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[Перевод] Эволюция архитектур больших языковых моделей: от GPT-2 к современным решениям

Прошло семь лет с момента разработки оригинальной архитектуры GPT. На первый взгляд, если оглянуться на GPT-2 (2019) и взглянуть вперёд на DeepSeek-V3 и Llama 4 (2024–2025), можно удивиться, насколько эти модели по-прежнему структурно схожи.

Разумеется, позиционные эмбеддинги эволюционировали от абсолютных к роторационным (RoPE), Multi-Head Attention в значительной степени уступил место Grouped-Query Attention, а более эффективная SwiGLU заменила такие функции активации, как GELU. Но если отбросить эти незначительные усовершенствования, действительно ли мы наблюдаем принципиальные архитектурные сдвиги — или просто продолжаем полировать одни и те же фундаментальные конструкции?

Сравнение LLM между собой с целью выявления ключевых факторов, влияющих на их качество (или недостатки), по-прежнему остаётся крайне нетривиальной задачей: датасеты, методы обучения и гиперпараметры сильно различаются и зачастую плохо документированы.

Тем не менее, я считаю, что изучение именно архитектурных изменений остаётся ценным подходом, позволяющим понять, над чем работают разработчики LLM в 2025 году. (Некоторые из этих архитектур показаны на Рисунке 1 ниже.)

Рисунок 1: Подмножество архитектур, рассмотренных в этой статье.

Итак, в этой статье я не буду рассуждать о результатах бенчмарков или алгоритмах обучения — вместо этого сосредоточусь на архитектурных нововведениях, которые определяют современные флагманские open-source модели.

1. DeepSeek V3/R1

Как вы, вероятно, уже не раз слышали, модель DeepSeek R1 произвела серьёзный эффект при релизе в январе 2025 года. DeepSeek R1 — это reasoning-модель, построенная на архитектуре DeepSeek V3, представленной в декабре 2024 года.

Хотя основное внимание в этом материале уделено архитектурам, выпущенным в 2025 году, считаю уместным включить сюда и DeepSeek V3, поскольку широкое внимание и активное принятие она получила только после запуска DeepSeek R1.

В этом разделе я сосредоточусь на двух ключевых архитектурных техниках, представленных в DeepSeek V3, которые повысили вычислительную эффективность и отличают её от большинства других LLM:

Multi-Head Latent Attention (MLA)
Mixture-of-Experts (MoE)

1.1 Multi-Head Latent Attention (MLA)

Прежде чем перейти к Multi-Head Latent Attention (MLA), кратко напомню контекст, объясняющий, зачем вообще возникла эта техника. Начнём с Grouped-Query Attention (GQA) — это замена Multi-Head Attention (MHA), ставшая в последние годы новым стандартом благодаря большей вычислительной и параметрической эффективности.

Кратко о GQA: в отличие от классического MHA, где у каждой attention head свой набор ключей и значений, в GQA несколько head'ов делят одни и те же проекции ключей и значений для снижения потребления памяти.

Например, как показано на рисунке 2 ниже: при наличии двух KV-групп и четырёх attention head'ов, головы 1 и 2 могут использовать одну общую пару {K, V}, а головы 3 и 4 — другую. Это снижает общее число операций над ключами и значениями, уменьшает использование памяти и повышает эффективность (без заметного ущерба для качества моделирования, что подтверждается абляционными экспериментами).

Рисунок 2: Сравнение между MHA и GQA. Здесь размер группы составляет 2 — одна пара ключ/значение разделяется между двумя запросами.

Идея GQA сводится к уменьшению количества K/V-пар путём их шаринга между query head'ами. Это:

Снижает общее количество параметров модели;
Уменьшает нагрузку на память при инференсе, так как в KV-кэше нужно хранить и считывать меньшее число K и V тензоров.

Хотя GQA — это прежде всего оптимизация на уровне эффективности, абляционные исследования (например, в оригинальной статье про GQA и в статье Llama 2) показывают, что по качеству моделирования она сопоставима с классическим MHA.

Теперь — к Multi-Head Latent Attention (MLA). Это другая стратегия сокращения памяти, особенно эффективно работающая с KV-кешированием. В отличие от GQA, которая шарит K/V между head'ами, MLA сжимает тензоры ключей и значений в пространство меньшей размерности перед тем, как они будут сохранены в KV-кэш.

Во время инференса эти сжатые тензоры обратно проецируются в исходное пространство перед использованием — как показано на рисунке 3 ниже. Это добавляет одну дополнительную матричную операцию, но позволяет существенно снизить объём используемой памяти.

Рисунок 3: Сравнение между MLA (используется в DeepSeek V3 и R1) и обычным MHA.

(К слову, query-компоненты также подвергаются сжатию, но только во время обучения, а не во время инференса.)

Кстати, MLA появилась не впервые в DeepSeek-V3 — эта архитектура использовалась (и даже была представлена) ещё в DeepSeek-V2. Кроме того, в статье по V2 приведены интересные результаты абляционных экспериментов, которые могут объяснить, почему команда DeepSeek отдала предпочтение MLA, а не GQA (см. рисунок 4 ниже).

Рисунок 4: Таблицы из статьи DeepSeek-V2, https://arxiv.org/abs/2405.04434

Как показано на рисунке 4 выше, GQA демонстрирует более низкие результаты по сравнению с MHA, в то время как MLA обеспечивает лучшее качество моделирования, чем MHA — вероятно, именно поэтому команда DeepSeek выбрала MLA вместо GQA.

Резюмируя: MLA — это изящный приём, позволяющий сократить объём KV-кэша, при этом немного превосходя MHA по качеству моделирования.

1.2 Mixture-of-Experts (MoE)

Другим важным архитектурным компонентом в DeepSeek, заслуживающим внимания, является использование слоёв Mixture-of-Experts (MoE). Хотя MoE и не является изобретением DeepSeek, в этом году она вновь набрала популярность, и многие архитектуры, которые будут рассмотрены далее, также её используют.

Скорее всего, ты уже знаком с концепцией MoE, но короткое напоминание будет уместно.

Базовая идея MoE заключается в том, чтобы заменить каждый FeedForward-модуль в блоке трансформера на несколько expert-слоёв, каждый из которых по сути является таким же FeedForward-модулем. То есть вместо одного FeedForward-блока мы подставляем несколько, как показано на рисунке 5 ниже.

Рисунок 5: Иллюстрация модуля Mixture-of-Experts (MoE) в DeepSeek V3/R1 (справа) по сравнению с LLM, использующей стандартный FeedForward-блок (слева).

FeedForward-блок внутри трансформера (на рисунке выше он показан тёмно-серым цветом) содержит значительную часть всех параметров модели. (Стоит отметить, что блок трансформера, а вместе с ним и FeedForward-блок, повторяются многократно в LLM; в случае DeepSeek-V3 — 61 раз.)

Таким образом, замена одного FeedForward-блока на несколько (в MoE-конфигурации) существенно увеличивает общее число параметров модели. Однако ключевой трюк заключается в том, что не все эксперты используются ("активируются") для каждого токена. Вместо этого маршрутизатор выбирает лишь небольшое подмножество экспертов для каждого токена.

Поскольку активны только некоторые эксперты, MoE-модули часто называют разрежёнными (sparse), в отличие от плотных (dense) модулей, в которых участвуют все параметры. При этом большое общее количество параметров в MoE повышает ёмкость модели, то есть её способность усваивать больше знаний в процессе обучения. Разреженность же позволяет сохранить эффективность на этапе инференса, так как одновременно задействуется лишь небольшая часть параметров.

