Products

Leapfrog Energy is a workflow-based 3D subsurface modelling software that enables you to build and refine models – fast. With over 30 years supporting the energy industry across the Seequent portfolio, this powerful technology streamlines your workflows and reduces risk to meet energy industry needs.

Aleph Alpha's LLM-powered agent accelerates complex semiconductor documentation retrieval, reducing search time by 90%.

Granite-4.0-Tiny-Base-Preview is a 7-billion-parameter hybrid mixture-of-experts (MoE) language model developed by IBM's Granite Team. It features a 128,000-token context window and utilizes the Mamba-2 architecture combined with softmax attention to enhance expressiveness. Notably, it omits positional encoding to improve length generalization. Key Features and Functionality: - Extensive Context Window: Supports up to 128,000 tokens, facilitating the processing of lengthy documents and c

The Phi-3 Mini-4K-Instruct is a lightweight, state-of-the-art language model developed by Microsoft, featuring 3.8 billion parameters. It is part of the Phi-3 model family and is designed to support a context length of 4,000 tokens. Trained on a combination of synthetic data and filtered publicly available websites, the model emphasizes high-quality, reasoning-dense content. Post-training enhancements, including supervised fine-tuning and direct preference optimization, have been applied to impr

Granite-3.1-1B-A400M-Base is a language model developed by IBM's Granite Team, designed to handle extensive context lengths up to 128K tokens. This model is based on a decoder-only sparse Mixture of Experts (MoE) transformer architecture, incorporating fine-grained experts, dropless token routing, and load balancing loss. It supports multiple languages, including English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. Key Features and Funct

Llama 3.2 3B Instruct is a 3-billion parameter multilingual large language model developed by Meta, designed to excel in conversational AI applications. It leverages an optimized transformer architecture and has been fine-tuned using supervised learning and reinforcement learning with human feedback to enhance its performance in generating contextually relevant and coherent responses. Key Features and Functionality: - Multilingual Proficiency: Supports multiple languages, enabling seamle

NVIDIA Nemotron-Nano-9B-v2 is a compact, open-source language model designed to deliver high-performance reasoning and agentic capabilities. Utilizing a hybrid Mamba-Transformer architecture, it efficiently processes long-context sequences up to 128,000 tokens, making it suitable for complex tasks requiring extensive context understanding. The model supports multiple languages, including English, German, French, Italian, Spanish, and Japanese, and excels in instruction following and code generat

Granite-3.2-8B-Instruct is an 8-billion-parameter AI model fine-tuned for advanced reasoning tasks. Built upon its predecessor, Granite-3.1-8B-Instruct, it has been trained using a combination of permissively licensed open-source datasets and internally generated synthetic data tailored for complex problem-solving. The model offers controllable reasoning capabilities, ensuring its application is precise and contextually appropriate. Key Features and Functionality: - Advanced Reasoning: E

Llama 3.2 1B Instruct is a multilingual large language model developed by Meta, designed to facilitate advanced natural language understanding and generation across multiple languages. With 1 billion parameters, this model is optimized for tasks such as dialogue generation, summarization, and agentic retrieval, offering robust performance in diverse linguistic contexts. Its architecture incorporates supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align outpu

Granite-4.0-Tiny-Preview is a 7-billion-parameter fine-grained hybrid mixture-of-experts (MoE) instruction-following model developed by IBM's Granite Team. Fine-tuned from the Granite-4.0-Tiny-Base-Preview, it utilizes a combination of open-source instruction datasets and internally generated synthetic data to address long-context problems. The model employs techniques such as supervised fine-tuning and reinforcement learning-based alignment to enhance its performance in structured chat formats.

