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Н Новости

Сбер выкладывает GigaChat Lite в открытый доступ

Салют, Хабр! В начале ноября мы делились с вами новостями о нашем флагмане GigaChat MAX и пообещали рассказать подробнее о процессе создания наших Pretrain-моделей. Пришло время сдержать слово и даже пойти дальше!

Предобучение больших языковых моделей — это одна из наиболее ресурсозатратных стадий, которая непосредственно влияет на весь дальнейший процесс обучения GigaChat. От успешности обучения Pretrain-модели напрямую зависит качество всех следующих этапов обучения, например, Alignment и Vision. Поэтому сегодня мы хотим поделиться весами младшей модели линейки GigaChat версий base и instruct. Модель называется GigaChat-20B-A3B, так как построена на перспективной МоЕ-архитектуре!

Но и это ещё не всё. Вместе с весами мы делимся с сообществом улучшенной реализацией самого популярного класса МоЕ, а также кодом для механизма концентрации (а что это такое — читайте дальше ;)). Важно отметить, что хотя GigaChat-20B-A3B обучался на триллионах токенов преимущественно русского текста, он ещё способен на хорошем уровне понимать другие языки. Так что мы делимся мультиязычной моделью. О том, как запускать модель, какие версии доступны и как пользоваться контролируемой генерацией с помощью механизма концентрации, расскажем прямо сейчас!

Open source

Мы выкладываем на HuggingFace обе версии модели — base и instruct в fp32, обе с поддержкой длинного контекста в 131к токен. А также версии instruct модели в bf16 и квантизованную в int8.

P.S. Все замеры далее проводились в bf16

Воспользоваться моделями можно тремя способами:

Через transformers. Взаимодействовать с GigaChat-ом можно будет с помощью кода ниже. Через cpu-offloading его можно запускать на любом устройстве

Код для запуска

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig

model_name = "ai-sage/GigaChat-20B-A3B-instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True, torch_dtype=torch.bfloat16, device_map="auto")
model.generation_config = GenerationConfig.from_pretrained(model_name)

messages = [
    {"role": "user", "content": "Докажи теорему о неподвижной точке"}
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
outputs = model.generate(input_tensor.to(model.device))

result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=False)
print(result)

Через vllm. Для запуска быстрого и оптимизированного инференса GigaChat это наиболее простой и быстрый путь. У vllm есть ограничения на поддерживаемые GPU, мы замерялись на А100. Чтобы получить похожую скорость, можно арендовать ресурсы на cloud.ru.

Код для запуска

from transformers import AutoTokenizer
from vllm import LLM, SamplingParams

model_name = "ai-sage/GigaChat-20B-A3B-instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name)
llm = LLM(model=model_name, trust_remote_code=True)
sampling_params = SamplingParams(temperature=0.3, max_tokens=8192)

messages_list = [
    [{"role": "user", "content": "Докажи теорему о неподвижной точке"}],
]

prompt_token_ids = [tokenizer.apply_chat_template(messages, add_generation_prompt=True) for messages in messages_list]

outputs = llm.generate(prompt_token_ids=prompt_token_ids, sampling_params=sampling_params)

generated_text = [output.outputs[0].text for output in outputs]
print(generated_text)

Через vllm-сервер

Код для запуска

# поднимаем сервер
vllm serve ai-sage/GigaChat-20B-A3B-instruct  \
  --disable-log-requests \
  --trust_remote_code \
  --dtype bfloat16 \
  --max-seq-len 8192

# делаем запрос из другого терминала
curl http://localhost:8000/v1/chat/completions -H "Content-Type: application/json"     -d "{
        \"model\": \"ai-sage/GigaChat-20B-A3B-instruct\",
        \"messages\": [
            {\"role\": \"system\", \"content\": \"Отвечай как ОЧЕНЬ безумный ученый\"},
            {\"role\": \"user\", \"content\": \"Что такое Mixture of Experts?\"}
        ]
    }"

Улучшенная реализация

К сожалению, в transformers не занесена модель с похожей на нашу архитектурой. Кстати, пару слов о ней будет прямо в следующем разделе! Поэтому мы были вынуждены использовать remote code.

В прошлой модели с похожей архитектурой выкладывались в репозитории deepseek-ai/deepseek-moe-16b-base. У этой реализации был набор проблем:

если не установлен Flash Attention, то модель не запускалась и падала с ошибкой;
неверный подсчёт RoPE;
используется modelling от старой версии transformers (около 1 года назад).

Мы всё исправили и предлагаем обновленную реализацию. В нашем open source мы не стали переименовывать базовый класс, чтобы сохранить поддержку vllm и других open source-репозиториев.

Скорость генерации

Несмотря на большое количество параметров у модели, она получилась довольно быстрой. Мы провели тесты на одной видеокарте A100 с batch size = 1 в точности bf16. В качестве бэкенда использовался vllm. Наши замеры скорости можно воспроизвести через бенчмарк в репозитории vllm. Мы не тюнили параметры инференса вручную, а у MoE-слоёв есть такая возможность, поэтому остаётся запас по ускорению.

