# Mathematical reasoning

OpenThinker3-7B-GGUF is a quantized version of open-thoughts/OpenThinker3-7B, optimized for efficient inference. It is fine-tuned based on Qwen/Qwen2.5-7B-Instruct and performs excellently on mathematical, code, and scientific problems.

Chinda LLM 4B is a cutting-edge Thai model launched by iApp Technology, built on the Qwen3-4B architecture, bringing advanced thinking capabilities to the Thai AI ecosystem.

Spec-T1-RL-7B is a high-precision large language model focused on mathematical reasoning, algorithm problem-solving, and code generation, and it performs excellently in technical benchmark tests.

This is a 4-bit quantized version in MLX format converted from the Microsoft Phi-4-mini-reasoning model, suitable for text generation tasks.

Phi-4-reasoning is an advanced reasoning model fine-tuned based on Phi-4. Through supervised fine-tuning and reinforcement learning, it demonstrates excellent reasoning abilities in fields such as mathematics, science, and coding.

Phi-4-mini-reasoning is a lightweight open model based on synthetic data, focusing on high-quality, dense reasoning data, and further fine-tuned to enhance mathematical reasoning capabilities.

An open-source large language model designed for Lean 4 formal theorem proving, collecting data through recursive theorem proving processes, combining informal and formal mathematical reasoning.

Phi-4-mini-reasoning is a lightweight open-source model that focuses on high-quality, dense reasoning data and is further fine-tuned to obtain more advanced mathematical reasoning capabilities.

A deep learning-based automatic math problem-solving system supporting various types of math questions including algebra, geometry, and calculus

OpenMath-Nemotron-7B is a mathematical reasoning model fine-tuned on the OpenMathReasoning dataset based on Qwen2.5-Math-7B, achieving state-of-the-art results on multiple mathematical benchmarks.

Turkish-Gemma-9b-v0.1 is a Turkish text generation model developed based on Gemma-2-9b, optimized through continued pretraining, supervised fine-tuning (SFT), direct preference optimization (DPO), and model merging techniques.

Nova 0.5 e3 is a 7B-parameter text generation model that demonstrates astonishing emergent properties, particularly excelling in mathematical reasoning.

Light-R1-32B-DS is a near-SOTA level 32B mathematical model, fine-tuned based on DeepSeek-R1-Distill-Qwen-32B, achieving high performance with only 3K SFT data.

FP8 quantized version of QwQ-32B, reducing storage and memory requirements by 50% through dynamic quantization while maintaining 99.75% of the original model accuracy

FP8 quantized version of QwQ-32B, reducing storage and memory requirements by 50% through dynamic quantization while maintaining 99.75% of the original model accuracy

A multilingual causal language model fine-tuned based on Lucie-7B, supporting French and English, focused on instruction following and text generation tasks.

DeepSeek-R1 is the first-generation inference model, which performs excellently in mathematics, code, and reasoning tasks, and its performance is comparable to that of OpenAI-o1.

AceMath is a series of cutting-edge models designed specifically for mathematical reasoning. It is improved based on Qwen and is good at using chain-of-thought (CoT) reasoning to solve English mathematical problems.

A fine-tuned version based on VAGOsolutions/SauerkrautLM-gemma-2-9b-it, optimized for Italian language performance with support for 8k context length.

Qwen2 1.5B is a compact language model specifically optimized for Italian, with performance close to ITALIA (iGenius) but 6 times smaller in size.

InternLM-Math-Plus is the most advanced bilingual open-source large language model for mathematical reasoning, with functions such as solving, proving, verifying, and enhancing, providing strong support for the field of mathematical reasoning.

Yi-1.5 is an upgraded version of the Yi model, excelling in programming, mathematics, reasoning, and instruction-following capabilities while maintaining outstanding language understanding, commonsense reasoning, and reading comprehension abilities.

Yi-1.5 is an upgraded version of the Yi model, excelling in programming, mathematics, reasoning, and instruction-following capabilities while maintaining excellent language understanding, commonsense reasoning, and reading comprehension.

Yi-1.5 is an upgraded version of the Yi model, excelling in programming, mathematics, reasoning, and instruction-following capabilities while maintaining outstanding language understanding, commonsense reasoning, and reading comprehension.

A DPO fine-tuned model based on Llama-3-8b, focused on enhancing truthfulness and mathematical reasoning capabilities

The OpenMath model is specifically designed for solving mathematical problems by integrating textual reasoning with Python interpreter-executed code blocks. Trained on the OpenMathInstruct-1 dataset containing 1.8 million math problem-solution pairs.

Piccolo-math-2x7b is a large language model specializing in mathematical and logical reasoning, named in honor of the author's pet dog Klaus. The model demonstrates outstanding performance across multiple benchmarks, particularly in mathematical and code generation tasks.