|Gorilla LLM Connected with Massive APIs
Gorilla is a LLM that can provide appropriate API calls. It is trained on three massive machine learning hub datasets: Torch Hub, TensorFlow Hub and HuggingFace.
The model is designed to extend Large Language Model (LLM) Chat Completion feature to formulate executable APIs call given natural language instructions and API context.
|Function Calling Mistral 7B
Function calling Mistral extends the HuggingFace Mistral 7B Instruct model with function calling capabilities.
The model responds with a structured json argument with the function name and arguments.
Functionary is a language model that can interpret and execute functions/plugins.
The model determines when to execute a function and can understand its output. It only triggers functions as needed. Function definitions are given as JSON Schema Objects, similar to OpenAI GPT function calls.
Based on Llama 2.
Invoker is a suite of large language models based on Llama-2 and is finetuned to plan between calling functions and providing responses directly
|OpenAI Cookbook: How to call functions with chat models
|This notebook covers how to use the Chat Completions API in combination with external functions to extend the capabilities of GPT models./td>
|OpenAI Function Calling Documentation
|In an API call, you can describe functions to gpt-3.5-turbo-0613 and gpt-4-0613, and have the model intelligently choose to output a JSON object containing arguments to call those functions. The Chat completions API does not call the function; instead, the model generates JSON that you can use to call the function in your code.
|OpenAI functions with LangChain
|Leverage OpenAI Functionc calls with LangChain
|LangChain Agents Documentation
|The core idea of agents is to use an LLM to choose a sequence of actions to take. In chains, a sequence of actions is hardcoded (in code). In agents, a language model is used as a reasoning engine to determine which actions to take and in which order.
|LlamaIndex Agents Documentation
|An “agent” is an automated reasoning and decision engine. It takes in a user input/query and can make internal decisions for executing that query in order to return the correct result.
|Augmented Language Models: a Survey
|This survey reviews works in which language models (LMs) are augmented with reasoning skills and the ability to use tools.