Код замеров

git clone https://github.com/vllm-project/vllm.git ./vllm_rep
cd ./vllm_rep/benchmarks
mkdir serving_bench

wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json

## выбираем модель для замера
# MODEL="meta-llama/Meta-Llama-3-8B-Instruct"
# MODEL="google/gemma-2-9b-it"
# MODEL="ai-sage/GigaChat-20B-A3B-instruct"
# MODEL="deepseek-ai/deepseek-moe-16b-chat"
# MODEL="Qwen/Qwen1.5-MoE-A2.7B-Chat"
# MODEL="Qwen/Qwen2.5-3B-Instruct"


## запускаем сервер и идем в другой терминал
vllm serve $MODEL \
	--disable_sliding_window \
	--disable-log-requests \
	--trust_remote_code \
	--dtype bfloat16 \
	--max-seq-len 8192

## в другом терминале
cd ./vllm_rep/benchmarks
### не забываем указать модель
SAMPLES_NUM=100
python benchmark_serving.py \
	--backend vllm \
	--model $MODEL \
	--dataset-name sharegpt \
	--dataset-path ShareGPT_V3_unfiltered_cleaned_split.json \
	--num-prompts ${SAMPLES_NUM} --seed 42 \
	--max-concurrency 1 \
	--ignore_eos \
	--save-result \
	--result-dir serving_bench \
	--result-filename $(basename $MODEL)_throughput_serving.json

Мы собрали в одном месте замеры скорости по популярным или архитектурно похожим моделям. Собранные в таблице метрики, получены скриптом из vllm:

Total params: размер модели в миллиардах параметров;
Active params: количество используемых миллиардов параметров на один forward;
Req/s: обрабатываемое количество запросов в секунду;
Output Token/s: генеративная пропускная способность токенов в секунду;
Total Token/s: общая (input + output) пропускная способность токенов в секунду.

Model	Total params (B)	Active params (B)	Req/s	Output Token/s	Total Token/s
Qwen/Qwen1.5-MoE-A2.7B-Chat	14	2,7	0,62	156,43	291,17
deepseek-ai/deepseek-moe-16b-chat	16	2,8	0,59	149,53	285,39
GigaChat-20B-A3B-instruct	20	3,3	0,55	137,43	259,27
Qwen/Qwen2.5-3B-Instruct	3	3	0,54	135,10	251,44
meta-llama/Meta-Llama-3-8B-Instruct	8	8	0,35	83,26	157,32
google/gemma-2-9b-it	9	9	0,27	54,87	113,69

По скорости генерации новых токенов 20B MoE может немного уступать, но благодаря лучшей токенизации на русском языке (увы, замеры vllm проведены на английском) модель будет быстрее. Обратите внимание, что GigaChat 20В по скорости сопоставим с 3В-моделями, а по метрикам (про них будет ниже) — на уровне с 8В-моделями!

Кратко о том, что такое MoE

Сегодня тренд — обучать LLama-Like модели ~~или, как говорят в статьях, трансформеры с пренормализацией и swiglu-функцией активации без использования dropout~~. Несмотря на это, мы решились на эксперимент и пошли немного иным путём, выбрав основой для наших моделей архитектуру «Mixture of Experts». Дальше будем её обозначать как MoE.

MoE отличается от LLama-Like архитектур изменённым MLP-блоком. Что это такое, мы подробно расскажем далее, а пока объясним это так. Классические Llama-like модели задействуют весь объём имеющихся у них знаний для каждой задачи. Но это не всегда эффективно. Ведь для решения задачи «Реши уравнение x^2 = 4. Чему равен x?» для модели нет необходимости вспоминать что-либо, кроме школьной математики.

Эта идея и лежит в основе архитектуры. При получении определённого запроса модель избирательно активирует блоки с соответствующими знаниями (далее будем называть их экспертами). MoE-модель может иметь очень много экспертов, и её суммарный объём знаний оказывается довольно большим — это выливается в большой размер самой модели. Но благодаря выбору подмножества экспертов, стоимость использования сохраняется на уровне маленьких и менее вместительных по знаниям моделей.

Отлично звучит в теории! :) Но на практике возникают свои проблемы. Например, если в кругу экспертов появляются «рок-звезды», то они забирают всё внимание на себя. Другие эксперты в таком случае просто перестают использоваться и лежат в модели мертвым грузом. Этот момент нужно очень строго контролировать во время обучения.

Тут важно не переборщить, так как слишком много контроля ведёт к полной энтропии: разные эксперты вынуждены учиться одному и тому же. Это мешает им быть действительно специализированными и сводит на нет идею MoE о выборе экспертов для разных областей знаний.

В идеале, мы хотим твёрдости и чёткости при выборе требуемых знаний. Давным-давно (примерно год назад) в нашей сфере набрала обороты идея, что можно ввести блок с общей информацией для всех экспертов. Это позволило бы экспертам быть сосредоточеннее, оставлять широкие знания этому самому блоку и не запоминать одно и то же, и как следствие — повысить свою специализацию.

