🌐 Website • 📜 Paper • 🤗 HF Models • 🐱 GitHub
🐦 Twitter • 💬 Reddit • 🍀 Unofficial Blog

🔥 News

• [2023-10-08] All ToRA models released at HuggingFace!
• [2023-09-29] ToRA paper, repo, and website released.

💡 Introduction

ToRA, or Tool-Integrated Reasoning Agent, is a remarkable set of models designed to tackle challenging mathematical reasoning problems. Think of ToRA as a multi-talented mathematician who not only solves problems but also collaborates with sophisticated tools, like avanzed calculators and symbolic solvers. By marrying the analytical reasoning abilities of a human with the computational power of machines, ToRA aims to redefine how we engage with complex mathematics.

📐 Model Performance

To give you an idea of how ToRA performs compared to other leading models, here’s a snapshot of its accuracy on the GSM8k and MATH tasks:

Model                  | Size | GSM8k | MATH | AVG@10 Math Tasks
-----------------------|------|-------|------|--------------------
GPT-4                  | -    | 92.0  | 42.5 | 78.3
ToRA-7B                | 7B   | 68.8  | 40.1 | 62.4
ToRA-Code-7B          | 7B   | 72.6  | 44.6 | 66.5
ToRA-Code-34B*        | 34B  | 80.7  | 51.0 | 74.8
ToRA-70B               | 70B  | 84.3  | 49.7 | 76.9

In the above table, think of these models as being participants in a math competition. Their scores represent how well they solve problems. For instance, ToRA-Code-34B is like that overachiever who has cracked the 50% accuracy mark in a notoriously tricky math exam called the MATH dataset.

⚡️ Training Process

The models are trained on an extensive dataset known as ToRA-Corpus 16k, which collects reasoning trajectories from previous successes. The training employs imitation learning to finely tune the models and to improve reasoning behaviors through a technique called *output space shaping*. For a deeper dive into these methodologies, check out the paper.

🪁 Inference and Evaluation

For anyone interested in testing their own models or understanding the evaluation process, you can find the necessary inference and evaluation code in the GitHub repo.

🛠 Troubleshooting Ideas

If you run into challenges while using ToRA models, here are a few troubleshooting ideas:

• Ensure your environment meets all dependencies listed in the GitHub repository.
• If you experience performance issues, try adjusting your model’s parameters or switching different input types to see if they yield better results.
• Consult the community on platforms like Reddit for tips and shared experiences.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.