nvidia/OpenMath-Nemotron-14B-Kaggle

Hugging Face
TEXT GENERATIONConcurrency Cost:1Model Size:14.8BQuant:FP8Ctx Length:32kPublished:Apr 22, 2025License:cc-by-4.0Architecture:Transformer0.0K Open Weights Warm

The OpenMath-Nemotron-14B-Kaggle model by NVIDIA is a 14.8 billion parameter, Qwen2.5-based transformer decoder-only language model, fine-tuned on a subset of the OpenMathReasoning dataset. With a 131,072 token context length, it is specifically optimized for advanced mathematical reasoning and problem-solving, achieving strong results on benchmarks like AIME and HMMT. This model was instrumental in NVIDIA's first-place submission to the AIMO-2 Kaggle competition, demonstrating its capability in complex mathematical tasks.

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Overview

NVIDIA's OpenMath-Nemotron-14B-Kaggle is a 14.8 billion parameter model built upon the Qwen2.5-14B architecture. It was fine-tuned using a subset of the OpenMathReasoning dataset, specifically designed to excel in mathematical reasoning tasks. This model played a key role in NVIDIA's first-place achievement in the AIMO-2 Kaggle competition.

Key Capabilities

  • Advanced Mathematical Reasoning: Optimized for solving complex math problems, as evidenced by its performance in the AIMO-2 Kaggle competition.
  • Benchmark Performance: Achieves competitive results on mathematical benchmarks such as AIME24, AIME25, HMMT-24-25, and HLE-Math, often outperforming other models in its class.
  • High Context Length: Supports a context length of up to 131,072 tokens, allowing for processing extensive mathematical problems and related information.
  • Code Execution Integration: Designed to work effectively with code execution for solving math problems, with a reference implementation available in NeMo-Skills.

Good For

  • Mathematical Research: Intended to facilitate research and development in the field of mathematical reasoning.
  • Competitive Problem Solving: Highly suitable for tasks requiring robust mathematical problem-solving capabilities, such as those found in mathematical olympiads.
  • Specialized Math Applications: Best utilized in scenarios where the primary requirement is accurate and deep mathematical understanding and solution generation, rather than general conversational AI.

Popular Sampler Settings

Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.

temperature
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frequency_penalty
presence_penalty
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