This repo is a 4-bit quantized GPTQ implementation of jeffrey-fong/invoker-13b.

Invoker is a suite of large language models based on Llama-2 and is finetuned to plan between calling functions and providing responses directly. It behaves similar to OpenAI's function-calling models which intelligently chooses the best function to call among a list of functions provided to it and summarizes the function responses.

This model stands out for its ability to plan between function calls and returning model responses directly. The fine-tuning process was performed with a 4096 sequence length on an 2x A100 80GB machine.

For more details, refer to https://github.com/jeffrey-fong/Invoker

The prompt to the model consists of a list of available of functions to call and the chat messages

You must provide the list of functions to the model. All functions passed in should be in the same JSON format as OpenAI function-calling. If no functions are to be passed to the model, provide None in the Available Functions Field.

or

The model was trained using QLora which significantly reduces the computational resources required. Training was also accelerated using DeepSpeed Zero Stage 2 which provides fast data parallelism.

We use a variety of sources when building our training dataset. All the datasets are carefully chosen to improve both the conversational and function-calling capability of the model.

ToolBench is an open-source, large-scale and high quality instruction tuning SFT dataset to facilitate the training of LLMs with general tool-use capability. It consists of multi-turn conversations where the assistant, who is presented with several potential functions to call, will call one or multiple functions before returning its response to the user. We had undergone rigorous cleaning of the data where we

ShareGPT-34K is a filtered dataset containing high quality multi-turn conversations between a user and an assistant. Some of the assistant responses are generated from OpenAI's GPT-3.5-Turbo while some are from GPT-4.

OASST1 is a human-generated and human-annotated assistant-style conversation corpus. We filtered out the conversations in English.

All the datasets used are under Apache-2.0 License. Therefore, this dataset will also be under the same license.