Например, в DeepSeek-V3 используется 256 экспертов на MoE-модуль, а общее количество параметров достигает 671 миллиарда. Но во время инференса одновременно активны только 9 экспертов (1 общий и 8, выбранных маршрутизатором). Это означает, что в расчёте на один шаг инференса задействуется лишь 37 миллиардов параметров — вместо всех 671.

Одной из примечательных особенностей MoE-дизайна в DeepSeek-V3 является использование shared expert — эксперта, который всегда активен для любого токена. Эта идея не нова: она была представлена в статьях DeepSeek 2024 MoE и ещё раньше — в DeepSpeedMoE (2022).

Рисунок 6: Иллюстрация из статьи "DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models", https://arxiv.org/abs/2401.06066

Преимущества наличия общего эксперта были впервые отмечены именно в DeepSpeedMoE, где показано, что это улучшает общее качество моделирования по сравнению с конфигурациями без shared expert. Вероятная причина — в том, что общие или повторяющиеся паттерны не нужно учить каждому индивидуальному эксперту отдельно, что оставляет больше места для обучения специализированных шаблонов.

1.3 Краткий итог по DeepSeek

Подводя итог: DeepSeek-V3 — это огромная модель с 671 миллиардом параметров, которая на момент запуска опережала все другие open-weight модели, включая Llama 3 на 405B. Несмотря на больший размер, модель гораздо эффективнее на этапе инференса за счёт архитектуры Mixture-of-Experts (MoE), где одновременно активируются только 37 миллиардов параметров на токен.

Другим ключевым отличием DeepSeek-V3 является использование Multi-Head Latent Attention (MLA) вместо Grouped-Query Attention (GQA). Обе схемы — это более эффективные альтернативы классической Multi-Head Attention (MHA) при использовании KV-кэша. Несмотря на большую сложность реализации MLA, исследования в статье по DeepSeek-V2 показывают, что она обеспечивает более высокое качество моделирования по сравнению с GQA.

2. OLMo 2

Серия моделей OLMo, разработанная некоммерческим Институтом искусственного интеллекта Аллена (Allen Institute for AI), заслуживает внимания благодаря своей прозрачности в отношении обучающих данных и кода, а также достаточно подробной технической документации.

Хотя вы вряд ли увидите OLMo в топах бенчмарков или лидербордов, модели из этой серии довольно «чистые» и, что более важно, служат отличным референсом для разработки LLM благодаря своей открытости.

Несмотря на то, что популярность OLMo 2 в первую очередь связана с прозрачностью, с точки зрения производительности они тоже вполне конкурентоспособны. На момент релиза в январе (до появления Llama 4, Gemma 3 и Qwen 3) модели OLMo 2 находились на границе Парето по соотношению вычислительных затрат и качества, как показано на рисунке 7 ниже.

Рисунок 7: Сравнение производительности на моделирующих бенчмарках (чем выше, тем лучше) и стоимости преподготовки (FLOPs; чем ниже, тем лучше) для различных LLM. Это иллюстрация из статьи OLMo 2: https://arxiv.org/abs/2501.00656

Как уже упоминалось ранее, в рамках этой статьи я сосредотачиваюсь только на архитектурных особенностях LLM (без углубления в данные и обучение), чтобы сохранить объём в разумных пределах. Так что же интересного в архитектуре OLMo 2? В основном — это нормализации: размещение слоёв RMSNorm, а также добавление QK-norm, о чём будет сказано ниже.

Также стоит отметить, что OLMo 2 по-прежнему использует классический Multi-Head Attention (MHA), а не MLA или GQA.

2.1 Размещение слоёв нормализации

В целом, OLMo 2 в значительной степени наследует архитектуру оригинальной модели GPT, как и большинство современных LLM. Однако есть несколько важных отличий. Начнём со слоёв нормализации.

Подобно Llama, Gemma и большинству других LLM, OLMo 2 перешёл от LayerNorm к RMSNorm.

Но поскольку RMSNorm — это уже пройденный этап (по сути, упрощённая версия LayerNorm с меньшим числом обучаемых параметров), я не буду подробно разбирать различия между RMSNorm и LayerNorm.

Тем не менее, стоит обсудить именно размещение слоя RMSNorm. В оригинальном трансформере (в статье “Attention is all you need”) два слоя нормализации располагались внутри трансформерного блока — после модуля внимания и после FeedForward-модуля соответственно.

Такое размещение известно как Post-LN или Post-Norm.

GPT и большинство последующих LLM стали размещать слои нормализации до модулей внимания и FeedForward, что получило название Pre-LN или Pre-Norm. Сравнение Post- и Pre-Norm представлено на схеме ниже.

Рисунок 8: Сравнение Post-Norm, Pre-Norm и варианта Post-Norm из OLMo 2.

В 2020 году Xiong и соавт. показали, что Pre-LN даёт более устойчивые градиенты на инициализации. Также было отмечено, что Pre-LN хорошо работает даже без тщательной настройки learning rate warm-up, которая обычно критична для Post-LN.

OLMo 2 же применяет разновидность Post-LN (с RMSNorm вместо LayerNorm, поэтому я называю это просто Post-Norm).

В OLMo 2 нормализация располагается после attention и FeedForward, как показано на рисунке ниже. Но при этом, в отличие от оригинального трансформера, нормализация остаётся внутри резидуальных связей (skip connections).

Зачем понадобилось перемещать нормализацию? Ответ — для улучшения стабильности обучения, как показано на графике ниже.

Рисунок 9: График, демонстрирующий устойчивость обучения для Pre-Norm (как в GPT-2, Llama 3 и многих других) по сравнению с версией Post-Norm из OLMo 2. Иллюстрация из статьи OLMo 2: https://arxiv.org/abs/2501.00656

К сожалению, на графике приведён эффект одновременно от перестановки нормализации и применения QK-Norm, так что невозможно точно оценить вклад каждой из техник по отдельности.

2.2 QK-Norm

Поскольку в предыдущем разделе уже упоминался QK-Norm, а в других моделях, о которых пойдёт речь позже (например, Gemma 2 и 3), он тоже используется, кратко объясню, что это.

QK-Norm — это ещё один слой RMSNorm. Он вставляется внутрь модуля Multi-Head Attention и применяется к векторам queries (q) и keys (k) до применения позиционного кодирования RoPE. Ниже приведён фрагмент реализации слоя Grouped-Query Attention (GQA), написанный для реализации Qwen 3 с нуля — применение QK-Norm в GQA аналогично его использованию в MHA у OLMo:

class GroupedQueryAttention(nn.Module):
    def __init__(
        self, d_in, num_heads, num_kv_groups,
        head_dim=None, qk_norm=False, dtype=None
    ):
        # ...

        if qk_norm:
            self.q_norm = RMSNorm(head_dim, eps=1e-6)
            self.k_norm = RMSNorm(head_dim, eps=1e-6)
        else:
            self.q_norm = self.k_norm = None

    def forward(self, x, mask, cos, sin):
        b, num_tokens, _ = x.shape

        # Apply projections
        queries = self.W_query(x) 
        keys = self.W_key(x)
        values = self.W_value(x) 

        # ...

        # Optional normalization
        if self.q_norm:
            queries = self.q_norm(queries)
        if self.k_norm:
            keys = self.k_norm(keys)

        # Apply RoPE
        queries = apply_rope(queries, cos, sin)
        keys = apply_rope(keys, cos, sin)

        # Expand K and V to match number of heads
        keys = keys.repeat_interleave(self.group_size, dim=1)
        values = values.repeat_interleave(self.group_size, dim=1)

        # Attention
        attn_scores = queries @ keys.transpose(2, 3)
        # ...

Как уже говорилось, вместе с Post-Norm, QK-Norm улучшает стабильность обучения. Отмечу, что QK-Norm был придуман не в OLMo 2 — он появился ещё в статье Scaling Vision Transformers в 2023 году.

