AIME-God v5.2 "Elite" is a specialized reasoning model fine-tuned for high-level competitive mathematics (AIME, Olympiad, AMC). It was trained on the "Elite Turbo" dataset—a curated collection of 28,500 high-density reasoning traces designed to master the most challenging contest problems.

👤 Credits: Abdullah Fowzan

This model and the underlying "God-Mode" dataset generation architecture were developed by Abdullah Fowzan.

🚀 Model Highlights

Architecture: Qwen 2.5 1.5B-Instruct (4-bit QLoRA)
Specialization: AIME Problems 11-15 and Tier-15 Olympiad methods.
Training Strategy: 1.5B instruction-tuned base model + Elite reasoning traces (28.5k samples).
Format: Zero-System Prompt optimized (merged instructions into User turn).

🧠 Tier-15 Mastery Documentation

The model is specifically trained on advanced mathematical lemmas and techniques:

LTE Lemma (Lifting The Exponent): For advanced valuations in Number Theory.
Geometric Inversion: Circle/Line inversion for hard geometry.
Barycentric Coordinates: Professional triangle solving.
Generating Functions: Specialized combinatorics and partition theory.
Roots of Unity Filter: Complex number summation for algebra.

📊 Dataset Composition

Source	Count	Description
Custom Reasoning traces	10,000	Hand-crafted traces using Tier-15 methods.
Competition Math	10,000	Sampled from AIME, Numina, and MATH (Elite subsets).
Synthetic God-Mode	8,500	Machine-generated chains of thought for difficult logic.

🛠️ Usage

It is recommended to use the model with a temperature between 0.0 and 0.3 for maximum deterministic accuracy in mathematical steps.

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "yesaf990/AIME-God-v5.2-Elite-1.5B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

# Use with standard Qwen 2.5 prompt template

Developed for the Benchmark-Crusher Initiative. 🚀💎🔱