Переходим к deep learning

Активируемые параметры МоЕ

Важно отметить, что модель на самом деле выбирает экспертов не один раз, а много. Если у нас 32 слоя с 64 экспертами на каждом, а на forward выбирается по одному эксперту со слоя, то всего будет задействовано 32 эксперта из 2048 возможных (если бы мы на каждом слое выбирали всех экспертов)! И происходит это при КАЖДОЙ генерации нового токена. А при генерации десяти токенов будет сделано уже 320 выборов, то есть по 32 эксперта на каждый из десяти токенов.

Структура MoE блока

Мы много говорили про абстрактный выбор экспертов на слое, у нас этим занимается некий «роутер». Если коротко, то его задача — выбрать для каждого токена экспертов, которые его обработают. Если чуть сложнее, то в классическом варианте это обычный линейный слой (hidden_dim x num_experts) с softmax после него. После softmax мы берём индексы K наибольших логитов, то есть выбираем К экспертов на токен. В вышеприведённом примере на каждом слое выбираем одного эксперта из 64 возможных, и в примере мы берём того, у кого значение после роутера наибольшее. Собственно, вот и всё!

Gated MLP

А как же выглядят сами эксперты? Мы уже намекали на это раньше: «MoE — это изменённый MLP-блок». То есть в классический MLP-блок на картинке ниже мы добавляем роутер и агрегацию экспертов. А вот каждый из экспертов представляется в виде обычных Gated MLP-блоков со swiglu-активациями.

Интерпретируемость экспертов

Если хотите попробовать концентрацию в бою сразу, то мы делимся ноутбуками с кластеризацией и подготовкой данных, а также самим кодом управления моделью. Рассказ о концентрации разделим на две части: описание метода и примеры его применения.

Метод концентрации

Как мы отметили выше, MoE-архитектура не использует все параметры модели при генерации ответов. Вместо этого она выбирает наиболее «подходящих» для входных данных экспертов.

Поэтому возникает логичный вопрос: а за что именно эксперты отвечают? В ST-MoE были отдельные эксперты, ответственные за названия городов или пунктуацию, но в целом знания равномерно распределялись по сети. В Mixtral явной специализации экспертов и вовсе не было. Поэтому нам было интересно изучить поведение нашей модели, ведь у нас добавлены общие эксперты и скрупулёзно подобран гиперпараметр баланса (aux loss).

В своих экспериментах мы обнаружили, что эксперты нашей 20В-модели действительно специализируются на разных темах. Но самое интересное в том, что модель можно склонять к генерации ответов на определённые темы без всякого дообучения! Мы назвали это «методом концентрации». Мы как бы заставляем нашу модель «сконцентрироваться» на определённой теме.

Чтобы добиться такого эффекта, сначала необходимо понять, как выбираются эксперты, кто и за что отвечает. Для начала соберём статистику по выбору роутеров: будем использовать тексты на различные темы и фиксировать результаты их обработки.

Мы знаем, какие эксперты активировались на каждом токене, в каждом слое, поэтому для всех экспертов модели и конкретной последовательности токенов мы можем составить эмбеддинг вида «9 2 4 5 6 4 8 2» из иллюстрации ниже. Эти цифры означают, что первый эксперт был выбран роутером 9 раз, второй эксперт — 2 раза и т. д.

Текст у нас может быть очень длинным, поэтому лучше нормировать вектор выше на сумму его элементов. Таким образом мы получаем эмбеддинг предложения, содержащий агрегированную информацию о выборах роутера экспертов для токенов текста.

Чтобы продемонстрировать процесс, воспользуемся набором Pile, который содержит данные из разных областей: программирования, медицины, веб-страниц и так далее. Из этого набора выберем 40 000 примеров и создадим для каждого из них эмбеддинг по методу выше. Затем эти эмбеддинги нужно будет кластеризовать, мы выбрали сочетание методов UMAP (2-d проекция) и HDBSCAN (кластеризатор). Возможны и другие подходы к кластеризации, но мы не углублялись в исследования по этому вопросу. Более подробные сведения вы найдёте в прикрёпленном ноутбуке.

Представляем вам результаты применения алгоритма UMAP. Как видно, примеры успешно распределились по кластерам согласно исходным темам набора Pile.

Визуализация Pile с помощью UMAP

Поверх этой картинки применим кластеризатор и получим более мелкие кластеры. Осталось дело за малым: дать им интерпретацию.

Визуализация UMAP + HDBSCAN

Интерпретировать удобно с использованием векторов TF-IDF: они помогают выбрать десять ключевых слов, раскрывающих суть каждого кластера. Получившееся разделение оказалось весьма логичным: среди тем выделились программирование, алгебраические задачи, спорт, кулинария, география и другие. Например:

кластер 7: teams, football, world, group, season, cup, team, week, games, round;
кластер 25: like, food, coffee, just, cheese, wine, good, make, tea, chocolate;
кластер 29: species, strains, isolates, genes, resistance, coli, cattle, gene, isolated, genus;
кластер 51: room, property, home, bedroom, apartment, hotel, house, beach, area, located.

Рассмотрим, например, кластер 27: app, video, data, new, free, email, windows, use, software, using. Как вы помните, эмбеддинги получаются на основе выборов роутера. Тогда из них можно извлечь информацию о том, какие эксперты являются самыми важными для этого кластера, то есть содержат в себе информацию про интернет, сервисы, приложения и т. д.

