12 hours for my funds to be recovered, I was so anxious but they assured me that all will be well and soon enough I will be able to have full control of my wallet, true to their word LEE ULTIMATE HACKER team were able to recover my wallet and I was able to access and change my log ins to my wallet, I was so happy and I couldn’t believe it I logged in and out of my account a few times just to be sure, for any lost crypto contact LEE ULTIMATE HACKER via LEEULTIMATEHACKER @ AOL . COM telegram: LEEULTIMATE wh@tsapp +1 (715) 314 - 9248 the solution to all your recovery problems.

  • 07.04.25 18:32 elizabethrush89

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  • 07.04.25 18:32 elizabethrush89

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  • 07.04.25 21:03 elizabethrush89

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  • 08.04.25 03:00 natashaohnson

    Working in conflict zones means improvising. When normal banking channels failed us, our NGO relied on Bitcoin to buy medical supplies directly. It worked, until a missile strike took out our field office, along with the hardware wallet that stored $410,000 in funds. Overnight, our ability to deliver life-saving aid is paralyzed. Amidst the chaos, I reached out to contacts in the humanitarian world. A UN aid worker whispered a name: Cyber Constable Intelligence. "They're the ones who can help you recover lost crypto," he assured me. Despair and hope clashed as I dialed their team on a satellite phone in a conflict zone. What followed was nothing short of a virtual rescue mission. Cyber Constable Intelligence's blockchain forensic experts didn't simply "recover" our assets; they improvised a fix like battlefield medics performing triage. They tracked our wallet's blockchain timestamps, reconstructing lost credentials from synced backups and transaction history. Working under direst duress, we communicated information between spotty internet and backup power sources. Cyber Constable Intelligence team members improvised, rendering security protocols impenetrable as they worked through the jurisdictional nightmares of working within war zones. Every update from them was a pulse that kept our mission alive. After our last available backup failed, they instituted a complex cryptographic reconstruction technique, a process I still don't understand, but it worked. Twelve days later, my satellite device displayed a message: "Access restored. Funds secured." It was not money. It was bandages, antibiotics, clean water, and hope. Thanks to Cyber Constable Intelligence, we replenish our medical supplies, ensuring that patients, innocent victims who had been caught in the crossfire, received the treatment they deserved. More than restoration, they advised us on decentralized storage and multi-signature security for long-term durability. We don't simply utilize Bitcoin presently; we utilize it astutely. Now, each time I sign a crypto transaction, I remember that minute, receiving life-saving medication that might not have come but for this group. In times of war, not every hero wears a uniform. Some carry keyboards, hunting down lost assets and securing humanitarian aid. Cyber Constable Intelligence not only restored our crypto, they kept our mission in the battle. If you think Bitcoin is just an investment, think again. To us, it's a lifeline. CYBER CONSTABLE INTELLIGENCE INFO: WhatsApp: 1 252378-7611 Website info; www.cyberconstableintelligence.com Email Info [email protected] Telegram Info: https://t.me/cyberconstable

  • 10.04.25 23:26 patricialovick86

    How To Recover Your Bitcoin Without Falling Victim To Scams: A  Testimony Experience With Capital Crypto Recover Services, Contact Telegram: @Capitalcryptorecover Dear Everyone, I would like to take a moment to share my positive experience with Capital Crypto Recover Services. Initially, I was unsure if it would be possible to recover my stolen bitcoins. However, with their expertise and professionalism, I was able to fully recover my funds. Unfortunately, many individuals fall victim to scams in the cryptocurrency space, especially those involving fraudulent investment platforms. However, I advise caution, as not all recovery services are legitimate. I personally lost $273,000 worth of Bitcoin from my Binance account due to a deceptive platform. If you have suffered a similar loss, you may be considering crypto recovery, The Capital Crypto Recover is the most knowledgeable and effective Capital Crypto Recovery Services assisted me in recovering my stolen funds within 24 hours, after getting access to my wallet. Their service was not only prompt but also highly professional and effective, and many recovery services may not be trustworthy. Therefore, I highly recommend Capital Crypto Recover to you. i do always research and see reviews about their service, For assistance finding your misplaced cryptocurrency, get in touch with them, They do their jobs quickly and excellently, Stay safe and vigilant in the crypto world. You can reach them via email at [email protected] OR Call/Text Number +1 (336)390-6684 his contact: [email protected] His website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 10.04.25 23:27 patricialovick86

    How To Recover Your Bitcoin Without Falling Victim To Scams: A  Testimony Experience With Capital Crypto Recover Services, Contact Telegram: @Capitalcryptorecover Dear Everyone, I would like to take a moment to share my positive experience with Capital Crypto Recover Services. Initially, I was unsure if it would be possible to recover my stolen bitcoins. However, with their expertise and professionalism, I was able to fully recover my funds. Unfortunately, many individuals fall victim to scams in the cryptocurrency space, especially those involving fraudulent investment platforms. However, I advise caution, as not all recovery services are legitimate. I personally lost $273,000 worth of Bitcoin from my Binance account due to a deceptive platform. If you have suffered a similar loss, you may be considering crypto recovery, The Capital Crypto Recover is the most knowledgeable and effective Capital Crypto Recovery Services assisted me in recovering my stolen funds within 24 hours, after getting access to my wallet. Their service was not only prompt but also highly professional and effective, and many recovery services may not be trustworthy. Therefore, I highly recommend Capital Crypto Recover to you. i do always research and see reviews about their service, For assistance finding your misplaced cryptocurrency, get in touch with them, They do their jobs quickly and excellently, Stay safe and vigilant in the crypto world. You can reach them via email at [email protected] OR Call/Text Number +1 (336)390-6684 his contact: [email protected] His website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 11.04.25 00:48 patricialovick86

