Hugging Face introduces a unified tool use API across multiple model families, making it easier to implement tool use in language models.
Hugging Face has extended chat templates to support tools, offering a unified approach to tool use with the following features:
- Defining tools: Tools can be defined using JSON schema or Python functions with clear names, accurate type hints, and complete docstrings.
- Adding tool calls to the chat: Tool calls are added as a field of assistant messages, including the tool type, name, and arguments.
- Adding tool responses to the chat: Tool responses are added as tool messages containing the tool name and content.
Hugging Face has extended chat templates to support tools, offering a unified approach to tool use with the following features:
- Defining tools: Tools can be defined using JSON schema or Python functions with clear names, accurate type hints, and complete docstrings.
- Adding tool calls to the chat: Tool calls are added as a field of assistant messages, including the tool type, name, and arguments.
- Adding tool responses to the chat: Tool responses are added as tool messages containing the tool name and content.
This article discusses how to overcome limitations of retrieval-augmented generation (RAG) models by creating an AI assistant using advanced SQL vector queries. The author uses tools such as MyScaleDB, OpenAI, LangChain, Hugging Face and the HackerNews API to develop an application that enhances the accuracy and efficiency of data retrieval process.
Learn how to build an open LLM app using Hermes 2 Pro, a powerful LLM based on Meta's Llama 3 architecture. This tutorial explains how to deploy Hermes 2 Pro locally, create a function to track flight status using FlightAware API, and integrate it with the LLM.