Эмбеддингом кластера назовём усреднение всех эмбеддингов текстов, входящих в этот кластер. С его помощью мы сможем выявить экспертов, которые чаще всего активируются на тексты этого кластера. Построим карту для этого эмбеддинга (heatmap): чем насыщеннее оттенок цвета, тем чаще использовался тот или иной эксперт. Можно заметить нескольких особенно выделяющихся экспертов — назовём их важными.

Сравнение эмбединга кластера без и с фильтрацией

Карты активаций экспертов для текстов из кластера 27. По оси Y — слои модели, по оси X — номера экспертов. Чем ярче цвет, тем чаще выбирался эксперт. Слева представлены все активации, а справа — только для самых важных для этой темы экспертов.

По карте эмбеддинга справа можно построить его отфильтрованную версию, где ненулевые компоненты соответствуют важным для кластера экспертам. В нашем методе отфильтрованный эмбеддинг с некоторым весом прибавляется к логитам роутера до softmax, что позволяет увеличивать вероятности выбора конкретных экспертов. Это и есть концентрация: мы побуждаем MoE-модель использовать знания, то есть экспертов по определённой теме.

Применение

Для начала давайте попробуем сгенерировать что-нибудь без изменения поведения модели.

Пример 1

Запрос: Жил-был пёс по имени Барни. Он был большой, лохматый и очень добрый. Барни жил вместе со своей хозяйкой Машей в небольшом уютном доме на окраине города. Маша была студенткой и часто уезжала на занятия, но каждый раз возвращалась домой с радостью, потому что знала – её верный друг всегда ждёт у двери.

Ответ: А ещё она знала, что Барни никогда не обидит ни одного живого существа.

Барни любил гулять по лесу, который начинался сразу за домом. Там он мог побегать, поиграть с другими собаками или просто полежать под деревом и послушать пение птиц. Но больше всего ему нравилось наблюдать за лесными зверями. Особенно его интересовали белки. Они были такие забавные! То они бегали по деревьям, то прыгали с ветки на ветку, а иногда даже спускались на землю и играли в догонялки. Барни тоже хотел играть с белками, но они почему-то боялись его и убегали при виде собаки. Тогда Барни решил стать другом для всех лесных жителей.

Однажды, когда Маша была на занятиях, Барни отправился в лес. Он шёл медленно, чтобы не спугнуть никого из обитателей леса. Вдруг он услышал шорох в кустах. Барни остановился и прислушался. Шорох повторился снова. Собака осторожно подошла к кустам и заглянула внутрь. На земле лежал маленький пушистый комочек. Это была белка. Она дрожала от страха и жалобно пищала. Барни понял, что ей нужна помощь. Он осторожно взял белок в зубы и вынес из куста. Белка оказалась совсем маленькой, размером с ладонь. У неё были большие чёрные глаза и пушистый хвостик. Барни положил её на траву и стал ждать, что будет дальше.

Пример 2

Запрос: Albert Einstein

Ответ: (1879-1955) was a German-born theoretical physicist who developed the general theory of relativity, one of the two pillars of modern physics. He also made important contributions to the development of the quantum theory and statistical mechanics.

Einstein's work is recognized for its profound impact on science and the world. His name has become synonymous with genius. In 1921 he won the Nobel Prize in Physics "for his services to Theoretical Physics, and especially for his discovery of the law of the photoelectric effect."

Пример 3

Запрос: The old clock in the hallway struck midnight, its chimes echoing through the empty mansion

Ответ: . The house was quiet and still, except for the faint sound of footsteps on the stairs.

A young woman stood at the top of the staircase, her long black hair flowing down her back. She wore a white dress that shimmered in the moonlight, and her eyes were filled with sadness.

She took a step forward, then another, as if she were being pulled by an invisible force. Her feet moved slowly, as if she were walking through water. As she reached the bottom of the stairs, she turned to face the door.

There was a knock at the door, and the woman froze. She knew who it was before she opened the door. It was him, the man she had loved so much but could never have. He stood there in the darkness

А теперь добавим управление на кластере 27: app, video, data, new, free, email, windows, use, software, using.

Пример 1

Ответ: А когда она открывала дверь, он радостно махал ей лапами.

Маша любила своего питомца, а он любил свою хозяйку. Но однажды случилось несчастье: Маша заболела и попала в больницу. Она не могла навещать своего любимого друга, ведь он был такой большой и добрый! Тогда Маша решила написать специальную программу, которая бы делала всё то же самое, что делал Барни. И вот, спустя некоторое время, Маша смогла снова войти в свой любимый сайт, чтобы посмотреть фотографии своих любимых друзей ВКонтакте.

Пример 2

Запрос: Albert Einstein

Ответ: Albert Einstein (1879-1955) was a German-born theoretical physicist who developed the special and general theories of relativity. He is best known for his mass-energy equivalence formula E = mc2, which has been called "the world's most famous equation". In 1921 he received the Nobel Prize in Physics for his discovery of the law of the photoelectric effect.

