Инструмент, который поможет добавить рассждуения в ваши LLMпроекты , подобно OpenAI o1 и deepseek R1.
✨ Функции: 🧠 Пошаговые рассуждения: Больше никаких ответов из «черного ящика»! Узнайте, как именно мыслит ваш LLM, по аналогии с O1. 🔄 Прогресс в реальном времени: позволяет наблюдать за ходом рассуждений с помощью плавных анимаций 🎯 Поддержка множества LLMпровайдеров: Работает со всеми провайдерами LiteLLM 🎮Streamlit: Удобный пользовательский интерфейс 🛠️ Поддердка CLI: для тех, кто любит возиться с командной строкой. 📊 Проверка уверенности ответа: Узнайте, насколько уверен ваш LLM в каждом шаге рассуждений.
⭐️Установка: pip install llm-reasoner
Пример с кодом:
from llm_reasoner import ReasonChain import asyncio
async def main(): # Create a chain with your preferred settings chain = ReasonChain( model="gpt-4", # Choose your model min_steps=3, # Minimum reasoning steps temperature=0.2, # Control creativity timeout=30.0 # Set your timeout )
# Watch it think step by step! async for step in chain.generate_with_metadata("Why is the sky blue?"): print(f"\nStep {step.number}: {step.title}") print(f"Thinking Time: {step.thinking_time:.2f}s") print(f"Confidence: {step.confidence:.2f}") print(step.content)
Инструмент, который поможет добавить рассждуения в ваши LLMпроекты , подобно OpenAI o1 и deepseek R1.
✨ Функции: 🧠 Пошаговые рассуждения: Больше никаких ответов из «черного ящика»! Узнайте, как именно мыслит ваш LLM, по аналогии с O1. 🔄 Прогресс в реальном времени: позволяет наблюдать за ходом рассуждений с помощью плавных анимаций 🎯 Поддержка множества LLMпровайдеров: Работает со всеми провайдерами LiteLLM 🎮Streamlit: Удобный пользовательский интерфейс 🛠️ Поддердка CLI: для тех, кто любит возиться с командной строкой. 📊 Проверка уверенности ответа: Узнайте, насколько уверен ваш LLM в каждом шаге рассуждений.
⭐️Установка: pip install llm-reasoner
Пример с кодом:
from llm_reasoner import ReasonChain import asyncio
async def main(): # Create a chain with your preferred settings chain = ReasonChain( model="gpt-4", # Choose your model min_steps=3, # Minimum reasoning steps temperature=0.2, # Control creativity timeout=30.0 # Set your timeout )
# Watch it think step by step! async for step in chain.generate_with_metadata("Why is the sky blue?"): print(f"\nStep {step.number}: {step.title}") print(f"Thinking Time: {step.thinking_time:.2f}s") print(f"Confidence: {step.confidence:.2f}") print(step.content)
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