    How To Recover Your Bitcoin Without Falling Victim To Scams: A  Testimony Experience With Capital Crypto Recover Services, Contact Telegram: @Capitalcryptorecover Dear Everyone, I would like to take a moment to share my positive experience with Capital Crypto Recover Services. Initially, I was unsure if it would be possible to recover my stolen bitcoins. However, with their expertise and professionalism, I was able to fully recover my funds. Unfortunately, many individuals fall victim to scams in the cryptocurrency space, especially those involving fraudulent investment platforms. However, I advise caution, as not all recovery services are legitimate. I personally lost $273,000 worth of Bitcoin from my Binance account due to a deceptive platform. If you have suffered a similar loss, you may be considering crypto recovery, The Capital Crypto Recover is the most knowledgeable and effective Capital Crypto Recovery Services assisted me in recovering my stolen funds within 24 hours, after getting access to my wallet. Their service was not only prompt but also highly professional and effective, and many recovery services may not be trustworthy. Therefore, I highly recommend Capital Crypto Recover to you. i do always research and see reviews about their service, For assistance finding your misplaced cryptocurrency, get in touch with them, They do their jobs quickly and excellently, Stay safe and vigilant in the crypto world. You can reach them via email at [email protected] OR Call/Text Number +1 (336)390-6684 his contact: [email protected] His website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 11.04.25 03:20 edwardoelliott6

    PROFESSIONAL MONEY RECOVERY AGENCY → FOLKWIN EXPERT RECOVERY. Hi, I’m sharing my ordeal today because I know many of you could be in the same situation, and I want to help you avoid the same mistake I made. A little over a month ago, I came across what seemed like an incredible deal for a movie streaming service. They promised all the latest movies, TV shows, and exclusive content for a very reasonable price. They were offering an annual subscription for just $39,000, which seemed like a great deal at the time when compared to some of the larger streaming services out there. I should’ve known something was off from the start. The website looked pretty legitimate, had professional graphics, and even customer reviews that were mostly positive. But there were no big, recognizable brand names behind it, and the service was claiming to have content from major studios, which raised a red flag I didn't fully process. Still, the deal was tempting, and after doing a quick search online (which, in hindsight, wasn’t thorough enough), I decided to go ahead and sign up. I paid the $39,000 upfront for the annual subscription, thinking that I was getting access to all the movies and shows I’d ever wanted. At first, everything seemed fine. I received an email confirming my subscription and even a receipt. But a few days later, when I went back to the site to browse content, I couldn’t get in. The site was down, and there was no way to contact anyone. I waited a few days, hoping it was just a technical issue. But then, I started doing some more research and realized that others had fallen victim to the same scam. The website had disappeared, and no one could find any trace of the company behind it. I was furious. I had just lost $39,000, and it seemed like there was no way to get it back. That's when I came across Folkwin Expert Recovery. They specialize in helping people recover funds from online scams like this. I was skeptical at first, but after reading reviews and seeing their success stories, I decided to give them a try. The team at Folkwin Expert Recovery was extremely professional. They asked for all the details about my transaction, including the payment method, and got to work right away. Within just a few days, I received updates from them, and eventually, they successfully recovered my $39,000. It felt like a huge weight was lifted off my shoulders. I honestly didn’t think it was possible to get my money back, but thanks to Folkwin Expert Recovery, I did. If you ever find yourself in a similar situation, I highly recommend reaching out to them. FOLKWINEXPERTRECOVERY(at)TECH-CENTER.C OM, TELEGRAM: @FOLKWIN_EXPERT_RECOVERY . They made the process simple and stress-free, and they delivered on their promises. Just remember to always be cautious when dealing with online subscriptions, especially if something feels too good to be true. Stay safe out there! Best Regards, Edward O. Elliott.

  • 11.04.25 08:10 Beatrice Gallagher

    I was defrauded of $78,000 by an individual I met online who was involved in a fraudulent investment endeavour. I initiated a search for legal assistance to retrieve my funds, and I encountered numerous testimonies regarding a criminal named RecoveryHacker101. I contacted them and provided the requisite information. The experts were able to locate and assist in the recovery of my misappropriated funds within approximately 36 hours. The fraudster was apprehended and apprehended by local authorities in his region, which is a source of immense relief for me. I trust that this information will be beneficial to the numerous individuals who have fallen victim to these fraudulent online investment scams. Their professional services are highly recommended for those in need of prompt and effective recovery assistance. If you require their services, you may only contact them via email at RecoveryHacker101[at]gmail[dot]com.