2.3 Краткий итог по OLMo 2

Если кратко, ключевые архитектурные особенности OLMo 2 сводятся к следующему:

использование RMSNorm вместо LayerNorm;
размещение RMSNorm после, а не до attention и FeedForward (вариант Post-Norm);
добавление RMSNorm к queries и keys внутри attention-механизма (QK-Norm).

Обе эти модификации в совокупности улучшают стабильность функции потерь на обучении.

Ниже приведена сравнительная схема OLMo 2 и Llama 3 — как видно, архитектуры во многом схожи, за исключением того, что OLMo 2 продолжает использовать традиционный MHA, а не GQA. (Хотя команда OLMo выпустила версию на 32B спустя 3 месяца, в которой уже используется GQA.)

Рисунок 10: Сравнение архитектуры Llama 3 и OLMo 2.

3. Gemma 3

Модели Gemma от Google всегда были весьма сильными, но, на мой взгляд, традиционно недооценёнными по сравнению с более «громкими» моделями, такими как серия LLaMA.

Одной из отличительных черт Gemma является довольно крупный словарь — это сделано для лучшей поддержки мультиязычности. Кроме того, у моделей акцент именно на размер 27B (в отличие от 8B или 70B). При этом стоит отметить, что Gemma 2 также доступна в меньших конфигурациях: 1B, 4B и 12B.

27B — это действительно удачный компромисс: модель заметно мощнее 8B, но при этом не требует таких ресурсов, как 70B.

Что ещё интересного в Gemma 3? Как мы уже обсуждали ранее, модели вроде DeepSeek-V3/R1 используют архитектуру Mixture-of-Experts (MoE) для снижения требований к памяти при инференсе при фиксированном размере модели. (Подход MoE используется и в других моделях, о которых мы поговорим позже.)

Gemma 3 применяет иной «трюк» для сокращения вычислительных затрат — sliding window attention.

3.1 Sliding Window Attention

Благодаря механизму sliding window attention (впервые представленному в работе LongFormer в 2020 году и уже применявшемуся в Gemma 2), команде Gemma 3 удалось существенно сократить требования к памяти в KV-кэше — как показано на схеме ниже.

Рисунок 11: Иллюстрация из статьи о Gemma 3 (https://arxiv.org/abs/2503.19786), показывающая экономию памяти KV-кэша благодаря механизму sliding window attention.

Так что же такое sliding window attention? Если стандартный self-attention можно рассматривать как глобальный механизм внимания (каждый токен может взаимодействовать со всеми остальными), то sliding window attention — это локальное внимание, поскольку здесь область контекста ограничена определённым окном вокруг текущей query-позиции. Это показано на иллюстрации ниже.

Рисунок 12: Сравнение между обычным attention (слева) и sliding window attention (справа).

Важно: sliding window attention можно использовать как с Multi-Head Attention, так и с Grouped-Query Attention (GQA); в Gemma 3 используется GQA.

Как упомянуто выше, sliding window attention также называют локальным вниманием, потому что окно ограничено и «скользит» вместе с позицией query. В отличие от этого, обычное self-attention глобально — каждый токен может взаимодействовать со всеми другими токенами.

Следует также отметить, что архитектура Gemma 2 тоже использовала sliding window attention. Разница в Gemma 3 — в изменении соотношения между глобальным и локальным вниманием.

Например, в Gemma 2 применялась гибридная схема: sliding window (локальное) внимание и глобальное внимание использовались в соотношении 1:1. Каждый токен имел доступ к контексту в окне шириной 4k токенов.

В то время как Gemma 2 использовала локальное внимание через один слой, в Gemma 3 это соотношение стало 5:1 — то есть, на каждые 5 слоёв с sliding window attention приходится всего 1 слой с глобальным вниманием. Более того, размер окна был уменьшен с 4096 (в Gemma 2) до 1024 токенов (в Gemma 3). Это делает модель более эффективной и смещает акцент в сторону локализованных вычислений.

Согласно их ablation-исследованию, использование sliding window attention практически не влияет на качество моделирования, что видно из графика ниже.

Рисунок 13: Иллюстрация из статьи о Gemma 3 (https://arxiv.org/abs/2503.19786), демонстрирующая, что sliding window attention практически не влияет на перплексию, генерируемую LLM.

Хотя sliding window attention — наиболее заметная архитектурная особенность Gemma 3, я также кратко затрону размещение слоёв нормализации, как продолжение секции про OLMo 2.

3.2 Размещение слоев нормализации в Gemma 3

Небольшой, но интересный момент, заслуживающий внимания: в Gemma 3 используется RMSNorm как в Pre-Norm, так и в Post-Norm конфигурации вокруг модуля grouped-query attention.

Это схоже с тем, что было реализовано в Gemma 2, но всё же стоит отметить, поскольку отличается от:

Post-Norm-схемы, применённой в оригинальном Transformer (“Attention is all you need”),
Pre-Norm, ставшего популярным с GPT-2 и используемого во многих последующих архитектурах,
Post-Norm-варианта в OLMo 2, который мы уже рассматривали ранее.

Рисунок 14: Сравнение архитектур OLMo2 и Gemma 3; обратите внимание на дополнительные слои нормализации в Gemma 3.

Считаю такой подход к размещению слоя нормализации достаточно интуитивным: он позволяет получить преимущества как Pre-Norm, так и Post-Norm. На мой взгляд, немного дополнительной нормализации — это не проблема. В худшем случае, если она окажется избыточной, мы просто получим небольшую неэффективность из-за дублирования. На практике же, учитывая относительную «дешевизну» RMSNorm с точки зрения вычислений, это не должно существенно повлиять на производительность.

3.3 Сводка по Gemma 3

Gemma 3 — это хорошо сбалансированная LLM, которая, как мне кажется, недооценена в open-source-среде. Самая интересная особенность — использование sliding window attention для повышения эффективности (будет любопытно посмотреть на её комбинацию с MoE в будущем).

Также у Gemma 3 уникальное размещение слоёв нормализации: RMSNorm применяется как до, так и после модулей attention и FeedForward.

3.4 Бонус: Gemma 3n

Через несколько месяцев после релиза Gemma 3 компания Google представила Gemma 3n — модификацию модели Gemma 3, оптимизированную под ограниченные вычислительные ресурсы с целью запуска на мобильных устройствах.

Одна из ключевых оптимизаций в Gemma 3n — использование слоя с параметрами Per-Layer Embedding (PLE). Основная идея здесь заключается в том, чтобы держать только подмножество параметров модели в памяти GPU. Эмбеддинги, специфичные для токенов и модальностей (текст, аудио, визуальные данные), подгружаются по мере необходимости из CPU или SSD.

На диаграмме ниже показана экономия памяти благодаря PLE: указывается 5.44 миллиарда параметров для стандартной версии Gemma 3. Вероятно, речь идёт о варианте Gemma 3 с 4 миллиардами параметров.

Рисунок 15: Иллюстрация из блога Google о Gemma 3n (https://developers.googleblog.com/en/introducing-gemma-3n/), демонстрирующая экономию памяти благодаря PLE.

Несоответствие между 5.44 и 4 миллиардами объясняется тем, что Google использует нестандартный способ подсчёта параметров в LLM: эмбеддинги часто исключаются из общего числа параметров, чтобы модель казалась «меньше» — за исключением случаев, когда выгоднее указать большую цифру. Такая практика характерна не только для Google, но и стала общераспространённой в индустрии.