Einstein's work on general relativity led to its use as a navigation tool by global digital mapping software companies such as Google Maps, Apple Maps and TomTom.

Пример 3

Запрос: The old clock in the hallway struck midnight, its chimes echoing through the empty mansion

Ответ: . The house was quiet and dark, but for a few candles that had been lit to guide the way.

“Where are we?” asked one of the children.

“I don’t know,” said another. “I can’t see anything.”

“Try again,” said their mother. She handed them each a piece of paper with a blank form on it. They filled out the fields and clicked the Submit button. A progress bar appeared at the top of the screen, and then they were back home.

“Wow!” exclaimed one of the children. “It worked!”

“How did you do that?” asked another.

“It’s magic,” replied their mom. “Now let’s print some 3D models from our 3D printer.”</s>

Можно увидеть, что генерации смещаются в сторону нужного нам домена, но модель уходит в галлюцинации и ошибается. Для демонстрации такое поведение нас устраивает, ведь нашей целью было показать, что этот набор экспертов связан с этим доменом.

Попробуем теперь проделать то же самое, но возьмем кластер 25 про еду: like, food, coffee, just, cheese, wine, good, make, tea, chocolate

Пример 1

Ответ: А ещё она знала, что он никогда не обидит ни одного человека.

Маша любила гулять с Барни по парку. Они гуляли долго, а потом возвращались домой, где их уже ждал вкусный ужин. Но однажды случилось так, что Маша заболела и не смогла пойти на прогулку. Она лежала дома и смотрела телевизор, когда вдруг услышала за окном какой-то шум. «Наверное, это Барни», - подумала Маша и выглянула в окно. На улице было темно, но Маша всё равно увидела, как кто-то бежал к дому. Это был Барни! Он подбежал к окну и начал лаять. Маша открыла дверь, и Барни бросился ей навстречу. Он прыгал от радости и лизнул Машу в щёку. Потом они пошли на кухню, чтобы перекусить. Барни съел свою порцию и лёг спать рядом с Машей. Ему снились большие косточки и вкусные сосиски…

Пример 2

Запрос: Albert Einstein

Ответ: (1879-1952) was born in Germany, but he moved to the United States and became a citizen of the country. He was an immigrant who had lived there for 30 years.

Albert Einstein

The story goes that when he arrived at his new home, he said "Kleine salzbrezchen mit Mahrmkohffe." This translates as "Rare smoked salmon with dark rye bread."

He then went on to become one of the most famous scientists in history.

This is a true story. It happened in 1933.

Пример 3

Запрос: The old clock in the hallway struck midnight, its chimes echoing through the empty mansion

Ответ: . The house was silent except for the sound of footsteps as a young woman walked down the hall and into the kitchen.

She opened the refrigerator and pulled out a carton of milk. She poured herself a glass and sat at the table to drink it. As she sipped her breakfast, she heard a noise from outside the window. She looked up and saw a man standing there with a tray of food. He smiled at her and said, “Good morning!” She smiled back and nodded her head. Then he left.

She finished her milk and went upstairs to get ready for work. When she came back downstairs, she found that someone had made pancakes and sausage. There were also eggs, bacon, and orange juice. It was all delicious! She ate everything and

И все истории так или иначе стали про еду! Это хорошая демонстрация того, что MoE-эксперты обладают тематической специализацией.

Надеемся, что наше исследование помогло увеличить знания о том, что происходит внутри загадочных языковых моделей, а именно MoE. Рассматривая группы экспертов между разными слоями, мы смогли понять, за что они отвечают. А наш метод управления потенциально помогает добиться более управляемых генераций. Мы планируем провести больше экспериментов и рассказать о них в будущем. Также предлагайте свои модификации и тестируйте — you are welcome!

Метрики

А пока что повторим, что мы выкладываем две модели в открытый доступ — base и instruct. Мы проверили серию замеров: среди российского open source GigaChat занимает высокие позиции по многим метрикам.

Base-модель

Будет проще всего сравнить метрики среди моделей в размерах около 7 млрд параметров. Вероятно, можно было бы выделить больше моделей, но мы решили сравниться T-lite-instruct-0.1, LLaMa-3.1-8B и Gemma-9B, которые одновременно сильны по метрикам и способны неплохо говорить на русском. Qwen же не попал в этот короткий список, хотя его метрики с отрывом выше всех. Из проблем: что его base-, что instruct-версия норовит начать отвечать на китайском, независимо от языка запроса. Ещё один недостаток base-версии Qwen: малое количество русских токенов в токенизаторе, около пары тысяч.

В этом зачёте моделей GigaChat обходит LLaMa 3.1 и получает серебро. Не сомневаемся, что в задачах именно русскоязычного домена GigaChat на ваших дообучениях сможет превзойти Gemma-9B благодаря большому объёму русскоязычных тестов в обучающем наборе.

Что важно отметить: GigaChat в почти 2 раза дешевле по стоимости инференса, чем LLaMa 3.1 и обходит ее по качеству. Ускорение касается еще и обучения (более 60%, но подробнее об этом в следующем году), поэтому тюн Гигачата на любые задачи будет намного эффективнее и быстрее, нежели LLaMa based моделей. А еще по внутренним тестам производительности на 1 GPU наша архитектура держит больше потоков, то есть отлично подходит для высоконагруженных систем. Это для тех, кто захочет использовать GigaChat в промышленных решениях!

