This repository provides a PyTorch implementation of Toolformer, a language model capable of using external tools. It's designed for researchers and developers looking to enhance LLMs with API integration for tasks requiring real-time data or specific functionalities. The primary benefit is enabling LLMs to self-teach tool usage, improving their accuracy and applicability.

How It Works

The core approach involves fine-tuning a language model to generate API calls within its output. It uses a novel fitness score to filter generated outputs, selecting those that correctly embed and execute API calls. This filtered data is then used to fine-tune the model, teaching it to produce more accurate and useful tool-augmented text. The implementation supports custom tools and provides utilities for filtering API responses and invoking tools.

Quick Start & Requirements

• Install: pip install toolformer-pytorch
• Requirements: PyTorch, CUDA (implied by .cuda() calls), Python.
• Usage: The README provides detailed examples for integrating custom tools like a calendar API and demonstrates the fine-tuning and sampling process.

Highlighted Details

• Implements the core Toolformer methodology for LLM tool usage.
• Includes utilities for filtering API responses and invoking tools with results.
• Supports custom tool definitions and integration.
• Demonstrates fine-tuning process with example API calls.

Maintenance & Community

The project is sponsored by Stability.ai. The README mentions Enrico for initial commits and ChatGPT for assistance with regular expressions. There are no explicit links to community channels or roadmaps provided.

Licensing & Compatibility

The repository does not explicitly state a license. The code uses PyTorch, which is typically available under permissive licenses like BSD-3-Clause. However, the absence of a declared license means users should exercise caution regarding commercial use or closed-source linking until clarified.

Limitations & Caveats

The project is marked as "wip" (work in progress). Several "todo" items indicate ongoing development, including end-to-end training, error handling for API calls, and support for multiple tools in a single inference. The lack of a specified license is a significant caveat for adoption.