  • 13.04.25 01:13 michaeldavenport218

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  • 13.04.25 01:13 michaeldavenport218

    I was recently scammed out of $53,000 by a fraudulent Bitcoin investment scheme, which added significant stress to my already difficult health issues, as I was also facing cancer surgery expenses. Desperate to recover my funds, I spent hours researching and consulting other victims, which led me to discover the excellent reputation of Capital Crypto Recover, I came across a Google post It was only after spending many hours researching and asking other victims for advice that I discovered Capital Crypto Recovery’s stellar reputation. I decided to contact them because of their successful recovery record and encouraging client testimonials. I had no idea that this would be the pivotal moment in my fight against cryptocurrency theft. Thanks to their expert team, I was able to recover my lost cryptocurrency back. The process was intricate, but Capital Crypto Recovery's commitment to utilizing the latest technology ensured a successful outcome. I highly recommend their services to anyone who has fallen victim to cryptocurrency fraud. For assistance, contact [email protected] Capital Crypto Recover on Telegram OR Call Number +1 (336)390-6684 via email: [email protected]

  • 14.04.25 23:44 elizabethrush89

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  • 17.04.25 03:56 tylerkevin

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  • 18.04.25 20:39 michaeldavenport218

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  • 20.04.25 17:22 patricialovick86

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  • 21.04.25 03:51 gleerandon

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  • 21.04.25 12:19 michaeldavenport218

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  • 24.04.25 18:17 Mary Perez

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  • 25.04.25 15:44 Sharo2025

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  • 26.04.25 19:01 ashlyncarson

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  • 27.04.25 02:41 elizabethrush89

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  • 29.04.25 12:10 walterkeith2004

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  • 30.04.25 22:56 thomaslilley99

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  • 01.05.25 06:40 armand231101

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  • 01.05.25 14:46 kookersylvia81

    Through Telegram, I've finally had the opportunity to witness genuine professionalism with DuneNectarWebExpert. This experience has renewed my trust in people and strengthened my conviction in the significance of persistence and empathy. As a long-standing physician practicing in Atlanta, Georgia, I've treated numerous patients who have been victimized, their lives irreversibly altered by the damaging consequences of entrusting the wrong person with their private and financial details. One of my patients suggested that I seek help from DuneNectarWebExpert. From the instant I contacted ( Support (@) Dunenectarwebexpert (.) C0M ), I was met with comprehension, as they grasped the emotional distress caused by an online romance scam. Their professionalism, compassion, and commitment to rectifying the injustices suffered by scam victims, including myself and many others, were evident. Their team, comprised of cybersecurity professionals and digital investigation specialists, promptly evaluated the situation and developed a thorough plan to retrieve my lost funds. I would not be writing this epistle if I had not achieved the outcome I anticipated when I engaged DuneNectarWebExpert services. The process was challenging, as these fraudsters actively resisted efforts to recover my scammed crypto funds successfully. At the end of everything, it was all a success, and I am forever grateful to my patient and the team of DUNENECTARWEBEXPERT for their aid in my life and my family's. Please deal with DUNENECTARWEBEXPERT directly via their officials: https:// dunenectarwebexpert . com/ Telegram, DuneNectarWebExpert.

  • 01.05.25 20:59 elizabethrush89

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  • 03.05.25 05:01 melissaholroyd

    "The breakthrough came when they traced the stolen BTC to a lesser-known Pakistani exchange. By collaborating with Interpol and Pakistani authorities, they managed to freeze the exchange account that held the bulk of my stolen coins. Although 0.3 BTC had already been liquidated, 3.2 BTC ($128,000) was successfully recovered and returned to me within 12 days. As for the people behind the scam, the click farm’s operators are now facing fraud and money laundering charges. I’m incredibly grateful for the hard work of Tech Cyber Force Recovery. They didn’t just help me recover my funds, they sent a clear message that scams like this won’t go unpunished. It’s a reminder that while the crypto space can be risky, there are Tech Cyber Force recovery teams out there who will fight to bring justice. WhatsApp +1 561 726 36 97 telegram (@)Techcyberforc

  • 03.05.25 12:24 ratty clara

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  • 08.05.25 03:35 jwright70

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  • 13.05.25 19:18 canemarcus0

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  • 13.05.25 19:24 graceharrison09

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  • 13.05.25 20:21 wendytaylor015

    My name is Wendy Taylor, I'm from Los Angeles, i want to announce to you Viewer how Capital Crypto Recover help me to restore my Lost Bitcoin, I invested with a Crypto broker without proper research to know what I was hoarding my hard-earned money into scammers, i lost access to my crypto wallet or had your funds stolen? Don’t worry Capital Crypto Recover is here to help you recover your cryptocurrency with cutting-edge technical expertise, With years of experience in the crypto world, Capital Crypto Recover employs the best latest tools and ethical hacking techniques to help you recover lost assets, unlock hacked accounts, Whether it’s a forgotten password, Capital Crypto Recover has the expertise to help you get your crypto back. a security company service that has a 100% success rate in the recovery of crypto assets, i lost wallet and hacked accounts. I provided them the information they requested and they began their investigation. To my surprise, Capital Crypto Recover was able to trace and recover my crypto assets successfully within 24hours. Thank you for your service in helping me recover my $647,734 worth of crypto funds and I highly recommend their recovery services, they are reliable and a trusted company to any individuals looking to recover lost money. Contact email [email protected] OR Telegram @Capitalcryptorecover Call/Text Number +1 (336)390-6684 his contact: [email protected] His website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 13.05.25 20:21 wendytaylor015

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  • 14.05.25 14:45 Clarkanderson

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  • 14.05.25 14:45 Clarkanderson

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  • 14.05.25 14:45 Clarkanderson

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  • 16.05.25 14:18 patricialovick86

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  • 16.05.25 14:18 patricialovick86

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  • 17.05.25 18:33 marywilliam3544

