Model training and deployment

GenPRM is an emerging process reward model (PRM) that improves computational efficiency at test time by generating inferences. This technology can provide more accurate reward evaluation when processing complex tasks and is suitable for a variety of applications in the field of machine learning and artificial intelligence. Its main advantage is the ability to optimize model performance under limited resources and reduce computational costs in practical applications.

Arthur Engine is a tool designed to monitor and govern AI/ML workloads, leveraging popular open source technologies and frameworks. The enterprise version of the product offers better performance and additional features such as custom enterprise-grade safeguards and metrics designed to maximize the potential of AI for organizations. It can effectively evaluate and optimize models to ensure data security and compliance.

DeepSeek Profile Data is a project focused on performance analysis of deep learning frameworks. It captures performance data for training and inference frameworks through PyTorch Profiler, helping researchers and developers better understand computation and communication overlapping strategies as well as underlying implementation details. This data is critical for optimizing large-scale distributed training and inference tasks, which can significantly improve system efficiency and performance. This project is an important contribution of the DeepSeek team in the field of deep learning infrastructure and aims to promote the community's exploration of efficient computing strategies.

Expert Parallelism Load Balancer (EPLB) is a load balancing algorithm for expert parallelism (EP) in deep learning. It ensures load balancing between different GPUs through redundant expert strategies and heuristic packaging algorithms, while using group-limited expert routing to reduce inter-node data traffic. This algorithm is of great significance for large-scale distributed training and can improve resource utilization and training efficiency.

DualPipe is an innovative bidirectional pipeline parallel algorithm developed by the DeepSeek-AI team. This algorithm significantly reduces pipeline bubbles and improves training efficiency by optimizing the overlap of calculation and communication. It performs well in large-scale distributed training and is especially suitable for deep learning tasks that require efficient parallelization. DualPipe is developed based on PyTorch and is easy to integrate and expand. It is suitable for developers and researchers who require high-performance computing.

DeepGEMM is a CUDA library focused on efficient FP8 matrix multiplication. It significantly improves the performance of matrix operations through fine-grained scaling and multiple optimization technologies, such as Hopper TMA features, persistence thread specialization, full JIT design, etc. This library is mainly oriented to the fields of deep learning and high-performance computing, and is suitable for scenarios that require efficient matrix operations. It supports the Tensor Core of NVIDIA Hopper architecture and shows excellent performance in a variety of matrix shapes. DeepGEMM's design is simple, with only about 300 lines of core code, making it easy to learn and use, while its performance is comparable to or better than expert-optimized libraries. The open source and free nature makes it an ideal choice for researchers and developers to optimize and develop deep learning.

DeepSeek R1 and V3 API are powerful AI model interfaces provided by Kie.ai. DeepSeek R1 is the latest inference model designed for advanced reasoning tasks such as mathematics, programming, and logical reasoning. It is trained by large-scale reinforcement learning to provide accurate results. DeepSeek V3 is suitable for handling general AI tasks. These APIs are deployed on secure servers in the United States to ensure data security and privacy. Kie.ai also provides detailed API documentation and multiple pricing plans to meet different needs, helping developers quickly integrate AI capabilities and improve project performance.

This product is an open source project developed by Vectara to evaluate the hallucination rate of large language models (LLM) when summarizing short documents. It uses Vectara’s Hughes Hallucination Evaluation Model (HHEM-2.1) to calculate rankings by detecting hallucinations in the model output. This tool is of great significance for the research and development of more reliable LLM, and can help developers understand and improve the accuracy of the model.

The DeepSeek Model Compatibility Check is a tool for evaluating whether a device is capable of running DeepSeek models of different sizes. It provides users with prediction results of model operation by detecting the device's system memory, video memory and other configurations, combined with the model's parameters, number of precision bits and other information. This tool is of great significance to developers and researchers when choosing appropriate hardware resources to deploy DeepSeek models. It can help them understand the compatibility of the device in advance and avoid operational problems caused by insufficient hardware. The DeepSeek model itself is an advanced deep learning model that is widely used in fields such as natural language processing and is efficient and accurate. Through this detection tool, users can better utilize DeepSeek models for project development and research.

