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Н Новости

OpenSource на поле против OpenAI:  Function Calls здесь и сейчас для самых маленьких… ресурсов

Введение

Можете скипнуть введение, если знаете:

Почему использовать Open Source лучше Open AI API

А для тех, кто остался: Давайте рассмотрим плюсы и минусы!

Плюсы:

  1. Бесплатные модели.

    На изображении представлен Khaby Lame
    На изображении представлен Khaby Lame
  2. Управление данными и приватность: локальное размещение моделей гарантирует, что ваши данные не покинут вашу систему.

  3. Доступность исходного кода позволяет лучше понимать и исправлять ошибки.

  4. Возможность fine-tuning для своих нужд.

  5. Поддержка сообщества с постоянными обновлениями.

  6. Отсутствие зависимости от вендоров и их политики.

Кроме того, можно использовать локально развернутую LLM вместе с OpenAI API.
Если, например, у вас заканчиваются средства на аккаунте или по какой-либо другой причине работа кода с OpenAI API приостанавливается, локальная модель может прийти на помощь. Это позволит вашему рабочему процессу продолжаться без перебоев.

Минусы:

  1. Необходимость самостоятельно контролировать поддержку работоспособности модели.

  2. Ответственность за развертку и настройку модели.

  3. Необходимость арендовать или приобретать вычислительные ресурсы.

  4. Параметры proprietary моделей обычно гораздо больше, и они могут быть более "умными".

  5. Необходимость иметь квалифицированных специалистов для управления и обслуживания модели.

Теоретические основы

Что это такое Function Calling?

В контексте больших языковых моделей Function Calling подразумевает способность LLM определять из запроса пользователя подходящую функцию для выполнения из доступного набора функций и правильные параметры для передачи этой функции. Вместо генерации обычных текстовых ответов, LLM для вызова функций обычно настраивается так, чтобы возвращать ответы со структурированными данными, чаще всего в формате JSON. Эти структурированные данные можно использовать для выполнения предопределенных функций, таких как извлечение данных из хранилищ данных или функциональных возможностей, получение данных в реальном времени или вызов сторонних API.

Отличия Function Calling от Tools

Данный вопрос неоднократно встречался на просторах интернета. Думаем, многие понимают различия, однако стоит прояснить.
Tools — это более широкое понятие, которое включает в себя Function Calling и многие другие инструменты. Например, Assistants, созданные с помощью Assistants API в OpenAI API, поддерживают не только Function Calling, но и File Search (встроенный инструмент RAG для обработки и поиска в файлах), Code Interpreter (позволяет писать и запускать код на Python, обрабатывать файлы и различные данные), а также создавать собственные тулы.

От теории к практике

Да, как уже стало понятно, тулы — это действительно очень крутой и мощный инструмент. Однако необходимо, чтобы кто-то ими воспользовался. В OpenAI API для этого существуют Assistants, но как это реализовать с локальными LLM-ками? Именно для этого мы здесь и собрались!

Модель, которая смогла: Mistral

Представляем вам лучшего работника месяца для нас: Mistral-7B-Instruct-v0.3:

Вместо крабсбургеров приготовит вам много проектов!
Вместо крабсбургеров приготовит вам много проектов!

Данная модель была выпущена 22 мая 2024 года, так что она может уже и не быть работником месяца, но мы начали с ней работать только сейчас, и для pet-проектов она довольно отлична.

Улучшения в Mistral-7B-v0.3

В Mistral-7B-v0.3 внесены следующие изменения по сравнению с Mistral-7B-v0.2:

  • Расширен словарный запас до 32 768 токенов

    Ранее размер словаря в v0.2 составлял 32 000 токенов. Может показаться, что увеличение на 768 токенов незначительно, однако эти дополнительные токены теперь могут охватывать редкие слова, новый жаргон или специфические термины.

    Для справки: Llama 3 имеет словарь в 128 256 токенов. В то время как GPT-4o превосходит их обоих, расширив словарь до 200 019 токенов по сравнению с 100 277 в GPT-4.