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  • 19.05.25 05:42 kylebro

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  • 20.05.25 11:40 robertalfred175

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  • 20.05.25 11:40 robertalfred175

    CRYPTO SCAM RECOVERY SUCCESSFUL – A TESTIMONIAL OF LOST PASSWORD TO YOUR DIGITAL WALLET BACK. My name is Robert Alfred, Am from Australia. I’m sharing my experience in the hope that it helps others who have been victims of crypto scams. A few months ago, I fell victim to a fraudulent crypto investment scheme linked to a broker company. I had invested heavily during a time when Bitcoin prices were rising, thinking it was a good opportunity. Unfortunately, I was scammed out of $120,000 AUD and the broker denied me access to my digital wallet and assets. It was a devastating experience that caused many sleepless nights. Crypto scams are increasingly common and often involve fake trading platforms, phishing attacks, and misleading investment opportunities. In my desperation, a friend from the crypto community recommended Capital Crypto Recovery Service, known for helping victims recover lost or stolen funds. After doing some research and reading multiple positive reviews, I reached out to Capital Crypto Recovery. I provided all the necessary information—wallet addresses, transaction history, and communication logs. Their expert team responded immediately and began investigating. Using advanced blockchain tracking techniques, they were able to trace the stolen Dogecoin, identify the scammer’s wallet, and coordinate with relevant authorities to freeze the funds before they could be moved. Incredibly, within 24 hours, Capital Crypto Recovery successfully recovered the majority of my stolen crypto assets. I was beyond relieved and truly grateful. Their professionalism, transparency, and constant communication throughout the process gave me hope during a very difficult time. If you’ve been a victim of a crypto scam, I highly recommend them with full confidence contacting: Email: [email protected] Telegram: @Capitalcryptorecover Call/Text: +1 (336) 390-6684 Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 26.05.25 17:55 Juliuslaurence1

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  • 30.05.25 05:03 kevinpatmore2

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  • 01.06.25 17:55 springthorpecameron5

    After losing $153,000 to a fake investment platform on Telegram, I found myself under immense pressure due to both the financial loss and my ongoing cancer treatment. Desperate to recover my stolen cryptocurrency, I researched viable options and discovered Ruder Cyber Tech Sleuths, a registered Bitcoin and USDT Recovery Agency with a strong reputation. Their impressive record of successful recoveries and positive reviews gave me hope. Despite my initial doubts, reaching out to them was a pivotal moment in my recovery journey. The process was complex, but their dedication, advanced technology, and professionalism were evident. I highly recommend their services to anyone who has fallen victim to scams. Ruder Cyber Tech Sleuths are truly exceptional and provide hope in an often dishonest world. [email protected] [email protected] whatsapp: +12132801476 Telegram : @rudercybersleuths

  • 02.06.25 16:01 wendytaylor015

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  • 02.06.25 16:01 wendytaylor015

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  • 03.06.25 22:21 [email protected]

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  • 06.06.25 18:43 jack67667766

    Jasmine Lopez is an expert in retrieving stolen cryptocurrency, including Ethereum and USDT. Her effective methods make her a reliable ally for theft victims. One client, who lost €908,000, sought her assistance, and Jasmine managed to recover the full amount within a day, bringing immense relief to the client. Jasmine is dedicated to helping others in similar situations and is available to offer support. For help with recovering lost funds, you can contact her via email at [email protected] or on WhatsApp at ‪+44 736 644 5035‬.

  • 08.06.25 12:49 jack67667766

    Jasmine Lopez es una experta en recuperar criptomonedas robadas, como Ethereum y USDT. Ella usa métodos efectivos que la hacen una buena ayuda para quienes han sido víctimas de robo. Un cliente que perdió 908 mil euros acudió a ella, y en un día, Jasmine logró devolverle toda la cantidad. Esto trajo gran alivio al cliente. Ella tiene como objetivo ayudar a otros en situaciones similares y está lista para ofrecer apoyo. Si necesitas ayuda para recuperar fondos perdidos, puedes contactarla por correo electrónico en [email protected] o por WhatsApp al número ‪+44 736 644 5035‬.

  • 09.06.25 19:49 garnierroux

    CRYPTO RECOVERY COMPANY: CONTACT iBOLT CYBER HACKER RECOVERY COMPANY I was a victim of a cryptocurrency scam and thought I had lost everything. After doing some research, I came across iBOLT CYBER HACKER RECOVERY COMPANY and decided to give them a try. To my surprise, they were extremely professional, responsive, and knowledgeable from the start. Their team worked diligently to trace the stolen funds, and within a short time, they were able to recover a significant portion of my lost crypto. I couldn’t believe it — something I thought was gone forever was returned to me. If you've lost money to a crypto scam, I highly recommend contacting iBOLT CYBER HACKER RECOVERY COMPANY. They truly deliver on what they promise. — Satisfied Client . EMAIL: [email protected]/ . WHTSAPP: +39 351 105 3619 . TELEGRAM: t.me/iboltcyberhackservice . WEBSITE: https://iboltcyberhack.org/