Ещё один интересный трюк — концепция MatFormer (сокращение от Matryoshka Transformer). Например, в Gemma 3n используется одна общая трансформер-архитектура, которую можно разрезать на более мелкие, автономно работающие части. Каждая такая «срезка» обучается выполнять задачи самостоятельно, так что на этапе инференса можно использовать только ту часть модели, которая реально нужна, без необходимости загружать всю архитектуру целиком.

4. Mistral Small 3.1

Модель Mistral Small 3.1 с 24 миллиардами параметров, выпущенная в марте вскоре после релиза Gemma 3, примечательна тем, что превзошла Gemma 3 27B по ряду бенчмарков (за исключением задач по математике), при этом обеспечивая более высокую скорость.

Низкая задержка инференса у Mistral Small 3.1 по сравнению с Gemma 3, вероятно, объясняется использованием кастомного токенизатора, а также сокращением объёма KV-кэша и количества слоёв. В остальном архитектура довольно стандартна, как показано на рисунке ниже.

Рисунок 16: Сравнение архитектур Gemma 3 27B и Mistral 3.1 Small 24B.

Интересно, что более ранние версии моделей Mistral использовали sliding window attention, но от этой механики, похоже, отказались в Mistral Small 3.1. Таким образом, поскольку Mistral применяет обычный Grouped-Query Attention, а не Grouped-Query Attention с использованием скользящего окна, как в Gemma 3, возможно, появляются дополнительные преимущества в производительности за счёт возможности задействовать более оптимизированный код (например, FlashAttention). Я предполагаю, что хотя sliding window attention снижает потребление памяти, это не обязательно ведёт к снижению задержки инференса — а именно на этом сделан фокус в Mistral Small 3.1.

5. Llama 4

Развёрнутое обсуждение архитектуры Mixture-of-Experts (MoE), приведённое ранее в статье, вновь оказывается полезным — Llama 4 также реализует MoE-подход, при этом в остальном она использует довольно стандартную архитектуру, во многом схожую с DeepSeek-V3, как показано на схеме ниже. (Llama 4 изначально поддерживает мультимодальность — как и Gemma или Mistral. Однако в данной статье акцент сделан на языковом моделировании, поэтому рассматривается только текстовая часть модели.)

Рисунок 17: Сравнение архитектур DeepSeek V3 (671 миллиард параметров) и Llama 4 Maverick (400 миллиард параметров).

Хотя архитектура Llama 4 Maverick визуально очень похожа на DeepSeek-V3, между ними есть ряд примечательных различий.

Во-первых, Llama 4, как и её предшественники, использует Grouped-Query Attention, тогда как в DeepSeek-V3 реализован Multi-Head Latent Attention — мы подробно разбирали его в начале статьи. Обе модели относятся к крупным архитектурам, при этом DeepSeek-V3 примерно на 68% превосходит Llama 4 по общему количеству параметров. Однако по числу активных параметров перевес ещё более значителен: DeepSeek-V3 использует 37 миллиардов активных параметров против 17 миллиардов у Llama 4 Maverick.

В Llama 4 Maverick реализована более классическая конфигурация MoE — меньшее число, но более «тяжёлые» эксперты (2 активных эксперта с размерностью скрытого слоя 8192), в то время как в DeepSeek-V3 задействовано 9 активных экспертов с размерностью скрытого слоя по 2048. Кроме того, DeepSeek вставляет MoE-слои в каждый transformer-блок (за исключением первых трёх), тогда как Llama 4 чередует MoE и плотные (dense) модули в каждом втором блоке.

Учитывая множество мелких архитектурных отличий, трудно однозначно оценить их вклад в итоговую производительность модели. Тем не менее, основной вывод таков: архитектуры на базе MoE переживают стремительный рост популярности в 2025 году.

6. Qwen3

Qwen3 — очередная успешная серия, лидирующая в своей размерной категории. В линейке представлено 7 "плотных" (dense) моделей: 0.6B, 1.7B, 4B, 8B, 14B и 32B. И 2 модели с архитектурой Mixture-of-Experts: 30B-A3B и 235B-A22B.

(К слову, отсутствие пробела в “Qwen3” — не опечатка; я намеренно сохраняю оригинальное написание, выбранное разработчиками Qwen.)

6.1 Qwen3 (плотная архитектура)

Начнём с плотных моделей. На момент написания статьи модель 0.6B, возможно, является самой компактной среди современных open-weight LLM. И, по моему личному опыту, она показывает отличные результаты для своего размера. У неё высокая пропускная способность (tokens/sec) и небольшой объём используемой памяти — если вы планируете запускать её локально. Более того, благодаря малому числу параметров её удобно обучать локально (в образовательных целях).

Для большинства задач у меня Qwen3 0.6B уже вытеснила Llama 3 1B. Ниже приведено сравнение этих двух архитектур.

Рисунок 18: Сравнение архитектур Qwen3 0.6B и Llama 3 1B; обратите внимание, что Qwen3 — более глубокая архитектура с большим количеством слоёв, тогда как Llama 3 — более широкая, с большим числом attention-глав.

Представленные выше показатели вычислительной производительности получены с моей реализации на PyTorch при запуске на GPU A100. Как видно, Qwen3 имеет меньший объём памяти за счёт компактной архитектуры: используются более узкие скрытые слои и меньшее число attention-head'ов. Однако при этом в модели больше трансформер-блоков, чем в Llama 3, что приводит к более медленной генерации (ниже скорость в токенах в секунду).

6.2 Qwen3 (MoE)

Как уже упоминалось, Qwen3 также выпускается в двух MoE-вариантах: 30B-A3B и 235B-A22B. Почему одни архитектуры, вроде Qwen3, имеют как обычные (dense), так и MoE (sparse) версии?

Как отмечалось в начале статьи, MoE-варианты позволяют снизить расходы на инференс для крупных базовых моделей. Предоставление и dense-, и MoE-версий даёт пользователям гибкость в зависимости от их целей и ограничений.

Dense-модели, как правило, проще в fine-tuning’е, деплое и оптимизации под различное оборудование.

С другой стороны, MoE-модели оптимизированы под масштабируемый инференс. Например, при фиксированном бюджете на инференс они обеспечивают большую общую модельную ёмкость (то есть, способность поглощать знания на этапе обучения, за счёт увеличения размеров модели), при этом без пропорционального роста затрат на инференс.

Благодаря выпуску обеих версий, серия Qwen3 охватывает широкий спектр сценариев использования: dense-модели — для устойчивости, простоты и fine-tuning’а, а MoE-модели — для эффективного масштабируемого сервинга.

Чтобы завершить этот раздел, давайте сравним Qwen3 235B-A22B (обратите внимание, что A22B означает «22B активных параметров») с DeepSeek-V3, у которой почти вдвое больше активных параметров — 37B.

Рисунок 19: Сравнение архитектур DeepSeek-V3 и Qwen3 235B-A22B.

Как видно на графике выше, архитектуры DeepSeek-V3 и Qwen3 235B-A22B крайне схожи. Однако важно отметить, что Qwen3 отказались от использования shared expert — в то время как более ранние модели, такие как Qwen2.5-MoE, использовали общего эксперта.

К сожалению, команда Qwen3 не раскрыла причины, по которым они отказались от shared expert. Если предположить, возможно, это оказалось ненужным для обеспечения стабильности обучения при увеличении числа экспертов с 2 (в Qwen2.5-MoE) до 8 (в Qwen3). Таким образом, они смогли сэкономить ресурсы (вычисления/память), задействовав только 8, а не 8+1 экспертов. (Хотя это не объясняет, почему DeepSeek-V3 до сих пор использует shared expert.)