  • Поддерживается Tokenizer v3

  • Поддерживается Function Calling

    Самое интересное и то, зачем мы сюда пришли, — это способность вызывать функции. Теперь модель можно использовать на полную мощность и в различных приложениях, позволяя ей выполнять задачи, выходящие за рамки простого создания текста.

Сравнение с GPT-4o

Если вы готовы платить денюжку, то можно использовать Mistral AI API. Тут можно продешевить и получить почти такое же качество по генерации текста как у Open AI GPT-4o.

Вот вам pricing GPT-4o и моделей от Mistral:

Pricing GPT-4o
Pricing GPT-4o
Pricing моделей от Mistral AI
Pricing моделей от Mistral AI

Ну, а качество у Mistral Large 2 сходно с GPT-4o. Вот вам таблица с характеристиками моделей с этого сайта:

Сравнение характеристик моделей
Сравнение характеристик моделей

И еще вот эту:

Сравнение MMLU моделей
Сравнение MMLU моделей

Ну, собственно, Mistral Large 2 неплохо так догнала GPT-4o, а еще использовать её дешевле.

Подумайте, чей код приведен ниже: описание тулов с использованием Mistral AI API или же OpenAI API?

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_current_weather",
            "description": "Get the current weather",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city and state, e.g. San Francisco, CA",
                    },
                    "format": {
                        "type": "string",
                        "enum": ["celsius", "fahrenheit"],
                        "description": "The temperature unit to use. Infer this from the users location.",
                    },
                },
                "required": ["location", "format"],
            },
        }
    }
]

Правильный ответ: Это сработает и там, и там. Ребята из Mistral AI сделали почти точно такой же API, как у OpenAI.
Они же одинаковые, Наташ!
Прекрасно, то есть, если вы захотите перейти от использования Open AI API к Mistral AI API, часть кода может остаться неизменной.

Но мы сюда за бесплатным Open Source пришли, о каком API идет речь? В этом и суть, что их API работает и бесплатно, с моделькой развернутой на LM Studio или других аналогах.

Вот пример кода:

from mistralai.client import MistralClient
from mistralai.models.chat_completion import ChatMessage
from mistralai.models.chat_completion import ChatMessage
from mistralai.client import MistralClient
from mistralai.models.chat_completion import ChatMessage

def get_current_weather(location, format):  
    # Тело функции опускается 
    return {
        "location": location,
        "temperature": 23,
        "format": format
    }


model="MaziyarPanahi/Mistral-7B-Instruct-v0.3-GGUF", 
api_key = "lm-studio"
endpoint = "http://localhost:1234/"

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_current_weather",
            "description": "Get the current weather",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city and state, e.g. San Francisco, CA",
                    },
                    "format": {
                        "type": "string",
                        "enum": ["celsius", "fahrenheit"],
                        "description": "The temperature unit to use. Infer this from the users location.",
                    },
                },
                "required": ["location", "format"],
            },
        }
    },
]

messages = [
    ChatMessage(role="user", content="What's is the current weather in Minsk Belarus?")
]

client = MistralClient(api_key=api_key, endpoint=endpoint)
response = client.chat(model=model, messages=messages, tools=tools, tool_choice="any")
print(response)

И на этом месте возникает вопрос: то есть, ребята из Mistral AI предоставили и модель, и API — а кто тогда будет покупать их ключи? Да, не все модели в открытом доступе, плюс они разворачивают их на своих серверах, но нам-то и не ракета в космосе нужна. И всё кажется таким заманчивым, но то ли бесплатный сыр только в мышеловке, то ли руки кривоваты, но добиться вызова функций через их API так и не получилось.

Что ж, таким образом мы плавно и перешли к использованию великого для нас crewAI.

Наш спаситель
Наш спаситель

Еще больше нерабочих решений!

На текущий момент возникают следующие сложности:

  • Количество моделей, нативно поддерживающих функции, достаточно невелико (среди них выбранный ранее Mistral-7B-v0.3).

