Olmo 3 is a new family of language models developed by the Allen Institute for AI, including two scales of 7B and 32B, with two variants: Instruction and Think. This model uses long-chain thinking to improve performance in reasoning tasks such as mathematics and coding, aiming to promote the scientific development of language models.

Natural Language ProcessingTransformersEnglish

Olmo 3 is a new series of 7B parameter language models developed by the Allen Institute for AI. Based on the Transformer architecture, it is trained on the Dolma 3 dataset, aiming to promote the scientific development of language models. The model offers various variants such as Base, Instruct, and Think, supporting a maximum context length of 65,536.

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MobileLLM-R1 is an efficient inference model series released by Meta, including three scales of 140M, 360M, and 950M. This model is specifically optimized for mathematical, programming, and scientific problems, achieving comparable or even better performance than large-scale models with a smaller parameter scale.

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MobileLLM-R1 is a series of efficient inference language models released by Meta, focusing on solving mathematical, programming, and scientific problems. This model can still achieve excellent performance with a relatively small parameter scale, and provides a complete training recipe and data sources to support reproducible research.

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MobileLLM-R1 is a series of efficient inference models launched by Facebook, focusing on solving mathematical, programming, and scientific problems. This model has achieved excellent performance in multiple benchmark tests with only about 2T high-quality labels for pre-training.

Natural Language ProcessingTransformersEnglish

MobileLLM-R1 is a series of efficient inference models focused on mathematics, programming, and scientific problems, achieving excellent performance with less training data and providing a complete training recipe and data source.

Natural Language ProcessingTransformersEnglish

MobileLLM-R1 is a series of efficient reasoning models launched by Meta, focusing on solving mathematical, programming, and scientific problems. The model offers three scale versions of 140M, 360M, and 950M, with excellent reasoning ability and reproducibility.

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KONI-4B-instruct-20250901 is a large language model developed by the Korea Institute of Science and Technology Information (KISTI) focusing on the technology field. It supports Korean and English and performs excellently in various scientific benchmark tests, outperforming other pre-trained models of the same scale.

Natural Language ProcessingTransformersMultiple Languages

SDAR is a new type of large language model that integrates autoregressive and discrete diffusion modeling strategies, combining the advantages of efficient training of AR models and parallel inference of diffusion models. It performs comparably to SOTA open - source AR models on general tasks and excels in scientific reasoning tasks, becoming the most powerful diffusion language model.

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Qwen3-235B-A22B-Thinking-2507 is a powerful mixture-of-experts language model that performs excellently in multiple fields such as knowledge reasoning, mathematical calculation, scientific analysis, and programming. This model has a total of 235B parameters and 22B active parameters, supports 256K long context understanding, and has reached the state-of-the-art performance level among open-source thinking models.

Natural Language ProcessingTransformers

Intern-S1 is currently the most advanced open-source multimodal reasoning model. It combines powerful general task processing capabilities with excellent performance in a wide range of scientific tasks and can be comparable to leading closed-source commercial models. The model is continuously pre-trained on a 5T token dataset, of which more than 50% is professional scientific data. It has a dynamic word segmenter that can natively understand molecular formulas, protein sequences, and seismic signals.

MultimodalSafetensors

Intern-S1 is currently the most advanced open-source multimodal reasoning model, combining powerful general task capabilities with excellent performance on a wide range of scientific tasks, and can compete with leading closed-source commercial models.

MultimodalSafetensors

SciGLM is a set of scientific language models capable of university-level scientific reasoning, constructed using a self-reflective instruction annotation framework to build the high-quality dataset SciInstruct.

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TheProfessor is a hybrid model created by merging multiple pre-trained language models using the mergekit tool, specializing in conversational interaction, logical reasoning, scientific research, medical knowledge, and mathematical capabilities.

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Pythia-12B is the largest model in EleutherAI's scalable language model suite, with 12 billion parameters, specifically designed to advance scientific research on large language models

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Pythia-70M-deduped is the smallest model in the Pythia scalable suite developed by EleutherAI, with 70 million parameters. This model is trained on the deduplicated Pile dataset and is specifically designed for interpretability research of language models. It provides 154 training checkpoints for scientific research.

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Pythia-1.4B-deduped is a 1.2 billion parameter deduplicated version language model developed by EleutherAI and is part of the Pythia interpretability research suite. This model is trained on the globally deduplicated Pile dataset and is specifically designed for scientific research, with performance comparable to models of the same scale.

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GALACTICA is a series of language models trained on large-scale scientific corpora, specializing in scientific task processing.

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