Astris AI is a subsidiary of Lockheed Martin established to drive the adoption of high-assurance artificial intelligence solutions across the U.S. defense industrial base and commercial industry sectors. Astris AI helps customers develop and deploy secure, resilient and scalable AI solutions by providing Lockheed Martin's leading technology and professional teams in artificial intelligence and machine learning. The establishment of Astris AI demonstrates Lockheed Martin's commitment to advancing 21st century security, strengthening the defense industrial base and national security, while also demonstrating its leadership in integrating commercial technologies to help customers address the growing threat environment.

Procyon AI Inference Benchmark for Android is an NNAPI-based benchmark tool used to measure AI performance and quality on Android devices. It leverages a range of popular, state-of-the-art neural network models to perform common machine vision tasks, helping engineering teams independently and standardizedly evaluate the AI ​​performance of NNAPI implementations and specialized mobile hardware. This tool can not only measure the performance of dedicated AI processing hardware on Android devices, but also verify the quality of NNAPI implementation, which is of great significance for optimizing drivers of hardware accelerators and comparing the performance of floating point and integer optimization models.

OLMo 2 1124 13B Preference Mixture is a large multilingual dataset provided by Hugging Face, containing 377.7k generated pairs, used for training and optimizing language models, especially in preference learning and instruction following. The importance of this dataset is that it provides a diverse and large-scale data environment that helps develop more precise and personalized language processing technologies.

The allenai/olmo-mix-1124 data set is a large-scale multi-modal pre-training data set provided by Hugging Face, which is mainly used to train and optimize natural language processing models. This dataset contains a large amount of text information, covers multiple languages, and can be used for various text generation tasks. Its importance lies in providing a rich resource that enables researchers and developers to train more accurate and efficient language models, thereby promoting the development of natural language processing technology.

FrontierMath is a mathematical benchmarking platform designed to test the limits of artificial intelligence's ability to solve complex mathematical problems. It was co-created by more than 60 mathematicians and covers the full spectrum of modern mathematics from algebraic geometry to Zermelo-Fraenkel set theory. Each FrontierMath problem requires hours of work from expert mathematicians, and even the most advanced AI systems, such as GPT-4 and Gemini, can solve less than 2% of the problems. This platform provides a true evaluation environment where all questions are new and unpublished, eliminating the data contamination problem prevalent in existing benchmarks.

Physical Intelligence (π) is a team of engineers, scientists, roboticists, and company builders developing the foundational models and learning algorithms that power today's robots and tomorrow's physically-driven devices. The team aims to apply general artificial intelligence technology to the physical world and promote the development and innovation of robotics.

SimpleQA is a factual benchmark released by OpenAI that measures the ability of language models to answer short, fact-seeking questions. It helps evaluate and improve the accuracy and reliability of language models by providing datasets with high accuracy, diversity, challenge, and a good researcher experience. This benchmark is an important advance for training models that produce factually correct responses, helping to increase the model's trustworthiness and broaden its range of applications.

SoundStorm is an audio generation technology developed by Google Research that significantly reduces audio synthesis time by generating audio tokens in parallel. 这项技术能够生成高质量、与语音和声学条件一致性高的音频，并且可以与文本到语义模型结合，控制说话内容、说话者声音和说话轮次，实现长文本的语音合成和自然对话的生成。 The importance of SoundStorm is that it solves the problem of slow inference speed of traditional autoregressive audio generation models when processing long sequences, and improves the efficiency and quality of audio generation.

Chai-1 is a multimodal basic model for drug discovery that can predict the molecular structure of proteins, small molecules, DNA, RNA, covalent modifications, etc. It achieved a 77% success rate on the PoseBusters benchmark, which is comparable to AlphaFold3. Chai-1 operates without multiple sequence alignments, retains most of its performance, and is able to fold multimeric structures more accurately. In addition, Chai-1 can be combined with laboratory data to improve prediction performance. This model aims to transform biology from science to engineering and promote the application of AI in biological research.

The Thousand Brains Project was launched by Jeff Hawkins and Numenta to develop new artificial intelligence systems by understanding the working principles of the cerebral neocortex. This project is based on the Thousand Brains Theory of Intelligence and proposes a fundamentally different working principle of the brain from traditional AI systems. The goal of the project is to build an efficient and powerful intelligent system that can achieve the intelligence capabilities of humans. Numenta has opened up its research resources, including conference proceedings, open source code, and built a large community around its algorithms. The project is financially supported by the Gates Foundation and others, and researchers around the world are encouraged to participate or join this exciting project.

