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painterly    音标拼音: [p'entɚli] [p'enɚli]
a. 画家的,绘画的,强调色彩的

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英文字典中文字典相关资料:


  • Customizing an LLM Tool — Prompt flow documentation
    In this document, we will guide you through the process of customizing an LLM tool, allowing users to seamlessly connect to a large language model with prompt tuning experience using a PromptTemplate
  • GitHub - idlirshkurti LLM-Orchestrator: A Python-based . . .
    To run the agent using ChatGPT-3 5 or another specified model, use the following command: This will execute the agent with a predefined query You can modify the query and model settings in the noxfile py Check types and format code with: To run all tasks sequentially: Here’s an example of how you might run the agent in a Python script directly:
  • Local LLM Orchestration with LangChain and Ollama
    We will demonstrate how LangChain serves as an orchestration layer, simplifying the management of local models provided by Ollama This approach enables developers to build applications that
  • How to Customize LLMs with Ollama | by Sumuditha . . . - Medium
    Ollama offers a compelling solution for large language models (LLMs) with its open-source platform, user-friendly interface, and local model execution Its customization features allow users to
  • How to Customize LLM Models with Ollamas Modelfile
    Ollama modelfile is the blueprint to create and share models with Ollama This guide will show you how to customize your own models, and interact with them via the command line or Web UI
  • How to use chat models to call tools | ️ LangChain
    LangChain implements standard interfaces for defining tools, passing them to LLMs, and representing tool calls This guide will cover how to bind tools to an LLM, then invoke the LLM to generate these arguments For a model to be able to call tools, we need to pass in tool schemas that describe what the tool does and what it's arguments are
  • Building an LLM orchestration framework using Pydantic
    The orchestrators oversee the execution of LLM tasks, integrate feedback mechanisms to improve output quality, and leverage memory stores to enhance contextual understanding By storing past





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