Tag: reasoning

Horizon Alpha is a platform integrated with next-generation artificial intelligence to provide fast, reliable solutions for modern creators. Its main advantage is to lead the development of artificial intelligence technology and provide excellent reasoning, coding and natural language understanding capabilities. This product is positioned as an enterprise-level AI platform and has excellent performance and flexibility.

Grok 4 is the latest version of the large-scale language model launched by xAI, which will be officially released in July 2025. It has leading natural language, mathematics and reasoning capabilities and is a top model AI. Grok 4 represents a huge step forward, skipping the expected Grok 3.5 version to speed up progress in the fierce AI competition.

Claude 4 is Anthropic’s latest AI model series, which has powerful programming and reasoning capabilities and can handle complex tasks efficiently. Its superior performance has placed it at the top of programming benchmarks, making it an important tool for developers. Claude 4 improves the efficiency and accuracy of information processing through the introduction of many new features, making it suitable for users who need efficient coding and logical reasoning.

DeepSeek-Prover-V2-671B is an advanced artificial intelligence model designed to provide powerful inference capabilities. It is based on the latest technology and suitable for a variety of application scenarios. This model is open source and aims to promote the democratization and popularization of artificial intelligence technology, lower technical barriers, and enable more developers and researchers to use AI technology to innovate. By using this model, users can improve their work efficiency and promote the progress of various projects.

This model improves the reasoning capabilities of diffusion large language models through reinforcement learning and masked self-supervised fine-tuning of high-quality reasoning trajectories. The importance of this technology lies in its ability to optimize the model's inference process and reduce computational costs while ensuring the stability of learning dynamics. Ideal for users who want to be more efficient in writing and reasoning tasks.

Llama-3.1-Nemotron-Ultra-253B-v1 is a large language model based on Llama-3.1-405B-Instruct, which undergoes multi-stage post-training to improve reasoning and chatting capabilities. This model supports context lengths up to 128K, has a good balance between accuracy and efficiency, is suitable for commercial use, and aims to provide developers with powerful AI assistant functions.

Gemini 2.5 is the most advanced AI model launched by Google. It has efficient inference and coding performance, can handle complex problems, and performs well in multiple benchmark tests. The model introduces new thinking capabilities, combines enhanced basic models and post-training to support more complex tasks, aiming to provide powerful support for developers and enterprises. Gemini 2.5 Pro is available in Google AI Studio and the Gemini app for users who require advanced inference and coding capabilities.

The o1-pro model is an advanced artificial intelligence language model designed to provide high-quality text generation and complex reasoning. It has superior performance in reasoning and response accuracy, and is suitable for application scenarios that require high-precision text processing. The pricing of this model is based on the tokens used, with an input price of $150 per million tokens and an output price of $600 per million tokens. It is suitable for enterprises and developers to integrate efficient text generation capabilities into their applications.

QwQ-32B is a reasoning model of the Qwen series, focusing on the thinking and reasoning capabilities of complex problems. It excels in downstream tasks, especially in solving puzzles. The model is based on the Qwen2.5 architecture and is pre-trained and optimized by reinforcement learning. It has 32.5 billion parameters and supports a processing capacity of 131,072 full context lengths. Its key benefits include powerful reasoning capabilities, efficient long text processing capabilities, and flexible deployment options. This model is suitable for scenarios that require deep thinking and complex reasoning, such as academic research, programming assistance, and creative writing.

QwQ-Max-Preview is the latest achievement of the Qwen series, built on Qwen2.5-Max. It shows stronger capabilities in mathematics, programming, and general tasks, and also performs well in Agent-related workflows. As a preview version of the upcoming QwQ-Max, this version is still being optimized. Its main advantages include strong capabilities for deep reasoning, mathematics, programming and agent tasks. In the future, we plan to release QwQ-Max and Qwen2.5-Max as open source under the Apache 2.0 license agreement, aiming to promote innovation in cross-domain applications.

Claude 3.7 Sonnet is the latest hybrid inference model launched by Anthropic, which can achieve seamless switching between fast response and deep inference. It excels in areas such as programming, front-end development, and provides granular control over the depth of inference via APIs. This model not only improves code generation and debugging capabilities, but also optimizes the processing of complex tasks, making it suitable for enterprise-level applications. Pricing is consistent with its predecessor, charging $3 per million tokens for input and $15 per million tokens for output.

DeepHermes 3 is an advanced language model developed by NousResearch that improves answer accuracy through systematic reasoning. It supports inference mode and regular response mode, and users can switch through system prompts. The model performs well in multi-turn dialogue, role-playing, reasoning, etc., and is designed to provide users with more powerful and flexible language generation capabilities. The model is fine-tuned based on Llama-3.1-8B, has 8.03 billion parameters, and supports a variety of application scenarios, such as reasoning, dialogue, function calling, etc.

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.

Grok 3 is the latest flagship AI model developed by Elon Musk’s AI company xAI. It has significantly improved computing power and data set size, can handle complex mathematical and scientific problems, and supports multi-modal input. Its main advantage is its powerful inference capabilities, the ability to provide more accurate answers, and surpassing existing top models in some benchmarks. The launch of Grok 3 marks the further development of xAI in the field of AI, aiming to provide users with smarter and more efficient AI services. This model currently mainly provides services through Grok APP and X platform, and will also launch voice mode and enterprise API interface in the future. It is positioned as a high-end AI solution, mainly for users who require deep reasoning and multi-modal interaction.