Обновление. Junyang Lin, один из разработчиков Qwen3, прокомментировал это следующим образом:

«В тот момент мы не увидели достаточно заметного прироста от использования shared expert и нас беспокоили возможные сложности с оптимизацией инференса, которые он может вызвать. Если честно, однозначного ответа на этот вопрос у нас нет».

7. SmolLM3

SmolLM3, возможно, не так популярен, как другие LLM, рассмотренные в этой статье, но он всё же заслуживает внимания: модель демонстрирует отличные показатели при относительно скромных и удобных 3 миллиардах параметров — промежуточном размере между 1.7B и 4B вариантами Qwen3, как показано на рисунке ниже.

Кроме того, как и в случае с OLMo, команда поделилась большим количеством информации о тренировочном процессе, что редкость и всегда высоко ценится.

Рисунок 20: Иллюстрация из анонсного поста SmolLM3 (https://huggingface.co/blog/smollm3), сравнивающая процент побед SmolLM3 с Qwen3 1.7B и 4B, а также с Llama 3 3B и Gemma 3 4B.

Как видно из сравнительной схемы архитектур, SmolLM3 использует довольно стандартную архитектуру. Возможно, наиболее интересной особенностью модели является применение NoPE (No Positional Embeddings).

Рисунок 21: Сравнение архитектур Qwen3 4B и SmolLM3 3B в формате side-by-side.

7.1 NoPE (No Positional Embeddings)

NoPE — это достаточно старая идея в контексте LLM, восходящая к статье 2023 года The Impact of Positional Encoding on Length Generalization in Transformers, где предлагалось отказаться от явной инъекции позиционной информации (например, через абсолютные позиционные эмбеддинги в ранних архитектурах GPT или современный RoPE).

В трансформерах позиционное кодирование обычно необходимо, поскольку self-attention изначально не учитывает порядок токенов. Абсолютные позиционные эмбеддинги решают эту проблему, добавляя отдельный эмбеддинг-слой, который кодирует позицию токена.

Рисунок 22: Модифицированная иллюстрация из книги Build A Large Language Model (From Scratch) (https://www.amazon.com/Build-Large-Language-Model-Scratch/dp/1633437167), демонстрирующая абсолютные позиционные эмбеддинги.

RoPE же действует иначе — он модифицирует (ротирует) векторы запросов и ключей в зависимости от позиции токена.

В слоях NoPE позиционный сигнал не добавляется вообще: ни фиксированный, ни обучаемый, ни относительный — никакой.

Несмотря на отсутствие явного позиционного кодирования, модель всё равно понимает порядок токенов благодаря маске причинности (causal attention mask), которая запрещает каждому токену «заглядывать» в будущее. Таким образом, токен в позиции t может «видеть» только токены в позициях ≤ t, что сохраняет автоагрессивный порядок.

Иными словами, хотя позиционная информация и не добавляется явно, в архитектуре модели всё равно есть встроенное направление, и модель может научиться использовать это при обучении градиентным спуском, если это помогает минимизировать loss. (Подробности — в теоремах из оригинальной статьи про NoPE.)

В целом, авторы статьи про NoPE показали не только то, что явное позиционное кодирование не обязательно, но и то, что отказ от него улучшает length generalization — способность LLM сохранять качество ответов при увеличении длины входной последовательности (см. график ниже).

Рисунок 23: Иллюстрация из статьи о NoPE (https://arxiv.org/abs/2305.19466), показывающая улучшенную обобщающую способность по длине благодаря NoPE.

Важно понимать, что эксперименты проводились на относительно маленькой GPT-подобной модели (~100M параметров) и с ограниченной длиной контекста. Пока неясно, насколько эти выводы масштабируются на современные крупные LLM.

Возможно, именно поэтому команда SmolLM3 применяет NoPE (или точнее, опускает RoPE) только в каждом четвёртом слое.

8. Kimi 2

Kimi 2 недавно наделал немало шума в AI-сообществе как open-weight модель с выдающейся производительностью. Согласно бенчмаркам, она наравне с лучшими проприетарными системами — такими как Gemini от Google, Claude от Anthropic и модели ChatGPT от OpenAI.

Особого внимания заслуживает использование варианта относительно нового оптимизатора Muon вместо классического AdamW. Насколько мне известно, это первый случай применения Muon в продакшн-модели такого масштаба — ранее он демонстрировал масштабируемость только до 16B параметров. Это дало отличную динамику функции потерь во время обучения, что, вероятно, и помогло модели резко вырваться в лидеры среди аналогов.

Хотя многие отмечали, что loss получился необычайно гладким (без характерных скачков), я бы не сказал, что он исключительно гладкий (например, посмотрите на кривую loss у OLMo 2 на графике ниже; к тому же, L2-норма градиента, возможно, была бы лучшей метрикой стабильности обучения). Но что действительно впечатляет — это скорость и стабильность убывания loss.

Тем не менее, как уже упоминалось во введении, методологии обучения — это тема для отдельного обсуждения.

Рисунок 24: Иллюстрации из блога, анонсирующего Kimi K2 (https://moonshotai.github.io/Kimi-K2/), и статьи о OLMo 2 (https://arxiv.org/abs/2305.19466).

Сама модель содержит 1 триллион параметров, что по-настоящему впечатляет.

На момент написания статьи это, вероятно, самая крупная LLM текущего поколения, если учитывать, что:

LLaMA 4 Behemoth ещё не вышла,
проприетарные LLM не учитываются,
а 1.6-триллионный Switch Transformer от Google — это энкодер-декодер архитектура из другого поколения.

Таким образом, круг замыкается: Kimi 2 использует архитектуру DeepSeek-V3, с которой мы начали эту статью, только в более масштабном виде, как показано на схеме ниже.

Рисунок 25: Сравнение архитектур DeepSeek V3 и Kimi K2.

Как видно, Kimi 2.5 — это по сути тот же DeepSeek V3, но с большим числом экспертов в MoE-модулях и меньшим числом голов в модуле Multi-head Latent Attention (MLA).

Стоит отметить, что Kimi 2 — не взявшийся из ниоткуда проект. Более ранняя модель — Kimi 1.5, подробно описанная в статье Kimi k1.5: Scaling Reinforcement Learning with LLMs, уже тогда выглядела многообещающей. Однако ей не повезло: статья про DeepSeek R1 вышла в тот же день — 22 января. Кроме того, насколько мне известно, веса Kimi 1.5 так и не были опубликованы.

Судя по всему, команда Kimi учла этот опыт и выложила Kimi K2 как open-weight модель до релиза DeepSeek R2.

На момент написания, Kimi K2 — самая впечатляющая open-weight модель, доступная сообществу.

Заключение

Эволюция архитектур LLM в последние годы шла по пути итеративных улучшений, а не революционных прорывов. Технологии, такие как GQA, SwiGLU и RoPE, стали стандартом. В 2025 году мы наблюдаем взрыв популярности архитектур Mixture-of-Experts (MoE), которые позволяют создавать огромные модели (Llama 4, DeepSeek-V3, Kimi K2) с эффективным инференсом. Эти тенденции определяют будущее больших языковых моделей, предлагая разработчикам все более мощные и эффективные инструменты.

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28 июл. 2025 г.

