---
title: LLM & Arithmetic
emoji: 🔢
colorFrom: blue
colorTo: green
sdk: docker
pinned: false
short_description: Explore LLM arithmetic and next-prime datasets.
---

# LLM & Arithmetic

Explore LLM arithmetic and next-prime experiments across raw, prompted, and tool modes.

Streamlit explorer for two public Hugging Face datasets:

- [`hoololi/llm-calculations`](https://huggingface.co/datasets/hoololi/llm-calculations)
- [`hoololi/llm-next-prime`](https://huggingface.co/datasets/hoololi/llm-next-prime)

The app compares how LLMs behave on arithmetic-like tasks across three modes:

- **raw**: direct answer;
- **prompted**: task-oriented system prompt;
- **tool**: external deterministic tool available to the model.

## Features

- top-level tabs for **Calculations** and **Next Prime**;
- nested **Statistics** and **Detailed results** views for each dataset;
- model comparison across raw, prompted, and tool modes;
- charts for accuracy, latency, cost, tokens, and category/magnitude breakdowns;
- tool-pipeline summaries where relevant;
- side-by-side inspection of two model × mode results for the same task;
- quick filters for wrong answers, run failures, tool issues, long answers, slowest answers, fastest answers, and high-cost rows;
- OpenRouter model metadata snapshot display when available.

The app loads the datasets directly from Hugging Face and is containerized with Docker.

To refresh OpenRouter metadata, update `model_families.json` if needed and run:

```bash
python fetch_openrouter_model_info.py
```