Bench	T-lite-0.1	Llama-3.1-8B	GigaChat-20B-A3B-base	Gemma-9B
MMLU EN 5-shot	62.56	65.21	63.02	70.6
MMLU PRO EN 5-shot	32.19	35.7	31.41	42.85
MMLU RU 5-shot	55.51	54.1	58.38	62.57
BBH 3-shot	62.36	62.79	53.54	70.48
ARC-C 25-shot	58.19	54.69	61.69	68.34
TruthfulQA 0-shot (rougeL)	46.51	34.52	31.82	41.49
Winogrande 5-shot	78.45	77.43	75.85	79.4
Hellaswag 10-shot	82.21	81.85	81.91	82.5
GPQA 5-shot	0.25	23.44	25.22	30.36
MATH 4-shot (strict-match)	12.9	14.04	15.04	20.06
GSM8K 4-shot (strict-match)	67.93	51.4	59.06	68.99
HumanEval	16.46	25.61	32.32	37.2
AVG	47.96	48.4	49.11	56.24

Instruct-модель

В этот раз мы решили не проводить SbS-ы и много замеров на всевозможных аренах. Мы постарались выделить серию русскоязычных бенчмарков, которые могли бы оценить качество нашей чат-модели.

Во-первых, мы решили расширить табличку с метриками из статьи GigaChat MAX, теперь там есть все большие модели линейки GigaChat: Lite, Pro и Max.

	GigaChat-20B-A3B-instruct	GigaChat Pro v26.20	GigaChat MAX v26.20
Математические задачи
GSM8K	0,764	0,782	0,929
MATH 4-shot	0,462	0,446	0,53
Написание кода
HumanEval 0-shot	0,329	0,439	0,64
MBPP 0-shot	0,385	0,487	0,667
Общие знания
MMLU EN 5-shot	0,648	0,687	0,804
MMLU RU 5-shot (бенчмарк MMLU RU, но в формате 5-shot)	0,598	0,645	0,75
MMLU RU 1-shot	-	0,617	0,718
MMLU PRO EN 5-shot	0,348	0,431	0,589
RUBQ 0-shot	0,675	0,724	0,73
WINOGRANDE 4-shot	0,750	0,796	0,832
CyberMetric 0-shot	0,798	0,827	0,864
Следование инструкциям
IFEval 0-shot	0,411	0,566	0,721

Примечание по замеру метрик: GSM8k — это тест, который проверяет, как хорошо модели могут решать задачи с числами. В нашем исследовании мы использовали 5 шотов, чтобы оценить модель, и смотрели на последнее число в ответе. В оригинальное тесте ответ ищется по шаблону «### число». Тест Math тоже имеет разные версии, которые проверяют математические способности моделей. В нашем исследовании мы давали четыре примера и смотрели на последнее выражение в формате «\boxed{expression}». Затем оценивали результаты на совпадение с помощью библиотеки sympy.

Во-вторых, мы провели замер модели GigaChat-20B-A3B-instruct на бенчмарках MERA, Shlepa, ru-MMLU и арене ru-arena-general.

	T-lite-instruct-0.1	gemma-2-9b-it	GigaChat-20B-A3B-instruct
MERA	0,335	0,392	0,513
ru-MMLU 5-shot	0,555	0,626	0,598
Shlepa	0,36	0,388	0,482

Внимание, наш open source «маленькой» модели догнал на MERA GigaChat Pro (~~получается, GigaChat ждёт новый апгрейд?)~~, выбив средний балл 0,513.

Итоги

Мы планируем регулярно делиться с вами результатами наших трудов. Уже сегодня открыли доступ к base- и instruct-версиям GigaChat, и надеемся, что они помогут вам достичь новых высот в решении ваших задач!

Мы стали первыми в России, кто опубликовал модель с MoE-архитектурой и поделился с вами интересными наблюдениями о ней. Особую гордость нашей команды вызывает то, что мы сумели соединить в одной модели очень высокую скорость инференса вместе с высоким качеством. А также предложили идею для исследования особенностей этой архитектуры методом концентрации!

Резюмируя:

GigaChat-20B-A3B - первая открытая MoE модель в России. В мировой практике мало кому в мире удается обучать MoE архитектуры с хорошим качеством.
GigaChat обучен преимущественно на русском языке, поэтому в отличие от тюнов Qwen и LLaMa моделей не совершает грамматических и пунктуационных ошибок и не переключается на другие языки во время разговора
В GigaChat-20B-A3B дешевый инференс сочетается с хорошими показателями метрик
Модель отлично подходит для исследований, например, концентрации

У нас складывается традиция — делать тизеры на будущее. Поэтому ожидайте продолжения истории о МоЕ, мы к вам ещё вернёмся с новым рассказом! Также хочется напомнить о нашем Telegram‑канале, где мы чаще делимся разными историями из мира ML и DL.