Hunyuan is Tencent's advanced AI model designed to revolutionize content creation across various industries, particularly in gaming. It offers a suite of tools that enhance the development process by integrating artificial intelligence into creative workflows. Key Features and Functionality: - Image Generation Models: Hunyuan provides four specialized models for 2D art design, including text-to-image generation tailored for gaming scenarios, text-to-game visual effects, image-to-game visual ef

Stable LM 2 12B is a 12.1 billion parameter decoder-only language model developed by Stability AI. Pre-trained on 2 trillion tokens from diverse multilingual and code datasets over two epochs, it is designed to generate coherent and contextually relevant text across various applications. The model employs a transformer decoder architecture with 40 layers, a hidden size of 5120, and 32 attention heads, supporting a sequence length of up to 4096 tokens. Key features include the use of Rotary Posit

Granite-3.2-2B-Instruct is a 2-billion-parameter language model developed by IBM's Granite Team, designed to handle a wide range of instruction-following tasks. Built upon its predecessor, Granite-3.1-2B-Instruct, this model has been fine-tuned using a combination of permissively licensed open-source datasets and internally generated synthetic data, focusing on enhancing reasoning capabilities. It supports multiple languages, including English, German, Spanish, French, Japanese, Portuguese, Arab

Phi-4-mini-reasoning is a compact, transformer-based language model developed by Microsoft, specifically optimized for mathematical reasoning tasks. With 3.8 billion parameters and support for a 128K token context length, it delivers high-quality, step-by-step problem-solving capabilities in environments where computational resources or latency are constrained. Fine-tuned using synthetic mathematical data generated by a more advanced model, Phi-4-mini-reasoning excels in multi-step, logic-intens

Step-1 8k is a large-scale language model developed by StepFun, designed to understand and generate natural language text across various domains. With a context length of 8,000 tokens, it can process substantial input and output, making it suitable for tasks such as content creation, multilingual communication, question answering, and logical reasoning. Additionally, Step-1 8k exhibits strong mathematical and coding capabilities, supporting applications in scientific computation and software dev

Granite-3.3-8B-Instruct is an advanced language model developed by IBM's Granite Team, featuring 8 billion parameters and a 128K context length. Fine-tuned for enhanced reasoning and instruction-following capabilities, it builds upon the Granite-3.3-8B-Base model to deliver significant improvements across various benchmarks, including AlpacaEval-2.0 and Arena-Hard. The model excels in tasks such as mathematics, coding, and structured reasoning, utilizing specialized tags to distinguish between i

StepFun is an innovative technology company specializing in the development of advanced artificial intelligence (AI) models and tools designed to enhance human-AI collaboration across various domains. By integrating cutting-edge research with practical applications, StepFun aims to provide solutions that streamline complex tasks, improve efficiency, and foster creativity. Key Features and Functionality: - Multimodal AI Models: StepFun has developed models like Step3, a multimodal reasoning mod

Phi-3.5-mini is a lightweight, state-of-the-art language model developed by Microsoft, designed to deliver high-quality reasoning capabilities within a compact architecture. Building upon the datasets used for Phi-3, it focuses on very high-quality, reasoning-dense data, including synthetic data and filtered publicly available websites. The model supports a 128K token context length, enabling it to handle extensive inputs effectively. Through rigorous enhancement processes such as supervised fin

Granite-3.3-2B-Instruct is a 2-billion parameter language model developed by IBM's Granite Team, designed to enhance reasoning and instruction-following capabilities. With a context length of 128K tokens, it builds upon the Granite-3.3-2B-Base model, delivering significant improvements in benchmarks such as AlpacaEval-2.0 and Arena-Hard, as well as in mathematics, coding, and instruction-following tasks. The model supports structured reasoning through the use of `` and `` tags, allowing for clea

The Phi-3 Mini-4K-Instruct is a lightweight, state-of-the-art language model developed by Microsoft, featuring 3.8 billion parameters. It is part of the Phi-3 model family and is designed to support a context length of 4,000 tokens. Trained on a combination of synthetic data and filtered publicly available websites, the model emphasizes high-quality, reasoning-dense content. Post-training enhancements, including supervised fine-tuning and direct preference optimization, have been applied to impr