README.md

---
license: apache-2.0
language:
  - en
task_categories:
  - text-generation
  - question-answering
tags:
  - math
  - synthetic
  - instruction-tuning
  - sft
  - arithmetic
  - catastrophic-forgetting-prevention
size_categories:
  - 10M<n<100M
---

# 🏆 Legend-Math Dataset

Welcome to the **Legend-Math** dataset! This is a massive, highly optimized, and meticulously generated dataset containing **10 Million rows** of diverse mathematical problems. It is designed to transform any Large Language Model (LLM) into a mathematical genius through Supervised Fine-Tuning (SFT).

## 📊 Dataset Overview
- **Name:** Legend-Math
- **Size:** 10,000,000 (10 Million) Records
- **Total Tokens:** ~350 Million Tokens (Approx. 35 tokens per row)
- **Format:** JSONL (OpenAI ChatML / Messages format)
- **Language:** English / Universal Math
- **Use Case:** Supervised Fine-Tuning (SFT), Continuous Pre-Training (CPT)

---

## 🧠 What Will the Model Learn? (Capabilities)
By fine-tuning your model on **Legend-Math**, it will master:
1. **Basic Arithmetic:** High-accuracy Addition, Subtraction, Multiplication, Division, and Modulo operations (+, -, *, /, %).
2. **Multi-Step Equations:** Solving complex BODMAS/PEMDAS expressions systematically.
3. **Advanced Mathematics:** 
   - Exponential calculations and powers (e.g., $x^y$).
   - Exact Geometry calculations using `Pi` (π).
   - High-precision Square Roots.
   - Trigonometry (Sine, Cosine, Tangent).
4. **Precision & Accuracy:** The answers are generated via Python's execution engine, ensuring **0% hallucination** in the dataset's ground truth.

---

## 🚀 Model SFT Capability (How large a model can you train?)
With **~350 Million high-quality instruction-response tokens**, this dataset is powerful enough to fine-tune models of virtually any size:
- **Small Models (1B - 3B):** Perfect for making edge-device math assistants (e.g., TinyLlama, Qwen-1.5-1.8B).
- **Medium Models (7B - 14B):** Ideal for robust SFT on models like Llama-3-8B, Mistral-7B, or Qwen-7B. It will easily push their math benchmarks (GSM8K, MATH) to top-tier levels.
- **Large Models (30B - 70B+):** You can safely use this for continuous pre-training or aggressive SFT on models like Llama-3-70B or Mixtral to create a specialized, world-class Math Oracle.

---

## 🛡️ Why Choose Legend-Math? (The Secret Sauce)

### 1. Zero Catastrophic Forgetting 🧠💡
One of the biggest problems with fine-tuning a model heavily on Math is **Catastrophic Forgetting**—the model becomes great at math but forgets how to hold a normal conversation or loses its natural language charm. 
**Legend-Math solves this natively!** Every 50,000th row in this dataset injects a high-quality programming joke. This periodic language-shift acts as a memory refresher, ensuring the model retains its conversational skills, humor, and general language abilities while mastering math.

### 2. Ready-to-Train Format 🛠️
No data wrangling required. The dataset is already formatted in the exact schema expected by modern fine-tuning libraries (Axolotl, LLaMA-Factory, Unsloth, Hugging Face TRL).

### 3. Computationally Perfect 🎯
Unlike scraped datasets that contain human errors or LLM hallucinations, every single math problem here was dynamically generated and computed.

---

## 📂 Dataset Structure

Each record in the dataset is structured as follows:

```json
{
  "id": "6790b369d262461abba8c3f3de1425d9",
  "date": "2026-06-30 09:29:40",
  "tokens": 14,
  "messages": [
    {
      "role": "user",
      "content": "Calculate the exact area of a circle with radius 45. Use pi."
    },
    {
      "role": "assistant",
      "content": "6361.7251"
    }
  ]
}