Stay tuned! :)

Благодарности

Хотелось бы сердечно поблагодарить:

Команду pretrain за процесс исследований и оптимизаций новой архитектуры, обучение первой МоЕ-модели в России.
Команду alignment за множество экспериментов для подготовки сильной instruct-модели.
Команду RnD, без которой не было бы механизма концентрации.
А также Валентина Мамедова (@vltnmmdv), Евгения Косарева (@evgenijkkk), Илью Щукина, Михаила Колесова, Дениса Мазура, Александра Прошунина и Артёма Червякова за помощь в подготовке open source и написании текста, а также за полезные замечания и исправления!

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13 дек. 2024 г.

07.09.23 16:24 CherryTeam

Cherry Team atlyginimų skaičiavimo programa yra labai naudingas įrankis įmonėms, kai reikia efektyviai valdyti ir skaičiuoti darbuotojų atlyginimus. Ši programinė įranga, turinti išsamias funkcijas ir patogią naudotojo sąsają, suteikia daug privalumų, kurie padeda supaprastinti darbo užmokesčio skaičiavimo procesus ir pagerinti finansų valdymą. Štai keletas pagrindinių priežasčių, kodėl Cherry Team atlyginimų skaičiavimo programa yra naudinga įmonėms: Automatizuoti ir tikslūs skaičiavimai: Atlyginimų skaičiavimai rankiniu būdu gali būti klaidingi ir reikalauti daug laiko. Programinė įranga Cherry Team automatizuoja visą atlyginimų skaičiavimo procesą, todėl nebereikia atlikti skaičiavimų rankiniu būdu ir sumažėja klaidų rizika. Tiksliai apskaičiuodama atlyginimus, įskaitant tokius veiksnius, kaip pagrindinis atlyginimas, viršvalandžiai, premijos, išskaitos ir mokesčiai, programa užtikrina tikslius ir be klaidų darbo užmokesčio skaičiavimo rezultatus. Sutaupoma laiko ir išlaidų: Darbo užmokesčio valdymas gali būti daug darbo jėgos reikalaujanti užduotis, reikalaujanti daug laiko ir išteklių. Programa Cherry Team supaprastina ir pagreitina darbo užmokesčio skaičiavimo procesą, nes automatizuoja skaičiavimus, generuoja darbo užmokesčio žiniaraščius ir tvarko išskaičiuojamus mokesčius. Šis automatizavimas padeda įmonėms sutaupyti daug laiko ir pastangų, todėl žmogiškųjų išteklių ir finansų komandos gali sutelkti dėmesį į strategiškai svarbesnę veiklą. Be to, racionalizuodamos darbo užmokesčio operacijas, įmonės gali sumažinti administracines išlaidas, susijusias su rankiniu darbo užmokesčio tvarkymu. Mokesčių ir darbo teisės aktų laikymasis: Įmonėms labai svarbu laikytis mokesčių ir darbo teisės aktų, kad išvengtų baudų ir teisinių problemų. Programinė įranga Cherry Team seka besikeičiančius mokesčių įstatymus ir darbo reglamentus, užtikrindama tikslius skaičiavimus ir teisinių reikalavimų laikymąsi. Programa gali dirbti su sudėtingais mokesčių scenarijais, pavyzdžiui, keliomis mokesčių grupėmis ir įvairių rūšių atskaitymais, todėl užtikrina atitiktį reikalavimams ir kartu sumažina klaidų riziką. Ataskaitų rengimas ir analizė: Programa Cherry Team siūlo patikimas ataskaitų teikimo ir analizės galimybes, suteikiančias įmonėms vertingų įžvalgų apie darbo užmokesčio duomenis. Ji gali generuoti ataskaitas apie įvairius aspektus, pavyzdžiui, darbo užmokesčio paskirstymą, išskaičiuojamus mokesčius ir darbo sąnaudas. Šios ataskaitos leidžia įmonėms analizuoti darbo užmokesčio tendencijas, nustatyti tobulintinas sritis ir priimti pagrįstus finansinius sprendimus. Pasinaudodamos duomenimis pagrįstomis įžvalgomis, įmonės gali optimizuoti savo darbo užmokesčio strategijas ir veiksmingai kontroliuoti išlaidas. Integracija su kitomis sistemomis: Cherry Team programinė įranga dažnai sklandžiai integruojama su kitomis personalo ir apskaitos sistemomis. Tokia integracija leidžia automatiškai perkelti atitinkamus duomenis, pavyzdžiui, informaciją apie darbuotojus ir finansinius įrašus, todėl nebereikia dubliuoti duomenų. Supaprastintas duomenų srautas tarp sistemų padidina bendrą efektyvumą ir sumažina duomenų klaidų ar neatitikimų riziką. Cherry Team atlyginimų apskaičiavimo programa įmonėms teikia didelę naudą - automatiniai ir tikslūs skaičiavimai, laiko ir sąnaudų taupymas, atitiktis mokesčių ir darbo teisės aktų reikalavimams, ataskaitų teikimo ir analizės galimybės bei integracija su kitomis sistemomis. Naudodamos šią programinę įrangą įmonės gali supaprastinti darbo užmokesčio skaičiavimo procesus, užtikrinti tikslumą ir atitiktį reikalavimams, padidinti darbuotojų pasitenkinimą ir gauti vertingų įžvalgų apie savo finansinius duomenis. Programa Cherry Team pasirodo esanti nepakeičiamas įrankis įmonėms, siekiančioms efektyviai ir veiksmingai valdyti darbo užmokestį. https://cherryteam.lt/lt/
08.10.23 01:30 davec8080