Automated Machine Learning as a Service is a website that provides automated machine learning services. Users can obtain their machine learning models by uploading data, and the platform provides users with a convenient machine learning model development and deployment process. The platform also provides a wealth of features and advantages, including an easy-to-use interface, automated model training and optimization, flexible pricing strategies, and more. Users can choose a suitable pricing plan according to their needs and apply the machine learning model in different scenarios. This product is positioned to provide users with efficient, convenient and flexible machine learning solutions.

Llama Family is an open source platform dedicated to building an open Llama model ecosystem, including a variety of large models and code models. It has rich functions and advantages, and provides various cooperation methods for computing power acquisition and model training. Prices vary based on partnership and include free and paid options. The main functions include model training, computing power acquisition, open source ecological co-construction, etc. Suitable for all kinds of technology enthusiasts and developers.

Cerebras Systems announced the launch of its third generation 5nm Wafer Scale Engine (WSE-3), a chip designed for training the industry’s largest AI models. WSE-3 offers twice the performance of its predecessor, WSE-2, while maintaining the same power consumption and price. The chip is based on a 5-nanometer process and has 4 trillion transistors, delivering 125 petaflops of peak AI performance through 900,000 AI-optimized computing cores.

CodeWithGPU is a GitHub community focused on AI algorithm reproduction. It aims to provide high-performance GPU support to help developers reproduce excellent AI algorithms. Users can find images and models of various popular algorithms on this platform, and communicate and learn.

Rawbot is an AI model comparison platform that helps users easily compare different AI models and realize their full potential in projects. Users can choose the best AI model based on accurate side-by-side comparisons. Rawbot is compatible with ChatGPT, Cohere and J2 Complete.

Neuton TinyML is a no-code AI platform that automatically builds tiny models and embeds them into any microcontroller and sensor. It is based on a patented neural network framework that enables extremely small model sizes while maintaining accuracy.

Ollama is a native large language model tool that allows users to quickly run Llama 2, Code Llama and other models. Users can customize and create their own models. Ollama currently supports macOS and Linux, with a Windows version coming soon. This product is positioned to provide users with a localized large language model operating environment to meet users' personalized needs.

Dark Pools is a leading artificial intelligence company focused on automated machine learning. Our intelligent solutions help various industries around the world meet industry requirements while providing high-value intelligent solutions to increase revenue, optimize operations, reduce risks, personalize customer experience and a variety of customizable anomaly detection. Dark Pools orchestration enables intelligence-driven automation, acceleration, and transparency throughout every step of the data science lifecycle. It also provides companies with a fully flexible architecture specifically designed to meet the complexity of service use cases through an extensible platform designed specifically to meet your industry business ontology (IBO).

AI-Flow is an open source, user-friendly UI application that creates interactive networks with different AI models. It can easily connect multiple AI models to achieve the function of responding to various prompts from multiple angles. AI-Flow supports designing customized AI networks by editing flow charts. Users can easily create, save and share their own AI networks and experiment with different outputs by changing the initial inputs. AI-Flow also supports obtaining content from external data sources and can be used to generate content or provide instant feedback on generated content.

Volcano Ark provides a full range of functions and services such as model training, inference, evaluation, and fine-tuning, and focuses on supporting large model ecology. Selected models to ensure model stability, rich platform applications and tools, information security, strong computing power, and professional services. The main functions include model square, model experience, model training and reasoning, model application, etc. It is suitable for industry scenarios such as automobiles, finance, large consumer goods, pan-Internet, education and office.

AIProval is an AI model verification platform that ensures that AI models are accurate, fair, compliant, and prepared for future development. Analyze model performance and provide actionable insights with instant feedback and rapid iteration capabilities. The platform automatically detects and mitigates bias, ensuring AI systems are fair and accountable. Support data privacy and compliance to help navigate the changing regulatory environment. Integrate AIproval into existing workflows and easily deploy models. Continuously track and optimize the performance of AI models through monitoring tools.