architecture, and other stakeholders to define and deliver strategic software initiatives.\nIdentify, troubleshoot, and resolve the most complex software defects and issues.\nCreate and maintain technical documentation, including architectural designs and best practice guidelines.\nRepresent [Company Name] ...
"TinyLlama is a compact 1.1B language model pretrained on around 1 trillion tokens for approximately 3 epochs. Building on the architecture and tokenizer of Llama 2, TinyLlama leverages various advances contributed by the open-source community (e.g., FlashAttention), achieving better computational ...
检索增强生成 (RAG) 是 AI 和 NLP 中的一个变革性概念。通过协调检索和生成组件,RAG 解决了现有语言模型的局限性,并为更智能和上下文感知的 AI 交互铺平了道路。它能够无缝集成外部知识源并生成符合用户意图的响应,使 RAG 成为开发能够真正理解用户并以类似人类的方式与用户交流的 AI 系统的游戏规则改变者。 外...
检索增强生成 (RAG) 是 AI 和 NLP 中的一个变革性概念。通过协调检索和生成组件,RAG 解决了现有语言模型的局限性,并为更智能和上下文感知的 AI 交互铺平了道路。它能够无缝集成外部知识源并生成符合用户意图的响应,使 RAG 成为开发能够真正理解用户并以类似人类的方式与用户交流的 AI 系统的游戏规则改变者。 外...
Building on the architecture and tokenizer of Llama 2, TinyLlama leverages various advances contributed by the open-source community (e.g., FlashAttention), achieving better computational efficiency. Despite its relatively small size, TinyLlama demonstrates remarkable performance in a series of ...
Baichuan2isa next-generation artificial intelligence model developed by XYZ Corp. It handles complex natural language processing tasksandutilizes a novel neural network architecture. 压缩的原理也很简单,和MuitiQueryRetriever类似,都是依赖prompt实现的,详见:https://github.com/langchain-ai/langchain/blob/mast...
Recherche Azure AI fournit des entrées à l’invite LLM, mais n’entraîne pas le modèle. Dans l’architecture RAG, il n’y a pas d’entraînement supplémentaire. Le LLM est préentraîné à l’aide de données publiques, mais il génère des réponses augmentées par des ...
Voyage AI’s embedding models are the preferred embedding models for Anthropic. In addition to general-purpose embedding models, Voyage AI offers domain-specific embedding models that are tuned to a particular domain. RAG architecture and embedding ...
了解語料庫中的不同文件格式有助於您確定測試文件的數量和分類。 例如,如果您有季度報告的 PDF 和 Office Open XML 文件類型,則需要每種文件類型的測試文件。 了解文件類型還可以幫助您了解載入和分塊文件的技術要求,例如適合處理這些文件格式的特定庫。
The architecture of UltraRAG is composed of three parts:Frontend,Service, andBackend. The specifics are as follows: Backend Modules (Module Layer):Defines the key components in the RAG system, such as the knowledge base, retrieval model, and generation model, supporting users to customize flexibl...