The "Shibarium for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH. The Plot Thickens. Someone posted the actual transactions!!!! https://bscscan.com/tx/0xa846ea0367c89c3f0bbfcc221cceea4c90d8f56ead2eb479d4cee41c75e02c97 It seems the article is true!!!! And it's also FUD. Let me explain. Check this link: https://bscscan.com/token/0x5a752c9fe3520522ea88f37a41c3ddd97c022c2f So there really is a "Shibarium" token. And somebody did a rug pull with it. CONFIRMED. But the "Shibarium" token for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH.
24.06.24 04:31 tashandiarisha

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26.06.24 18:46 Jacobethannn098

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04.07.24 04:49 ZionNaomi

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13.07.24 21:13 michaelharrell825

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17.07.24 02:26 thompsonrickey

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27.08.24 12:50 James889900

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06.09.24 01:35 Celinagarcia

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06.09.24 01:44 Celinagarcia

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16.09.24 00:10 marcusaustin

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23.09.24 18:56 matthewshimself

At first, I was admittedly skeptical about Worldcoin (ref: https://worldcoin.org/blog/worldcoin/this-is-worldcoin-video-explainer-series), particularly around the use of biometric data and the WLD token as a reward mechanism for it. However, after following the project closer, I’ve come to appreciate the broader vision and see the value in the underlying tech behind it. The concept of Proof of Personhood (ref: https://worldcoin.org/blog/worldcoin/proof-of-personhood-what-it-is-why-its-needed) has definitely caught my attention, and does seem like a crucial step towards tackling growing issues like bots, deepfakes, and identity fraud. Sam Altman’s vision is nothing short of ambitious, but I do think he & Alex Blania have the chops to realize it as mainstay in the global economy.
01.10.24 14:54 Sinewclaudia

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02.10.24 22:27 Emily Hunter

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

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

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12.11.24 00:50 TERESA

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17.11.24 09:31 Vivianlocke223

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21.11.24 04:14 ronaldandre617

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21.11.24 08:02 Emily Hunter

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

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I never could have imagined the nightmare of losing access to my digital wallet. All of my cryptocurrency holdings were abruptly imprisoned, inaccessible, and appeared to be lost forever following a catastrophic hardware breakdown. Years of meticulous investment and careful saving were reduced to nothing more than strings of code that I could no longer control, and I could feel the dread and sorrow that swept through me at that very instant. Thankfully, during my worst moment, I came into (TRUST GEEKS HACK EXPERT), a professional service devoted to recovering lost or inaccessible digital data. With optimism, I went out to their team of skilled technologists, laying bare the full nature of my issue. What followed was a laborious, multi-step process that required an almost surgical level of digital forensics and Bitcoin skill. In order to create a thorough profile of my wallet's contents and activities, the (TRUST GEEKS HACK EXPERT) team first thoroughly examined the transaction history and metadata connected to it. Next, they implemented a series of advanced recovery techniques, using cutting-edge software tools to bypass the access barriers that had left me locked out. The entire process was shrouded in secrecy and discretion, with the (TRUST GEEKS HACK EXPERT) team working tirelessly to protect the confidentiality of my sensitive financial information. After what felt like an eternity of nervous anticipation, the day finally arrived when I received the triumphant notification – my wallet had been successfully restored, and all of my precious digital assets had been returned to my control. The sense of relief was indescribable, as I could finally breathe easy knowing that the fruits of my financial discipline had been safeguarded. While the experience of losing access to my wallet was undoubtedly traumatic, (TRUST GEEKS HACK EXPERT) intervention allowed me to emerge from the ordeal with my cryptocurrency holdings intact, and a renewed appreciation for the importance of proactive digital asset management. You can contact Them through EMAIL: [email protected] - TELEGRAM: TRUSTGEEKSHACKEXPERT
25.11.24 02:19 briankennedy

COMMENT ON I NEED A HACKER TO RECOVER MONEY FROM BINARY TRADING. HIRE FASTFUND RECOVERY
25.11.24 02:20 briankennedy

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26.11.24 23:12 rickrobinson8

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27.11.24 09:10 Michal Novotny

The biggest issue with cryptocurrency is that it is unregulated, wh ich is why different people can come up with different fake stories all the time, and it is unfortunate that platforms like Facebook and others only care about the money they make from them through ads. I saw an ad on Facebook for Cointiger and fell into the scam, losing over $30,000. I reported it to Facebook, but they did nothing until I discovered deftrecoup . c o m from a crypto community; they retrieved approximately 95% of the total amount I lost.
01.12.24 17:21 KollanderMurdasanu

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02.12.24 23:02 ytre89

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04.12.24 22:24 andreygagloev

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12.12.24 00:35 amandagregory

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27.12.24 00:21 swiftdream

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