  • Фреймворки и платформы для локальной развёрки LLM зачастую не способствуют тому, чтобы данная фича даже в своём присутствии хотя бы изредка работала корректно.

В частности, некоторые модели из LocalAI имели в своём арсенале интересующую нас функциональность, однако на практике они показали удручающие результаты. Некоторые модели были не в состоянии вычленить нужные параметры или же сообщали о необходимости вызова функции в ситуациях, где контекст явно никак не связан с предлагаемой функцией.

Использование фреймворка AutoGen в совокупности с развернутой при помощи LM Studio модели с поддержкой Function Calling не дало никаких результатов. Агенты не использовали заявленные функции, а заставляли модель писать её самостоятельно. Естественно, подобное никуда не годится.

Вот репозиторий, на основе которого мы пытались соединить AutoGen и локальные LLM для реализации вызовов функций.

Величие сrewAI

В конце концов, замена фреймворка с AutoGen на аналогичный ему crewAI при всё той же развертке на LM Studio уже описанной модели Mistral-7B-Instruct-v0.3 принесла свои плоды.

Агент на основе данной LLM в действительности понял, какие у него есть функции и в каких ситуациях их необходимо применять. На этом месте мы и остановимся подробнее. Сейчас мы покажем, как повторить наши результаты шаг за шагом.

Вся система работала на обычном офисном ноутбуке с 16GB RAM и процессором AMD Ryzen 7 4700U без единого намёка на такую роскошь как GPU. О скорости работы в данной статье речи не идёт, но это это определённый плюс, ведь запустить свою небольшую LLM с крутыми фишками для личных целей сможет практически каждый желающий.

Теперь же к реализации:

  • Мы использовали Anaconda для создания виртуального окружения Python, LM Studio, установленный на ОС Windows. Для начала работы с CrewAI достаточно было установить всего две зависимости: pip install crewai crewai_tools.

  • Переходим к коду. Задача перед нами стоит следующая:
    1. Загрузить модель.
    2. Подвязать к ней агента.
    3. Описать функции-тулы и сообщить агенту о их существовании.
    4. Создать задачу.
    5. Запустить всю систему.

Создание объекта модели:

from langchain_community.chat_models.openai import ChatOpenAI

OPENAI_API_BASE_URL = "http://localhost:1234/v1"  # Адрес, по которому мы будем обращаться к модели в LM Studio
OPENAI_API_KEY = "NA"  # В базовой конфигурации LM Studio не важен
MODEL_NAME = "MaziyarPanahi/Mistral-7B-Instruct-v0.3-GGUF"  # Необязательно, важна лишь фактическая модель, которую развернули в LM Studio

default_llm = ChatOpenAI(
    openai_api_base=OPENAI_API_BASE_URL,
    openai_api_key=OPENAI_API_KEY,
    model_name=MODEL_NAME,
)

Создание функции-тулы для агента:

from crewai_tools import tool

# Аннотирование типов и docstring строго обязательны согласно документации CrewAI
# Формат docstring формально не важен, но тем подробнее объяснишь суть функции агенту, тем лучше
@tool("Summator") # Имя, под которым агент будет упоминать данную тулу
def find_sum(a: int, b: int) -> int: 
    """
    Function for finding the sum of two `int` numbers.
    Args:
        a (int): The first number.
        b (int): The second number.
    Returns:
        int: The sum of `a` and `b`.
    """
      return a + b

Создание агента:

from crewai import Agent

general_agent = Agent(
    role="Assistant",
    goal="Answer Questions",
    backstory="",
    allow_delegation=False,
    verbose=True,
    llm=default_llm,
    tools=[find_sum], # Именно здесь мы отдаём агенту возможные функции, которые он может применить
)

Осталось создать задачу агенту и запустить Crew:

from crewai import Crew, Task

task = Task(
    description="12345 + 54321",
    expected_output='Int number',
    agent=general_agent,
)

crew = Crew(agents=[general_agent], tasks=[task], verbose=2)
result = crew.kickoff()
print(result)