  • 21.06.25 04:37 jamesgrant

    The emergence of cryptocurrencies has revolutionized the financial landscape, yet it has also given rise to significant challenges, particularly concerning security and the potential for loss of assets. In this context, individuals often seek assistance from entities called SCANNER HACKER CRYPTO RECOVERY, which purport to employ sophisticated techniques to reclaim lost Bitcoin (BTC) from compromised wallets or exchanges. However, while the allure of recovering lost assets can be compelling, it is imperative to approach these services with a critical lens. The cryptocurrency ecosystem is rife with scams and fraudulent schemes, ranging from phishing attacks to outright theft; thus, due diligence is paramount. Scholars and practitioners in the field must advocate for more robust security measures, educate stakeholders on the inherent risks of cryptocurrency ownership, and promote best practices in digital asset management to mitigate the likelihood of loss. Furthermore, effective policymaking to regulate these recovery services, hold malicious actors accountable, and protect consumers is essential to fostering a safer environment in the rapidly evolving world of digital currencies. You can communicate with SCANNER HACKER CRYPTO RECOVERY Via Email: [email protected] Web: https://scannerhacktech.com/ Whatsapp: +1 431 801 7493

  • 23.06.25 15:44 michaeldavenport218

    I was recently scammed out of $53,000 by a fraudulent Bitcoin investment scheme, which added significant stress to my already difficult health issues, as I was also facing cancer surgery expenses. Desperate to recover my funds, I spent hours researching and consulting other victims, which led me to discover the excellent reputation of Capital Crypto Recover, I came across a Google post It was only after spending many hours researching and asking other victims for advice that I discovered Capital Crypto Recovery’s stellar reputation. I decided to contact them because of their successful recovery record and encouraging client testimonials. I had no idea that this would be the pivotal moment in my fight against cryptocurrency theft. Thanks to their expert team, I was able to recover my lost cryptocurrency back. The process was intricate, but Capital Crypto Recovery's commitment to utilizing the latest technology ensured a successful outcome. I highly recommend their services to anyone who has fallen victim to cryptocurrency fraud. For assistance, contact [email protected] Capital Crypto Recover on Telegram OR Call Number +1 (336)390-6684 via email: [email protected] you can visit his website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 23.06.25 15:44 michaeldavenport218

    I was recently scammed out of $53,000 by a fraudulent Bitcoin investment scheme, which added significant stress to my already difficult health issues, as I was also facing cancer surgery expenses. Desperate to recover my funds, I spent hours researching and consulting other victims, which led me to discover the excellent reputation of Capital Crypto Recover, I came across a Google post It was only after spending many hours researching and asking other victims for advice that I discovered Capital Crypto Recovery’s stellar reputation. I decided to contact them because of their successful recovery record and encouraging client testimonials. I had no idea that this would be the pivotal moment in my fight against cryptocurrency theft. Thanks to their expert team, I was able to recover my lost cryptocurrency back. The process was intricate, but Capital Crypto Recovery's commitment to utilizing the latest technology ensured a successful outcome. I highly recommend their services to anyone who has fallen victim to cryptocurrency fraud. For assistance, contact [email protected] Capital Crypto Recover on Telegram OR Call Number +1 (336)390-6684 via email: [email protected] you can visit his website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 24.06.25 04:10 Beth Wilson

    CONTACT A LEGITIMATE CRYPTOCURRENCY RECOVERY SPECIALISTS // THE HACK ANGELS I'd like to take this opportunity to express my gratitude to A Legitimate Bitcoin Recovery Specialists THE HACK ANGELS. I wholeheartedly recommend THE HACK ANGELS to anyone dealing with lost or stolen cryptocurrency. They have deep understanding of blockchain technology, combined with advanced cyber forensic tools, makes them a powerful ally in asset recovery. THE HACK ANGELS is a trusted and reliable solution for individuals seeking to recover lost or inaccessible cryptocurrency assets, if you are experiencing similar troubles and you need help in recovering your money. I strongly advise you to contact them via details below. WhatsApp +1(520)200-2320 Email at [email protected] Website at www.thehackangels.com I am here to give glory to THE HACK ANGELS for assisting me in recovering my lost cryptocurrency.

  • 24.06.25 04:10 Beth Wilson

    CONTACT A LEGITIMATE CRYPTOCURRENCY RECOVERY SPECIALISTS // THE HACK ANGELS I'd like to take this opportunity to express my gratitude to A Legitimate Bitcoin Recovery Specialists THE HACK ANGELS. I wholeheartedly recommend THE HACK ANGELS to anyone dealing with lost or stolen cryptocurrency. They have deep understanding of blockchain technology, combined with advanced cyber forensic tools, makes them a powerful ally in asset recovery. THE HACK ANGELS is a trusted and reliable solution for individuals seeking to recover lost or inaccessible cryptocurrency assets, if you are experiencing similar troubles and you need help in recovering your money. I strongly advise you to contact them via details below. WhatsApp +1(520)200-2320 Email at [email protected] Website at www.thehackangels.com I am here to give glory to THE HACK ANGELS for assisting me in recovering my lost cryptocurrency.

  • 24.06.25 04:10 Beth Wilson

    CONTACT A LEGITIMATE CRYPTOCURRENCY RECOVERY SPECIALISTS // THE HACK ANGELS I'd like to take this opportunity to express my gratitude to A Legitimate Bitcoin Recovery Specialists THE HACK ANGELS. I wholeheartedly recommend THE HACK ANGELS to anyone dealing with lost or stolen cryptocurrency. They have deep understanding of blockchain technology, combined with advanced cyber forensic tools, makes them a powerful ally in asset recovery. THE HACK ANGELS is a trusted and reliable solution for individuals seeking to recover lost or inaccessible cryptocurrency assets, if you are experiencing similar troubles and you need help in recovering your money. I strongly advise you to contact them via details below. WhatsApp +1(520)200-2320 Email at [email protected] Website at www.thehackangels.com I am here to give glory to THE HACK ANGELS for assisting me in recovering my lost cryptocurrency.