22.06.26 21:51 kimberlyhebert786

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05.07.26 14:44 lydiassmith567

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06.07.26 16:20 Olga Ognjanović

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06.07.26 16:31 Joseph Weigl

Invest wisely and stay cautious. Don't be influenced by promises of unusually high returns or convincing sales pitches from brokers. I learned this the hard way after falling victim to an investment scam that promised huge profits. Fortunately, I acted quickly and reported the incident to a recovery firm for assistance. Contact: Email: [email protected] Telegram: @Resqprofirm WhatsApp: +1 985 296 9146
06.07.26 16:33 Jaran Løvlien

A heartfelt thank you to RESQPRO FIRM for their commitment and professionalism throughout the investigation of my case. Their team worked diligently and helped recover assets valued at $88,000, which were returned to my wallet. I truly appreciate their support, clear communication, and dedication, and I'm grateful for the assistance I received. Contact: Email: [email protected] Telegram: @Resqprofirm WhatsApp: +1 985 296 9146
07.07.26 18:00 robertalfred175

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07.07.26 18:01 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 Contact: [email protected] Call/Text: +1 (336) 390-6684 Website: https://recovercapital.wixsite.com/capital-crypto-rec-1
09.07.26 19:06 Toivo Walli

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09.07.26 19:10 Misty Alexander

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12.07.26 03:33 Pahal Mathew

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13.07.26 23:49 [email protected]

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15.07.26 11:53 Sarah Green

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16.07.26 21:38 patricialovick86

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16.07.26 21:38 patricialovick86

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17.07.26 19:24 laimqq90

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17.07.26 20:12 martinsjude080

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18.07.26 17:12 Malthe Larsen

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18.07.26 17:42 پریا رضایی

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18.07.26 23:51 bernalzenaida

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19.07.26 04:10 Fraddy Pual

I can't thank Fundsretriever enough for everything they did for me. My name is Vanessa Conway, and I'm here to tell you my story of how I recovered money I never thought I'd see again. A few months ago, I put a significant amount of money into what looked like a genuine online investment company. At first, it felt real—they showed me fake profits and convinced me to invest even more. But when I tried to cash out, they went quiet and started asking for extra fees. That's when it hit me—I had been scammed. I was heartbroken, frustrated, and didn't know where to turn. That money was my savings—months of hard work gone. Then I found Fundsretriever online. I reached out, hoping for a miracle. Right away, their team made me feel heard. They were responsive, knowledgeable, and walked me through everything. They didn't just take my case—they took it seriously and kept me in the loop every step of the way. I finally felt like I had real experts fighting for me. And guess what? They actually got my money back. It wasn't instant, and it took teamwork, but it happened. When I saw those funds returned, I cried with joy. I'm sharing this so that anyone else out there who's been scammed knows—don't lose hope. Do your research before investing, and stay far away from platforms that promise too much too fast. And if you've already been stung, don't wait—get professional help immediately. Thank you, Fundsretriever, from the bottom of my heart. You didn't just recover my money—you restored my faith. — Vanessa Conway 📧 [email protected] 📱 WhatsApp: +16035121448 💬 Telegram: @Fundsretriever
19.07.26 19:53 kimberlyhebertt6877

I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] Call/Text Number: +1 (336) 390-6684
19.07.26 19:53 kimberlyhebertt6877

I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] Call/Text Number: +1 (336) 390-6684
19.07.26 19:54 kimberlyhebertt6877

I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] Call/Text Number: +1 (336) 390-6684
30.07.26 00:04 Ahmed

A really cool analysis, thank you. I was especially struck by how strictly the order is defined: data processing and formatting come first, with color only at the very end. This really saves you from the typical mistake of "first a pretty palette, then we figure out what the chart is for." And the palette validator with OKLCH + colorblindness check is absolutely fantastic; you almost never see that in regular tools. By the way, while reading about rank-trajectory and the logarithmic scale, I immediately remembered how convenient it is to analyze dynamics on charts in ExpertOption—I've been trading there for quite some time now. When the data is well visualized, decisions are noticeably easier and more relaxed. I also liked the point about "no more than eight colors" and the dual-axis ban. Strict restrictions sometimes actually produce better results than complete freedom. I'll try this approach myself.
30.07.26 17:27 wendytaylor015

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30.07.26 17:27 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
31.07.26 16:21 rssllhrnsb

I learned an important lesson after investing in what appeared to be a genuine opportunity. Unfortunately, I was unable to access my funds, which reinforced the importance of carrying out thorough due diligence, checking whether an investment is appropriately regulated, and seeking qualified professional advice rather than relying solely on online reviews or testimonials. During the process of addressing my case, I worked with Mrs. Doris Ashley, who communicated clearly, provided regular updates, and handled the matter in a professional manner. According to my experience, I have recovered $50,000 so far, while efforts to resolve the remaining balance are still in progress. If you wish to contact her, the details I used are: Mrs. Tatiana Sorina TEXT : (tatianasorina06 at G.Ma IL ..c 0 m ) Before committing money to any investment, take time to verify the legitimacy of the platform, confirm any relevant regulatory authorisations, and avoid investing more than you can comfortably afford to lose.
01.08.26 15:05 keithwilson9899

ETHEREUM RECOVERY ASSISTANCE: CAPITAL CRYPTO RECOVER HELPED ME RECOVER $98,000 WORTH OF LOST ETH In cases of cryptocurrency scams, having accurate information and trusted support is essential. I would like to recommend Capital Crypto Recover Service, a professional team that specializes in assisting individuals with the recovery of lost or stolen Bitcoin and Ethereum (ETH). Their experienced experts are dedicated to helping victims of digital asset fraud by carefully analyzing each case, developing strategic recovery plans, Capital Crypto Recover Service knowledgeable team's primary goals are to satisfy clients and offer significant support and working diligently toward fund retrieval. The team is committed to providing reliable assistance and maintaining a high level of client satisfaction. Based on my assessment, their reputation professionalism and a strong commitment to their clients. If you have experienced a cryptocurrency loss, you can contacting them for further assistance Phone (Call/Text): +1 (336) 390-6684 Email: [email protected] Alternate Email: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1
01.08.26 15:05 keithwilson9899

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03.08.26 20:05 Philip

I was a victim of a crypto theft involving a Pink Drainer, which resulted in the theft of my Wrapped Bitcoin (WBTC) from my Polygon network. The experience was incredibly frustrating and distressing, as I had no idea how to recover my funds. Unfortunately, by the time I noticed the theft, the funds had already been drained to an address that I had no control over, making it seem like an irreversible situation.The attack happened when I clicked on what appeared to be a legitimate link. I didn’t realize at the time that it was a phishing attempt designed to siphon off my private keys and access my wallet. The Pink Drainer, a type of malicious script used by attackers, is specifically designed to exploit such vulnerabilities in crypto wallets. The moment I realized that my WBTC had been drained from my Polygon network. I felt completely helpless, as I didn’t have direct access to the thief's address, and there was no way to reverse the transaction on my own at that point, I started searching for ways to recover my funds, but most resources only offered generic advice that wasn’t practical in this particular case. I quickly realized that if I wanted to have any hope of getting my assets back, I would need professional assistance. After some research, I came across a reliable and trusted recovery team called Aspen Recovery Experts. Their expertise in cryptocurrency recovery, especially in cases like mine, seemed promising.I decided to reach out to Aspen Recovery Experts, and I’m incredibly grateful that I did. Their team of crypto recovery experts was able to help me trace the stolen funds and identify the path the funds took after they left my wallet. Using advanced tools and techniques, they were able to track the transactions on the blockchain, helping me understand where my WBTC had been sent. More importantly, they worked tirelessly to assist me in contacting the necessary parties and even interfaced with blockchain analysts to help facilitate the recovery process.Thanks to their efforts, I was able to successfully recover my stolen funds. The entire process took some time, but the Aspen Recovery Experts dedicated team provided regular updates and kept me informed throughout the process, which gave me a sense of hope and relief during an otherwise stressful time. If you’re ever in a similar situation, I highly recommend reaching out to a trusted recovery team like Aspen Recovery Expertshhh . Their professionalism, knowledge, and expertise were critical in helping me recover my funds and regain control of my crypto assets. Whatsapp : +1 747 231 9036 Telegram : @ prohackerspy Email : [email protected]
03.08.26 20:06 Philip