Предоставляем результаты запуска агента, использующего описанную функцию подсчёта суммы чисел:

Как можно заметить, Function Call в действительности сработала как положено: модель решила, что необходимо вызвать внешнюю функцию, отдала агенту параметры, а тот в своё очередь напрямую выполнил соответствующий код и отдал ответ модели.
Как можно заметить, Function Call в действительности сработала как положено: модель решила, что необходимо вызвать внешнюю функцию, отдала агенту параметры, а тот, в свою очередь, напрямую выполнил соответствующий код и отдал ответ модели

То, что мы изначально хотели получить и даже лучше! (ну почти...читайте далее).

Теперь предоставим чуть более интересный пример, переписав функцию так, чтобы она принимала в качестве своего аргумента список целых чисел для суммирования. У моделей часто возникают проблемы с математическими расчётами, поэтому попробуем частично исправить этот недочёт.

Переписанная тула:

@tool("Summator")

def find_sum(nums: List[int]) -> int:
    """
    Function for finding the sum of array of numbers.
    Args:
        nums (List[int]): Array of numbers to find their sum.
    Returns:
        int: The sum of all numbers in array.
    """
    return sum(nums)

Ниже представлены результаты запроса без использования каких-либо функций и тулов и с применением тулы:

В первом случае сама LMM, к сожалению, не справилась с поставленной задачей
В первом случае сама LMM, к сожалению, не справилась с поставленной задачей
Во втором случае ей оказалось вполне по силам выделить список чисел и делегировать ответственность по их суммированию на внешнего исполнителя. Просто превосходно!
Во втором случае ей оказалось вполне по силам выделить список чисел и делегировать ответственность по их суммированию на внешнего исполнителя.
Просто превосходно!

Хорошо, сложение чисел - это всё круто и прикольно, однако давайте предоставим более интересный пример. Реализуем аналог демонстрационной функции из документации OpenAI по запросу погоды на местноти.

В третий раз, код самой функции с использованием Open-Meteo API:

@tool("Weather API")
def get_current_weather(location: str) -> str:
    """
    Function to get current weather in provided city
    Args:
        location (str): The target city.
    Returns:
        str: Weather info as JSON string if answer is valid or error message
    """
    CITY_LATLONG_URL = "https://geocoding-api.open-meteo.com/v1/search?name={}&count=1&language=en&format=json"
    url = CITY_LATLONG_URL.format(location)

    response = requests.get(url)
    if response.status_code == 200:
        try:
            data = response.json()["results"][0]
        except KeyError:
            return f"ERROR: Nonexisting city {location}"
        latitude = data["latitude"]
        longitude = data["longitude"]
    else:
        return f"ERROR: Failed to get city location {location}"
    
    WEATHER_CURRENT_URL = "https://api.open-meteo.com/v1/forecast?latitude={}&longitude={}&current=temperature_2m,relative_humidity_2m,apparent_temperature,rain,showers,snowfall,wind_gusts_10m,wind_direction_10m&wind_speed_unit=ms&timezone=auto"
    url = WEATHER_CURRENT_URL.format(latitude, longitude)
    
    response = requests.get(url)
    if response.status_code == 200:
        return str(response.json())
    else:
        return f"ERROR: Failed to get current weather in {location}"

Пришлось также немного видоизменить описание агента и его задачи:

general_agent = Agent(
    role="Wheather Assistant",
    goal="Answer Questions and reformat data as human-readable form. Only use tools to get weather info, formatting is your own task",
    backstory="",
    allow_delegation=False,
    verbose=True,
    llm=default_llm,
    tools=[get_current_weather],
)

task = Task(
    description="What is the weather in Minsk now?",
    expected_output='Weather description',
    agent=general_agent,
)

Спустя некоторое время ожидания, окончательный результат перед нами:

Заметим, что на момент тестирования функции погода в Минске была именно такой
Заметим, что на момент тестирования функции погода в Минске была именно такой, т.е. Open-Meteo API работает

Агент сработал корректно, результат получен, все довольны — мир, дружба, жвачка! Однако... есть и небольшая ложка дёгтя:

  • При трёх одновременно загруженных в агента тулах модель стала путаться и запускать функции, совершенно не касающиеся текущего запроса, с выдуманными параметрами. Вполне вероятно, что это можно нивелировать гораздо более детальным пояснением сути тулов и в каких ситуациях их нужно вызывать.