  • 24.06.25 04:10 Beth Wilson

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  • 24.06.25 04:12 Beth Wilson

    CONTACT A LEGITIMATE CRYPTOCURRENCY RECOVERY SPECIALISTS // THE HACK ANGELS I'd like to take this opportunity to express my gratitude to A Legitimate Bitcoin Recovery Specialists THE HACK ANGELS. I wholeheartedly recommend THE HACK ANGELS to anyone dealing with lost or stolen cryptocurrency. They have deep understanding of blockchain technology, combined with advanced cyber forensic tools, makes them a powerful ally in asset recovery. THE HACK ANGELS is a trusted and reliable solution for individuals seeking to recover lost or inaccessible cryptocurrency assets, if you are experiencing similar troubles and you need help in recovering your money. I strongly advise you to contact them via details below. WhatsApp +1(520)200-2320 Email at [email protected] Website at www.thehackangels.com I am here to give glory to THE HACK ANGELS for assisting me in recovering my lost cryptocurrency.

  • 24.06.25 04:14 Beth Wilson

    CONTACT A LEGITIMATE CRYPTOCURRENCY RECOVERY SPECIALISTS // THE HACK ANGELS I'd like to take this opportunity to express my gratitude to A Legitimate Bitcoin Recovery Specialists THE HACK ANGELS. I wholeheartedly recommend THE HACK ANGELS to anyone dealing with lost or stolen cryptocurrency. They have deep understanding of blockchain technology, combined with advanced cyber forensic tools, makes them a powerful ally in asset recovery. THE HACK ANGELS is a trusted and reliable solution for individuals seeking to recover lost or inaccessible cryptocurrency assets, if you are experiencing similar troubles and you need help in recovering your money. I strongly advise you to contact them via details below. WhatsApp +1(520)200-2320 Email at [email protected] Website at www.thehackangels.com I am here to give glory to THE HACK ANGELS for assisting me in recovering my lost cryptocurrency.

  • 24.06.25 04:14 Beth Wilson

    CONTACT A LEGITIMATE CRYPTOCURRENCY RECOVERY SPECIALISTS // THE HACK ANGELS I'd like to take this opportunity to express my gratitude to A Legitimate Bitcoin Recovery Specialists THE HACK ANGELS. I wholeheartedly recommend THE HACK ANGELS to anyone dealing with lost or stolen cryptocurrency. They have deep understanding of blockchain technology, combined with advanced cyber forensic tools, makes them a powerful ally in asset recovery. THE HACK ANGELS is a trusted and reliable solution for individuals seeking to recover lost or inaccessible cryptocurrency assets, if you are experiencing similar troubles and you need help in recovering your money. I strongly advise you to contact them via details below. WhatsApp +1(520)200-2320 Email at [email protected] Website at www.thehackangels.com I am here to give glory to THE HACK ANGELS for assisting me in recovering my lost cryptocurrency.

  • 24.06.25 04:18 Beth Wilson

    CONTACT A LEGITIMATE CRYPTOCURRENCY RECOVERY SPECIALISTS // THE HACK ANGELS I'd like to take this opportunity to express my gratitude to A Legitimate Bitcoin Recovery Specialists THE HACK ANGELS. I wholeheartedly recommend THE HACK ANGELS to anyone dealing with lost or stolen cryptocurrency. They have deep understanding of blockchain technology, combined with advanced cyber forensic tools, makes them a powerful ally in asset recovery. THE HACK ANGELS is a trusted and reliable solution for individuals seeking to recover lost or inaccessible cryptocurrency assets, if you are experiencing similar troubles and you need help in recovering your money. I strongly advise you to contact them via details below. WhatsApp +1(520)200-2320 Email at [email protected] Website at www.thehackangels.com I am here to give glory to THE HACK ANGELS for assisting me in recovering my lost cryptocurrency.

  • 24.06.25 04:23 Beth Wilson

    CONTACT A LEGITIMATE CRYPTOCURRENCY RECOVERY SPECIALISTS // THE HACK ANGELS I'd like to take this opportunity to express my gratitude to A Legitimate Bitcoin Recovery Specialists THE HACK ANGELS. I wholeheartedly recommend THE HACK ANGELS to anyone dealing with lost or stolen cryptocurrency. They have deep understanding of blockchain technology, combined with advanced cyber forensic tools, makes them a powerful ally in asset recovery. THE HACK ANGELS is a trusted and reliable solution for individuals seeking to recover lost or inaccessible cryptocurrency assets, if you are experiencing similar troubles and you need help in recovering your money. I strongly advise you to contact them via details below. WhatsApp +1(520)200-2320 Email at [email protected] Website at www.thehackangels.com I am here to give glory to THE HACK ANGELS for assisting me in recovering my lost cryptocurrency.