I was a victim of a crypto theft involving a Pink Drainer, which resulted in the theft of my Wrapped Bitcoin (WBTC) from my Polygon network. The experience was incredibly frustrating and distressing, as I had no idea how to recover my funds. Unfortunately, by the time I noticed the theft, the funds had already been drained to an address that I had no control over, making it seem like an irreversible situation.The attack happened when I clicked on what appeared to be a legitimate link. I didn’t realize at the time that it was a phishing attempt designed to siphon off my private keys and access my wallet. The Pink Drainer, a type of malicious script used by attackers, is specifically designed to exploit such vulnerabilities in crypto wallets. The moment I realized that my WBTC had been drained from my Polygon network. I felt completely helpless, as I didn’t have direct access to the thief's address, and there was no way to reverse the transaction on my own at that point, I started searching for ways to recover my funds, but most resources only offered generic advice that wasn’t practical in this particular case. I quickly realized that if I wanted to have any hope of getting my assets back, I would need professional assistance. After some research, I came across a reliable and trusted recovery team called Aspen Recovery Experts. Their expertise in cryptocurrency recovery, especially in cases like mine, seemed promising.I decided to reach out to Aspen Recovery Experts, and I’m incredibly grateful that I did. Their team of crypto recovery experts was able to help me trace the stolen funds and identify the path the funds took after they left my wallet. Using advanced tools and techniques, they were able to track the transactions on the blockchain, helping me understand where my WBTC had been sent. More importantly, they worked tirelessly to assist me in contacting the necessary parties and even interfaced with blockchain analysts to help facilitate the recovery process.Thanks to their efforts, I was able to successfully recover my stolen funds. The entire process took some time, but the Aspen Recovery Experts dedicated team provided regular updates and kept me informed throughout the process, which gave me a sense of hope and relief during an otherwise stressful time. If you’re ever in a similar situation, I highly recommend reaching out to a trusted recovery team like Aspen Recovery Expertshhh . Their professionalism, knowledge, and expertise were critical in helping me recover my funds and regain control of my crypto assets. Whatsapp : +1 747 231 9036 Telegram : @ prohackerspy Email : [email protected]
04.08.26 11:05 Kisnoles

Excellent analysis, thank you. I was particularly struck by how rigidly the skill sets the order: data processing and form come first, with color coming last. This really cures the habit of "first a pretty palette, then we'll figure it out." A palette validator using OKLCH + color blindness + WCAG is something most chart generators lack. And regarding the boundaries of competence, it's very clear. Where there's a self-checking loop (color), you delegate freely. Where there's a heuristic (form, data interpretation), you remain the final filter. This is a universal principle, not just for /dataviz. By the way, when you look at trading dashboards (including those of decent platforms like ExpertOption), it's immediately clear who thought about readability and who just threw in rainbow lines. Tools like this skill could greatly improve the quality of analytics. Thanks again for the detailed analysis – saved.
04.08.26 12:37 kimberlyhebertt6877

I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] Call/Text Number: +1 (336) 390-6684
04.08.26 12:37 kimberlyhebertt6877

I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] Call/Text Number: +1 (336) 390-6684
06.08.26 08:11 ROMMYHENDERSON344

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06.08.26 13:56 lydiassmith567

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06.08.26 13:56 lydiassmith567

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11.08.26 03:43 raymont0714

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12.08.26 16:37 rssllhrnsb

I learned an important lesson after investing in what appeared to be a genuine opportunity. Unfortunately, I was unable to access my funds, which reinforced the importance of carrying out thorough due diligence, checking whether an investment is appropriately regulated, and seeking qualified professional advice rather than relying solely on online reviews or testimonials. During the process of addressing my case, I worked with Mrs. Tatiana Sorina , who communicated clearly, provided regular updates, and handled the matter in a professional manner. According to my experience, I have recovered $50,000 so far, while efforts to resolve the remaining balance are still in progress. If you wish to contact her, the details I used are: Mrs. Tatiana Sorina TEXT : (tatianasorina06 at G_Ma IL dot ..c 0 m)…. Before committing money to any investment, take time to verify the legitimacy of the platform, confirm any relevant regulatory authorisations, and avoid investing more than you can comfortably afford to lose.
16.08.26 01:44 Matt Kegan

CapitalNode Analytics. They help to investigate and recover stolen digital assets from fake trading platforms. Great firm i must say.
18.08.26 04:59 marcushenderson624

Bitcoin Recovery Testimonial After falling victim to a cryptocurrency scam group, I lost $354,000 worth of USDT. I thought all hope was lost from the experience of losing my hard-earned money to scammers. I was devastated and believed there was no way to recover my funds. Fortunately, I started searching for help to recover my stolen funds and I came across a lot of testimonials online about Capital Crypto Recovery, an agent who helps in recovery of lost bitcoin funds, I contacted Capital Crypto Recover Service, and with their expertise, they successfully traced and recovered my stolen assets. Their team was professional, kept me updated throughout the process, and demonstrated a deep understanding of blockchain transactions and recovery protocols. They are trusted and very reliable with a 100% successful rate record Recovery bitcoin, I’m grateful for their help and highly recommend their services to anyone seeking assistance with lost crypto. Contact: [email protected] Phone WhatsApp/Text Number: +1 (336) 390-6684 Email: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1
18.08.26 04:59 marcushenderson624

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19.08.26 14:50 BAYER7043

My account was locked, and I couldn’t access my own information until [email protected] +447476618364 whats?up? helped me recover it. This lady hacker was kind and professional, but this experience showed me how risky account lockouts can be. If you’re facing the same problem, do not panic consult her A. S. A. P. Be careful with recovery agents outchea, and research their name, business, and reviews before sharing any details. Never send passwords, security codes, banking information, or copies of your ID unless you’ve confirmed who you’re dealing with. Be wary of promises that sound too good to be true, especially claims that require little information or guarantee instant access with outrageous fees.
20.08.26 11:32 michaeldavenport218

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20.08.26 11:32 michaeldavenport218

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23.08.26 20:02 leslieyee

Excellent analysis by /dataviz! I was particularly struck by how strictly the order is defined: "data meaning first, color last" and how the validator actually adjusts the palette according to OKLCH, color blindness, and WCAG standards. These rules greatly improve the quality of charts. By the way, a similar approach to clean and legible charts is highly valued in trading—for example, on the ExpertOption platform, the interface and price visualization are designed to ensure information is read instantly and without unnecessary noise.
25.08.26 13:44 lydiassmith567

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25.08.26 13:44 lydiassmith567

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30.08.26 15:37 [email protected]

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30.08.26 15:37 [email protected]

Discovering I had been defrauded, I searched online for services claiming to recover stolen ETH. I contacted several companies, but none succeeded. Although some claimed they could trace assets or recover funds, I could not verify their success or recover my money. I later found SYLVESTER BRYANT Recovery through Google. After contacting him, he recovered 450,000,00. He can be reached at [email protected] or WhatsApp at +1 512 577 7957.
01.09.26 11:16 lisawerth897