  • Наличие тулов склоняет модель к лени. В первый раз после получении данных о погоде LLM решила вызвать функцию для форматирования JSON в читабельный текст, которой даже не существовало, вместо того, чтобы выполнить свою основную задачу самостоятельно. Исправить это удалось прописыванием более чёткого ТЗ для модели.

В отличие от OpenAI API и в целом от классического Function Calling, агенты crewAI также берут на себя обработку ответа модели. И пока в других случаях нам возвращается JSON с параметрами функции, которые необходимо так или иначе обрабатывать вручную, CrewAI задвигает это всё под капот, вызывая функцию самостоятельно.

Заключение и подводные камешки

Куда же без камешков
Куда же без камешков

У этого подхода есть как плюсы, так и минусы.

Плюсы: даже с RAM в 8 ГБ можно запустить квантованную Mistral размером 3-5 ГБ и радоваться вызовам функций.

Минусы: Что ж...заглянем под капот. Вот как выглядел POST запрос к модели на LLM:

{
  "messages": [
    {
      "content": "You are Wheather Assistant. \nYour personal goal is: Answer Questions and reformat data as human-readable form. Only use tools to get weather info, formatting is your own task\nYou ONLY have access to the following tools, and should NEVER make up tools that are not listed here:\n\nTool Name: Weather API(*args: Any, **kwargs: Any) -> Any\nTool Description: Weather API(location: 'string') -      Function to get current weather in provided city      Args:         location (str): The target city.      Returns:         str: Weather info as JSON string if answer is valid or error message      \nTool Arguments: {'location': {'title': 'Location', 'type': 'string'}}\n\nUse the following format:\n\nThought: you should always think about what to do\nAction: the action to take, only one name of [Weather API], just the name, exactly as it's written.\nAction Input: the input to the action, just a simple python dictionary, enclosed in curly braces, using \" to wrap keys and values.\nObservation: the result of the action\n\nOnce all necessary information is gathered:\n\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n\nCurrent Task: What is the weather in Minsk now?\n\nThis is the expect criteria for your final answer: Weather description \n you MUST return the actual complete content as the final answer, not a summary.\n\nBegin! This is VERY important to you, use the tools available and give your best Final Answer, your job depends on it!\n\nThought:\n",
      "role": "user"
    }
  ],
  "model": "MaziyarPanahi/Mistral-7B-Instruct-v0.3-GGUF",
  "logprobs": false,
  "n": 1,
  "stop": [
    "\nObservation"
  ],
  "stream": true,
  "temperature": 0.7
}

А вот так выглядел запрос к модели для нерабочего варианта вызова функции через Mistral AI API:

{
  "messages": [
    {
      "role": "user",
      "content": "What's is the current weather in Minsk Belarus?"
    }
  ],
  "model": [
    "MaziyarPanahi/Mistral-7B-Instruct-v0.3-GGUF"
  ],
  "tools": [
    {
      "type": "function",
      "function": {
        "name": "get_current_weather",
        "description": "Get the current weather",
        "parameters": {
          "type": "object",
          "properties": {
            "location": {
              "type": "string",
              "description": "The city and state, e.g. San Francisco, CA"
            },
            "format": {
              "type": "string",
              "enum": [
                "celsius",
                "fahrenheit"
              ],
              "description": "The temperature unit to use. Infer this from the users location."
            }
          },
          "required": [
            "location",
            "format"
          ]
        }
      }
    }
  ],
  "stream": false,
  "tool_choice": "any"
}