  • 25.06.25 22:56 rennebrandon

    I am forever grateful to the amazing team at Dune Nectar Web Expert. They did what I thought was never possible. They played a crucial role in helping me recover all my lost funds from a fraudulent forex and crypto trading scheme, including the pråofits I thought I'd earned. Looking back, I realize I was a bit of a fool for trusting greedy and deceitful brokers with my hard-earned money. However, I'm overjoyed that I found Dune Nectar Web Expert. They are a team of honest and highly skilled professionals available for hire. Dune Nectar Web Expert helps individuals and organizations recover stolen cryptocurrencies and digital assets. They helped me heal every penny I lost and provided me with the right signals and a reliable platform to trade with. Thanks to Dune Nectar Web Expert, I'm earning more than ever, and I couldn't be happier. That's why I can't stop sharing my positive experience and praising their amazing services and expertise. If you're still struggling with failures in binary options, crypto, or forex trading, or if you're looking to recover your lost USDC, Bitcoin, Ethereum, or other crypto funds, I strongly advise you to reach out to: Telegram>>> ( T.me/dunenectarwebexpert ) Mail>>>> Support (@) Dunenectarwebexpert (.) Com Web>>> https://dunenectarwebexpert.com/

  • 26.06.25 20:33 paulmargaret

    Dear Wizard Hilton Cyber Tech Team, I am writing to express my heartfelt gratitude and appreciation for your exceptional service in recovering my scammed funds. After falling victim to a scam and experiencing numerous disappointments, I was amazed by your team's professionalism, expertise, and dedication to helping me retrieve my lost money. Your prompt response and efficient handling of my case were truly impressive. Within 24 hours, you successfully recovered my funds, and I was overjoyed to see the money safely returned to my account. Your team's commitment to helping individuals like me, who have been scammed, is truly commendable. Your work not only restored my financial security but also gave me peace of mind and renewed trust in the digital world. I would like to highly recommend your services to anyone who has been a victim of scams or cybercrime. Your expertise and support can make a significant difference in recovering lost funds and navigating complex digital situations. Thank you again for your outstanding work and dedication. I am grateful for your help and wish you continued success in your endeavors     Email : wizardhiltoncybertech ( @ ) gmail (. ) com OR support ( @ ) wizardhiltoncybertech (.) com WhatsApp number +18737715701 . Sincerely, Kimberly Williams

  • 27.06.25 00:46 paulmargaret

    Dear Wizard Hilton Cyber Tech Team, I am writing to express my heartfelt gratitude and appreciation for your exceptional service in recovering my scammed funds. After falling victim to a scam and experiencing numerous disappointments, I was amazed by your team's professionalism, expertise, and dedication to helping me retrieve my lost money. Your prompt response and efficient handling of my case were truly impressive. Within 24 hours, you successfully recovered my funds, and I was overjoyed to see the money safely returned to my account. Your team's commitment to helping individuals like me, who have been scammed, is truly commendable. Your work not only restored my financial security but also gave me peace of mind and renewed trust in the digital world. I would like to highly recommend your services to anyone who has been a victim of scams or cybercrime. Your expertise and support can make a significant difference in recovering lost funds and navigating complex digital situations. Thank you again for your outstanding work and dedication. I am grateful for your help and wish you continued success in your endeavors     Email : wizardhiltoncybertech ( @ ) gmail (. ) com OR support ( @ ) wizardhiltoncybertech (.) com WhatsApp number +18737715701 . Sincerely, Kimberly Williams

  • 27.06.25 22:31 rennebrandon

    I am forever grateful to the amazing team at Dune Nectar Web Expert. They did what I thought was never possible. They played a crucial role in helping me recover all my lost funds from a fraudulent forex and crypto trading scheme, including the pråofits I thought I'd earned. Looking back, I realize I was a bit of a fool for trusting greedy and deceitful brokers with my hard-earned money. However, I'm overjoyed that I found Dune Nectar Web Expert. They are a team of honest and highly skilled professionals available for hire. Dune Nectar Web Expert helps individuals and organizations recover stolen cryptocurrencies and digital assets. They helped me heal every penny I lost and provided me with the right signals and a reliable platform to trade with. Thanks to Dune Nectar Web Expert, I'm earning more than ever, and I couldn't be happier. That's why I can't stop sharing my positive experience and praising their amazing services and expertise. If you're still struggling with failures in binary options, crypto, or forex trading, or if you're looking to recover your lost USDC, Bitcoin, Ethereum, or other crypto funds, I strongly advise you to reach out to: Telegram>>> ( T.me/dunenectarwebexpert ) Mail>>>> Support (@) Dunenectarwebexpert (.) Com Web>>> https://dunenectarwebexpert.com/

  • 28.06.25 00:46 patricialovick86

    How To Recover Your Bitcoin Without Falling Victim To Scams: A  Testimony Experience With Capital Crypto Recover Services, Contact Telegram: @Capitalcryptorecover Dear Everyone, I would like to take a moment to share my positive experience with Capital Crypto Recover Services. Initially, I was unsure if it would be possible to recover my stolen bitcoins. However, with their expertise and professionalism, I was able to fully recover my funds. Unfortunately, many individuals fall victim to scams in the cryptocurrency space, especially those involving fraudulent investment platforms. However, I advise caution, as not all recovery services are legitimate. I personally lost $273,000 worth of Bitcoin from my Binance account due to a deceptive platform. If you have suffered a similar loss, you may be considering crypto recovery, The Capital Crypto Recover is the most knowledgeable and effective Capital Crypto Recovery Services assisted me in recovering my stolen funds within 24 hours, after getting access to my wallet. Their service was not only prompt but also highly professional and effective, and many recovery services may not be trustworthy. Therefore, I highly recommend Capital Crypto Recover to you. i do always research and see reviews about their service, For assistance finding your misplaced cryptocurrency, get in touch with them, They do their jobs quickly and excellently, Stay safe and vigilant in the crypto world. You can reach them via email at [email protected] OR Call/Text Number +1 (336)390-6684 his contact: [email protected] His website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 28.06.25 00:46 patricialovick86

