This article is a comprehensive beginner's guide to Large Language Models (LLMs), explaining their core principles (Transformer architecture, self-attention), prompt engineering basics, and how to call LLM APIs (OpenAI, DeepSeek) with Python examples. It highlights the statistical nature of LLMs, their limitations, and practical tips for effective interaction.
原文翻译:
本文是一篇面向初学者的全面大语言模型(LLM)入门指南,解释了其核心原理(Transformer架构、自注意力机制)、提示词工程基础,以及如何通过Python调用LLM API(OpenAI、DeepSeek)。文章强调了LLM的统计本质、局限性以及有效交互的实用技巧。
AutoAgents is a modular, multi-agent framework in Rust for building intelligent systems, featuring type-safe agents, structured tool calling, configurable memory, and pluggable LLM backends including OpenAI, Anthropic, DeepSeek, and local providers like Ollama and llama.cpp.
原文翻译:
AutoAgents是一个基于Rust的模块化多智能体框架,用于构建智能系统,具有类型安全代理、结构化工具调用、可配置内存和可插拔的LLM后端,支持OpenAI、Anthropic、DeepSeek等云端提供商以及Ollama、llama.cpp等本地提供商。
DeepSeek-V4 is a preview of the next-generation large language model with 1M context, leading open-source performance in knowledge, reasoning, and agent capabilities. It comes in Pro and Flash versions, both open-sourced with dual reasoning modes.
原文翻译:
DeepSeek-V4是新一代大语言模型预览版,拥有百万上下文,在知识、推理和Agent能力方面达到开源领先水平。提供Pro和Flash两个版本,均已开源并支持双模式推理。
[Original Summary]
DeepSeek officially released the preview version of DeepSeek-V4, supporting 1M token context, open-sourcing the model, and offering two versions (Pro & Flash) with enhanced Agent capabilities, marking a milestone for open-source models to rival top closed-source models.
原文翻译:
DeepSeek正式发布DeepSeek-V4预览版,支持百万token上下文,开源模型,提供Pro与Flash两个版本,并增强Agent能力,标志着开源模型首次比肩顶级闭源模型。