В случае сrewAI инструменты добавляются в основной промпт, а в случае Mistral AI API они передаются отдельным параметром в запросе. Поэтому мы предполагаем (но не утверждаем), что модель могла плохо справляться в случае, когда мы ей давали несколько функций, из-за перегруженности контента промпта. В целом, передача всей информации о каждой функции просто в промпт выглядит слегка как костыль и даже не совсем как Function Call, но несмотря на такие подводные камни, всё выглядит довольно хорошо: параметры функций вычленяются правильно, функции вызываются сами — а что ещё нужно?

Мы продолжаем искать различные более лучшие способы реализовывать вызов функций, создание агентов и прочие крутые фишки на Open Source решениях. Поэтому мы будем рады любому комментарию или предложению. А пока мы понимаем, что на поле между Open Source и OpenAI одерживает победу второй игрок, но первый уже совсем не аутсайдер.

Всем спасибо за прочтение данной статьи!

Авторы статьи

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  • 14.10.25 08:46 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

  • 15.10.25 18:07 crypto

    Cryptocurrency's digital realm presents many opportunities, but it also conceals complex frauds. It is quite painful to lose your cryptocurrency to scam. You can feel harassed and lost as a result. If you have been the victim of a cryptocurrency scam, this guide explains what to do ASAP. Following these procedures will help you avoid further issues or get your money back. Communication with Marie ([email protected] and WhatsApp: +1 7127594675) can make all the difference.

  • 15.10.25 21:52 harristhomas7376

    "In the crypto world, this is great news I want to share. Last year, I fell victim to a scam disguised as a safe investment option. I have invested in crypto trading platforms for about 10yrs thinking I was ensuring myself a retirement income, only to find that all my assets were either frozen, I believed my assets were secure — until I discovered that my BTC funds had been frozen and withdrawals were impossible. It was a devastating moment when I realized I had been scammed, and I thought my Bitcoin was gone forever, Everything changed when a close friend recommended the Capital Crypto Recover Service. Their professionalism, expertise, and dedication enabled me to recover my lost Bitcoin funds back — more than €560.000 DEM to my BTC wallet. What once felt impossible became a reality thanks to their support. If you have lost Bitcoin through scams, hacking, failed withdrawals, or similar challenges, don’t lose hope. I strongly recommend Capital Crypto Recover Service to anyone seeking a reliable and effective solution for recovering any wallet assets. They have a proven track record of successful reputation in recovering lost password assets for their clients and can help you navigate the process of recovering your funds. Don’t let scammers get away with your hard-earned money – contact Email: [email protected] Phone CALL/Text Number: +1 (336) 390-6684 Contact: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

  • 15.10.25 21:52 harristhomas7376

    "In the crypto world, this is great news I want to share. Last year, I fell victim to a scam disguised as a safe investment option. I have invested in crypto trading platforms for about 10yrs thinking I was ensuring myself a retirement income, only to find that all my assets were either frozen, I believed my assets were secure — until I discovered that my BTC funds had been frozen and withdrawals were impossible. It was a devastating moment when I realized I had been scammed, and I thought my Bitcoin was gone forever, Everything changed when a close friend recommended the Capital Crypto Recover Service. Their professionalism, expertise, and dedication enabled me to recover my lost Bitcoin funds back — more than €560.000 DEM to my BTC wallet. What once felt impossible became a reality thanks to their support. If you have lost Bitcoin through scams, hacking, failed withdrawals, or similar challenges, don’t lose hope. I strongly recommend Capital Crypto Recover Service to anyone seeking a reliable and effective solution for recovering any wallet assets. They have a proven track record of successful reputation in recovering lost password assets for their clients and can help you navigate the process of recovering your funds. Don’t let scammers get away with your hard-earned money – contact Email: [email protected] Phone CALL/Text Number: +1 (336) 390-6684 Contact: [email protected] Website: https://recovercapital.wixsite.com/capital-crypto-rec-1

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