    How To Recover Your Bitcoin Without Falling Victim To Scams: A  Testimony Experience With Capital Crypto Recover Services, Contact Telegram: @Capitalcryptorecover Dear Everyone, I would like to take a moment to share my positive experience with Capital Crypto Recover Services. Initially, I was unsure if it would be possible to recover my stolen bitcoins. However, with their expertise and professionalism, I was able to fully recover my funds. Unfortunately, many individuals fall victim to scams in the cryptocurrency space, especially those involving fraudulent investment platforms. However, I advise caution, as not all recovery services are legitimate. I personally lost $273,000 worth of Bitcoin from my Binance account due to a deceptive platform. If you have suffered a similar loss, you may be considering crypto recovery, The Capital Crypto Recover is the most knowledgeable and effective Capital Crypto Recovery Services assisted me in recovering my stolen funds within 24 hours, after getting access to my wallet. Their service was not only prompt but also highly professional and effective, and many recovery services may not be trustworthy. Therefore, I highly recommend Capital Crypto Recover to you. i do always research and see reviews about their service, For assistance finding your misplaced cryptocurrency, get in touch with them, They do their jobs quickly and excellently, Stay safe and vigilant in the crypto world. You can reach them via email at [email protected] OR Call/Text Number +1 (336)390-6684 his contact: [email protected] His website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 29.06.25 07:39 paulmargaret

    Dear Wizard Hilton Cyber Tech Team, I am writing to express my heartfelt gratitude and appreciation for your exceptional service in recovering my scammed funds. After falling victim to a scam and experiencing numerous disappointments, I was amazed by your team's professionalism, expertise, and dedication to helping me retrieve my lost money. Your prompt response and efficient handling of my case were truly impressive. Within 24 hours, you successfully recovered my funds, and I was overjoyed to see the money safely returned to my account. Your team's commitment to helping individuals like me, who have been scammed, is truly commendable. Your work not only restored my financial security but also gave me peace of mind and renewed trust in the digital world. I would like to highly recommend your services to anyone who has been a victim of scams or cybercrime. Your expertise and support can make a significant difference in recovering lost funds and navigating complex digital situations. Thank you again for your outstanding work and dedication. I am grateful for your help and wish you continued success in your endeavors     Email : wizardhiltoncybertech ( @ ) gmail (. ) com OR support ( @ ) wizardhiltoncybertech (.) com WhatsApp number +18737715701 . Sincerely, Kimberly Williams

  • 29.06.25 11:52 wendytaylor015

    My name is Wendy Taylor, I'm from Los Angeles, i want to announce to you Viewer how Capital Crypto Recover help me to restore my Lost Bitcoin, I invested with a Crypto broker without proper research to know what I was hoarding my hard-earned money into scammers, i lost access to my crypto wallet or had your funds stolen? Don’t worry Capital Crypto Recover is here to help you recover your cryptocurrency with cutting-edge technical expertise, With years of experience in the crypto world, Capital Crypto Recover employs the best latest tools and ethical hacking techniques to help you recover lost assets, unlock hacked accounts, Whether it’s a forgotten password, Capital Crypto Recover has the expertise to help you get your crypto back. a security company service that has a 100% success rate in the recovery of crypto assets, i lost wallet and hacked accounts. I provided them the information they requested and they began their investigation. To my surprise, Capital Crypto Recover was able to trace and recover my crypto assets successfully within 24hours. Thank you for your service in helping me recover my $647,734 worth of crypto funds and I highly recommend their recovery services, they are reliable and a trusted company to any individuals looking to recover lost money. Contact email [email protected] OR Telegram @Capitalcryptorecover Call/Text Number +1 (336)390-6684 his contact: [email protected] His website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 30.06.25 15:06 wendytaylor015

    My name is Wendy Taylor, I'm from Los Angeles, i want to announce to you Viewer how Capital Crypto Recover help me to restore my Lost Bitcoin, I invested with a Crypto broker without proper research to know what I was hoarding my hard-earned money into scammers, i lost access to my crypto wallet or had your funds stolen? Don’t worry Capital Crypto Recover is here to help you recover your cryptocurrency with cutting-edge technical expertise, With years of experience in the crypto world, Capital Crypto Recover employs the best latest tools and ethical hacking techniques to help you recover lost assets, unlock hacked accounts, Whether it’s a forgotten password, Capital Crypto Recover has the expertise to help you get your crypto back. a security company service that has a 100% success rate in the recovery of crypto assets, i lost wallet and hacked accounts. I provided them the information they requested and they began their investigation. To my surprise, Capital Crypto Recover was able to trace and recover my crypto assets successfully within 24hours. Thank you for your service in helping me recover my $647,734 worth of crypto funds and I highly recommend their recovery services, they are reliable and a trusted company to any individuals looking to recover lost money. Contact email [email protected] OR Telegram @Capitalcryptorecover Call/Text Number +1 (336)390-6684 his contact: [email protected] His website: https://recovercapital.wixsite.com/capital-crypto-rec-1

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