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01.09.26 11:16 lisawerth897

My Experience With Cryptocurrency — CAPITAL CRYPTO RECOVER I lost over $82,000 in Bitcoin after falling victim to a fraudulent online investment scheme. After realizing I had been scammed, I spent considerable time researching possible ways to recover my funds. During my research, I came across CAPITAL CRYPTO RECOVER and decided to contact them after reading their reported success record and encouraging client positive reviews and testimonials. Their team guided me through the recovery process, and I was grateful to successfully recover my lost cryptocurrency. The experience was challenging, but I appreciated the assistance and support I received throughout the process. I’m sharing my experience in the hope that it may encourage other victims to carefully research their options and verify any recovery service before proceeding. I will forever be thankful to you CAPITAL CRYPTO RECOVER 📧 [email protected] 🌐 Website: recovercapital.wixsite.com/capital-crypto-rec-1 📧 [email protected] 📞 Call/WhatsApp: +1 (336) 390-6684
01.09.26 17:34 Garry42

Really crazy world. These fraudsters go at any length to steal your hard earned funds. I have been a victim of a bitcoin scam about 7 months back. A Con artist gained access to my cashapp account through a phishing scam. They stole $409,000. I was really devastated. I did everything to get back my funds by contacting the FBI but they claimed there was nothing they could do. A friend told me about a recovery expert. He helps fight against various phishing and investment scams and they were able to help trace and recover my funds even though it took over 2 days. you can reach out to him through recoverydarek@gmail. com . I can guarantee his services are still active.
01.09.26 17:34 Garry42

Really crazy world. These fraudsters go at any length to steal your hard earned funds. I have been a victim of a bitcoin scam about 7 months back. A Con artist gained access to my cashapp account through a phishing scam. They stole $409,000. I was really devastated. I did everything to get back my funds by contacting the FBI but they claimed there was nothing they could do. A friend told me about a recovery expert. He helps fight against various phishing and investment scams and they were able to help trace and recover my funds even though it took over 2 days. you can reach out to him through recoverydarek@gmail. com . I can guarantee his services are still active.
02.09.26 03:43 kimberlyhebertt6877

I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] WhatsApp/Text Number: +1 (336) 390-6684
02.09.26 03:43 kimberlyhebertt6877

I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] WhatsApp/Text Number: +1 (336) 390-6684
04.09.26 21:51 Kovengray

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05.09.26 21:38 [email protected]

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09.09.26 21:31 lisawerth897

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09.09.26 21:31 lisawerth897

My Experience With Cryptocurrency — CAPITAL CRYPTO RECOVER I lost over $82,000 in Bitcoin after falling victim to a fraudulent online investment scheme. After realizing I had been scammed, I spent considerable time researching possible ways to recover my funds. During my research, I came across CAPITAL CRYPTO RECOVER and decided to contact them after reading their reported success record and encouraging client positive reviews and testimonials. Their team guided me through the recovery process, and I was grateful to successfully recover my lost cryptocurrency. The experience was challenging, but I appreciated the assistance and support I received throughout the process. I’m sharing my experience in the hope that it may encourage other victims to carefully research their options and verify any recovery service before proceeding. I will forever be thankful to you CAPITAL CRYPTO RECOVER 📧 [email protected] 🌐 Website: recovercapital.wixsite.com/capital-crypto-rec-1 📧 [email protected] 📞 Call/WhatsApp: +1 (336) 390-6684
09.09.26 23:22 Fraddy Pual

Hearing that these individuals are focusing on other people makes me very sad. I had a similar situation with them and lost a lot of money, but after learning about (Cruxcipherteam @ proton DoT me), whataqq:+168160-15021, telegram: @Cruxcipherteam I was able to get my money back. It's critical that we all be watchful and keep reporting these occurrences.
11.09.26 03:23 kimberlyhebertt6877

I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] WhatsApp/Text Number: +1 (336) 390-6684
11.09.26 03:23 kimberlyhebertt6877

I invested in bitcoin trading After losing $78.4 USDT) linked to a romance fraud scam worth of cryptocurrency through an online investment platform and later discovered it was a scam. After extensive research for recovery options, I contacted CAPITAL CRYPTO RECOVER based on positive client reviews and recommendations. Their professional security team guided me through the recovery process using advanced technology, and I was able to recover my lost cryptocurrency successfully. I am truly grateful for their support and assistance during such a difficult experience. I will advise you to contact CAPITAL CRYPTO RECOVER helped me recover my funds. For anyone facing similar issues, Website: https://recovercapital.wixsite.com/capital-crypto-rec-1 Email: [email protected] Telegram: @Capitalcryptorecover Contact: [email protected] WhatsApp/Text Number: +1 (336) 390-6684
15.09.26 03:35 elioduncan

I fell victim to a freelance web development contract scam that ultimately cost me CAD 4,000. It all started when I came across a job posting on Indeed Canada, a popular online job portal. The position seemed perfect: a freelance web developer role for an established company looking for someone to design and build a fully functional e-commerce website. The job promised a high payment of CAD 20,000 upon successful completion, which sounded like a great opportunity for me to gain experience and earn decent pay.The employer, who introduced himself as a project manager from a "well-known" tech company, was very persuasive and professional in our initial communication. He explained that the project involved developing a user-friendly, responsive online store for their client and that they had a strict timeline to meet. He assured me that the payment would be made promptly after completing the tasks. However, before starting the work, he told me that I would need to pay an upfront fee of CAD 4,000 to cover certain software tools and licensing fees required for the project. This was supposedly a part of their company policy for freelance contractors, ensuring access to their premium resources. The idea of working on a professional project, coupled with the promise of a substantial payout, convinced me to pay the upfront fee.As soon as I made the payment, the project manager became increasingly difficult to reach. He initially responded to my emails and provided some vague instructions on what the project would entail, but as time went on, communication slowed to a complete halt. I never received the required tools or any proper project details. My emails went unanswered, and any attempts to contact the company were met with silence. After waiting for weeks, I realized that I had been scammed.Desperate to recover my money, I turned to TechY Force Cyber Retrieval, a service that specializes in helping victims of online scams. They helped me track the payment and took legal steps to pursue the fraudsters. Through their guidance, I was able to recover the full CAD 4,000. TechY Force Cyber Retrieval worked with my bank to reverse the transaction and liaised with the authorities to trace the scammer's details.This was a hard lesson, and I now know to be highly cautious about online job offers that require upfront payments. It is crucial to research companies thoroughly and avoid any job that seems too good to be true, especially when it involves paying money upfront. WhatsApp https://wa.link/2x6ktp Mail. [email protected]
15.09.26 15:45 lydiassmith567

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15.09.26 15:45 lydiassmith567

HIRE A HACKER YOUR STOLEN CRYPTO RECOVERY / BTC / USDT / ETH WITH THE HELP OF CAPITAL CRYPTO RECOVER. I want to share my experience publicly regarding cryptocurrency wallet recovery, I highly recommend you to contact CAPITAL CRYPTO RECOVER, a professional private investigator in the Bitcoin world. They have a certified expert security team specializing in Bitcoin Recovery Services and have helped many people worldwide recover their lost funds. My wife and I were defrauded by an online manipulator posing as an experienced crypto investment professional. We lost $9.2 Million Stolen BTC in cryptocurrency and were left feeling homeless. After spending hours searching for a reliable crypto recovery service, I discovered CAPITAL CRYPTO RECOVER online. By patiently explaining my situation to their team, I was able to recover all my funds. Remarkably, my money was returned to my wallet in less than 24 hours. I am extremely grateful to CAPITAL CRYPTO RECOVER for their excellent assistance—they truly were a godsend in my difficult situation. If you have fallen victim to a cryptocurrency scam, you can reach CAPITAL CRYPTO RECOVER through the following channels Email: [email protected] OR Call/WhatsApp: +1 (336) 390-6684 Contact: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1