INTELLECT-3 is a Mixture of Experts (MoE) inference model developed by Prime Intellect with a parameter scale of 106B (12B active parameters). It is supervised and fine-tuned based on GLM-4.5-Air-Base and trained through large-scale reinforcement learning, showing excellent performance in mathematics, coding, and inference tasks.

Natural Language ProcessingGguf

OpenThinker - Agent - v1 is an open - source intelligent agent model that is subsequently trained based on Qwen3 - 8B. It is designed for terminal operations and software engineering tasks. It is first trained on a high - quality supervised fine - tuning dataset and then further optimized through reinforcement learning. It performs excellently in agent benchmark tests such as Terminal - Bench 2.0 and SWE - Bench.

Natural Language ProcessingTransformersOther

INTELLECT-3 is a Mixture of Experts (MoE) model with 106 billion parameters, trained through large-scale reinforcement learning. It demonstrates excellent performance in mathematics, coding, and reasoning benchmark tests. The model, training framework, and environment are all open-sourced under a permissive license.

Natural Language ProcessingTransformersEnglish

Gheya-1 is a new-generation basic language model in the LES-IA-ETOILES ecosystem, with 202 million parameters. It is an upgraded version of the old Small-lamina series. This model is designed specifically for professional fine-tuning and has targeted training in the fields of artificial intelligence, professional language models, and biology.

Natural Language ProcessingTransformersFrench

Olmo-3-Think is a fully open-source language model series launched by the Allen Institute for Artificial Intelligence, available in two scales: 7B and 32B. This model is specially trained to demonstrate clear reasoning chains, support transparent step-by-step reasoning and inspectable intermediate thinking traces, and performs excellently in reasoning, mathematics, and code tasks.

Natural Language ProcessingTransformersEnglish

Fyodor-Q3-8B-Instruct is a high-fidelity instruction-tuned model designed for intelligent reasoning and robust code generation. It is based on the Qwen3-8B architecture and trained with a high-rank LoRA configuration. It excels in planning before coding and is well-suited for complex software engineering tasks.

Natural Language ProcessingTransformersMultiple Languages

DeepMath is a mathematical reasoning model with 4 billion parameters, built on Qwen3 - 4B Thinking, combining a fine - tuned large language model and a sandboxed Python executor. It can generate concise Python code snippets for calculation steps, significantly reducing errors and shortening the output length.

Natural Language ProcessingTransformersEnglish

Qwen3-32B is the latest generation of large language models in the Qwen series, with powerful reasoning, instruction-following, intelligent agent interaction, and multilingual processing capabilities. It supports over 100 languages and dialects and can seamlessly switch between thinking and non-thinking modes, providing users with a natural and smooth dialogue experience.

Natural Language ProcessingTransformers

SenseNova-SI is a series of spatial intelligence enhancement models built on multimodal foundation models. Through careful training with 8 million samples of data, it has achieved excellent performance in multiple spatial intelligence benchmark tests while maintaining strong general multimodal understanding capabilities.

MultimodalTransformersEnglish

RealRobot_chatbot_llm is a professional product artificial intelligence model based on the Gemma3n architecture, specifically fine-tuned on the proprietary dataset of the RealRobot product catalog. This model can quickly create an accurate, cost-effective, and deployable professional language model based on the enterprise's own product data, providing highly specialized product Q&A services.

Natural Language ProcessingGgufMultiple Languages

Cyber Intelligence Brain is a large language model specifically fine-tuned for Capture the Flag (CTF) challenges and network security tasks. It is based on the Llama-3.1-8B-Instruct architecture and can assist in solving CTF problems, analyzing security vulnerabilities, conducting reverse engineering and cryptography challenges, and providing vulnerability exploitation reasoning while adhering to ethical guidelines.

Natural Language ProcessingSafetensorsMultiple Languages

SenseNova-SI is a series of spatial intelligence enhancement models built on mature multimodal foundation models. Through training with carefully curated 8 million data samples, it demonstrates excellent performance in multiple spatial intelligence benchmark tests while maintaining strong general multimodal understanding ability.

MultimodalTransformers

Qwen3-Coder-REAP-363B-A35B is a sparse mixture-of-experts model obtained by pruning 25% of the experts from Qwen3-Coder-480B-A35B-Instruct using the REAP method. While maintaining performance close to the original model, it significantly reduces the parameter scale and memory requirements, making it particularly suitable for code generation and intelligent coding scenarios with limited resources.

Natural Language ProcessingTransformersEnglish

SenseNova-SI is a series of spatial intelligence models built on multimodal foundation models, specifically optimized for spatial understanding capabilities. Through large-scale spatial intelligence data training, it performs excellently in metric estimation, spatial relationship understanding, viewpoint change handling, etc.

MultimodalTransformers

Qwen3-VL-4B-Instruct is the latest vision-language model in the Tongyi Qianwen series. It achieves excellent visual perception, text understanding and generation, spatial reasoning, and intelligent agent interaction capabilities at the 4B parameter scale. It supports long contexts and video understanding, and has powerful OCR and multi-language processing capabilities.

MultimodalTransformers

MiniMax-M2 is a small Mixture of Experts (MoE) model built specifically for maximizing coding and agent workflows. It has a total of 230 billion parameters, with 1 billion active parameters. This model excels in coding and agent tasks while maintaining strong general intelligence. It is compact, fast, and cost-effective.

Natural Language ProcessingTransformers

JanusCoder-14B is an open-source foundation model with 14B parameters built on Qwen3-14B, aiming to establish a unified visual programming interface for code intelligence. This model is trained on the JANUSCODE-800K multimodal code corpus and can handle various visual programming tasks in a unified manner.

Natural Language ProcessingTransformers

JanusCoder-8B is an open-source code intelligence foundation model built on Qwen3-8B, aiming to establish a unified visual programming interface. This model is trained on JANUSCODE-800K (the largest multimodal code corpus to date) and can handle various visual programming tasks, including data visualization, interactive web UI, and code-driven animation.

MultimodalTransformers

Emu3.5 is a native multimodal model developed by the Beijing Academy of Artificial Intelligence (BAAI). It can jointly predict the next state across vision and language, enabling coherent world modeling and generation. After end-to-end pre-training and large-scale reinforcement learning post-training, it demonstrates excellent performance in multimodal tasks.

MultimodalTransformers

Emu3.5 is a native multimodal model developed by the Beijing Academy of Artificial Intelligence (BAAI). It can jointly predict the next state across vision and language, achieving coherent world modeling and generation, and performs excellently in multimodal tasks.

MultimodalTransformers