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URL: https://huggingface.co/Azmainadeeb/MathGPT

โ‡ฑ Azmainadeeb/MathGPT ยท Hugging Face


MathGPT (GPT-OSS-120B-Olympiad)

MathGPT is a high-performance reasoning model fine-tuned from GPT-OSS 120B. It is engineered specifically for solving complex mathematical theorems, competition-level problems (AIME/IMO), and advanced scientific reasoning.

  • Developed by: Azmainadeeb
  • Model Type: Causal Language Model (Fine-tuned for Mathematical Reasoning)
  • Base Model: unsloth/gpt-oss-120b-unsloth-bnb-4bit
  • Training Framework: Unsloth + TRL

๐Ÿ‘ Image

๐Ÿงฉ Model Architecture

MathGPT leverages the Mixture-of-Experts (MoE) architecture of the GPT-OSS family, utilizing 117B total parameters with 5.1B active parameters per token. This allows the model to maintain state-of-the-art reasoning depth while remaining computationally efficient during inference.

๐Ÿ“š Training Data

The model was trained on a massive synthesis of reasoning-dense datasets to ensure "Chain of Thought" consistency:

Primary Thinking Dataset

Olympiad & Competition Sets

  • OlympiadBench & MathOlympiadBench: High-difficulty benchmark problems.
  • IMO Math Boxed: Problems curated from the International Mathematical Olympiad.
  • AoPS (Art of Problem Solving): Diverse competition-style math problems.
  • AIMO External Data: Specific sets designed for the AI Mathematical Olympiad.

๐Ÿš€ Quickstart Usage

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
 model_name = "Azmainadeeb/MathGPT",
 max_seq_length = 4096,
 load_in_4bit = True,
)

messages = [
 {"role": "user", "content": "Find all real numbers x such that 8^x + 2^x = 130."}
]

# Apply the template with reasoning_effort to trigger the "Thinking" mode
inputs = tokenizer.apply_chat_template(
 messages,
 add_generation_prompt = True,
 reasoning_effort = "medium", # Options: low, medium, high
 return_tensors = "pt"
).to("cuda")

outputs = model.generate(inputs, max_new_tokens = 1024)
print(tokenizer.decode(outputs[0]))
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