Huginn-0125 is a latent variable loop depth model developed in Tom Goldstein's laboratory at the University of Maryland, College Park. The model has 3.5 billion parameters, was trained on 800 billion tokens, and performs well in inference and code generation. Its core feature is to dynamically adjust the amount of calculation during testing through the loop depth structure, which can flexibly increase or decrease calculation steps according to task requirements, thereby optimizing resource utilization while maintaining performance. The model is released based on the open source Hugging Face platform and supports community sharing and collaboration. Users can freely download, use and further develop it. Its open source nature and flexible architecture make it an important tool in research and development, especially in scenarios where resources are constrained or high-performance inference is required.

MedRAX is an innovative AI framework designed for intelligent analysis of chest X-rays (CXR). It is capable of dynamically processing complex medical queries by integrating state-of-the-art CXR analysis tools and multi-modal large-scale language models. MedRAX can run without additional training, supports real-time CXR interpretation, and is suitable for a variety of clinical scenarios. Its main advantages include high flexibility, powerful reasoning capabilities, and transparent workflows. This product is aimed at medical professionals and aims to improve diagnostic efficiency and accuracy and promote the practical use of medical AI.

DeepClaude is a powerful AI tool designed to combine the inference capabilities of DeepSeek R1 with the creativity and code generation capabilities of Claude, delivered through a unified API and chat interface. It leverages a high-performance streaming API (written in Rust) to achieve instant responses, while supporting end-to-end encryption and local API key management to ensure the privacy and security of user data. The product is completely open source and users are free to contribute, modify and deploy it. Its key benefits include zero-latency response, high configurability, and support for bring-your-own-key (BYOK), providing developers with great flexibility and control. DeepClaude is mainly aimed at developers and enterprises who need efficient code generation and AI reasoning capabilities. It is currently in the free trial stage and may be charged based on usage in the future.

Confucius-o1-14B is an inference model developed by NetEase Youdao team and optimized based on Qwen2.5-14B-Instruct. It adopts a two-stage learning strategy that can automatically generate reasoning chains and summarize the step-by-step problem-solving process. This model is mainly oriented to the education field, and is especially suitable for answering K12 mathematics problems. It can help users quickly obtain correct problem-solving ideas and answers. The model is lightweight and can be deployed on a single GPU without quantization, lowering the threshold for use. Its reasoning capabilities have performed well in internal evaluations, providing strong technical support for AI applications in the education field.

UI-TARS is a new GUI agent model developed by ByteDance that focuses on seamless interaction with graphical user interfaces through human-like perception, reasoning, and action capabilities. The model integrates key components such as perception, reasoning, localization, and memory into a single visual language model, enabling end-to-end task automation without the need for predefined workflows or manual rules. Its main advantages include powerful cross-platform interaction capabilities, multi-step task execution capabilities, and the ability to learn from synthetic and real data, making it suitable for a variety of automation scenarios, such as desktop, mobile, and web environments.

Gemini Flash Thinking is the latest AI model launched by Google DeepMind, designed for complex tasks. It can display the reasoning process and help users better understand the decision-making logic of the model. The model excels in mathematics and science, supporting long text analysis and code execution capabilities. It aims to provide developers with powerful tools to advance the application of artificial intelligence in complex tasks.

DeepSeek-R1-Distill-Llama-8B is a high-performance language model developed by the DeepSeek team, based on the Llama architecture and optimized for reinforcement learning and distillation. The model performs well in reasoning, code generation, and multilingual tasks, and is the first model in the open source community to improve reasoning capabilities through pure reinforcement learning. It supports commercial use, allows modifications and derivative works, and is suitable for academic research and corporate applications.

DeepSeek-R1-Distill-Qwen-14B is a distillation model based on Qwen-14B developed by the DeepSeek team, focusing on reasoning and text generation tasks. This model uses large-scale reinforcement learning and data distillation technology to significantly improve reasoning capabilities and generation quality, while reducing computing resource requirements. Its main advantages include high performance, low resource consumption, and broad applicability to scenarios that require efficient reasoning and text generation.

DeepSeek-R1-Distill-Llama-70B is a large language model developed by the DeepSeek team, based on the Llama-70B architecture and optimized through reinforcement learning. The model performs well in reasoning, conversational and multilingual tasks and supports a variety of application scenarios, including code generation, mathematical reasoning and natural language processing. Its main advantages are efficient reasoning capabilities and the ability to solve complex problems, while supporting open source and commercial use. This model is suitable for enterprises and research institutions that require high-performance language generation and reasoning capabilities.

Kimi k1.5 is a multi-modal language model developed by MoonshotAI. Through reinforcement learning and long context expansion technology, it significantly improves the model's performance in complex reasoning tasks. The model has reached industry-leading levels on multiple benchmarks, surpassing GPT-4o and Claude Sonnet 3.5 in mathematical reasoning tasks such as AIME and MATH-500. Its main advantages include an efficient training framework, powerful multi-modal reasoning capabilities, and support for long contexts. Kimi k1.5 is mainly targeted at application scenarios that require complex reasoning and logical analysis, such as programming assistance, mathematical problem solving, and code generation.

InternVL2.5-MPO is a series of multi-modal large-scale language models based on InternVL2.5 and Mixed Preference Optimization (MPO). It performs well on multi-modal tasks by integrating the newly incrementally pretrained InternViT with multiple pretrained large language models (LLMs), such as InternLM 2.5 and Qwen 2.5, using randomly initialized MLP projectors. This model series was trained on the multi-modal reasoning preference data set MMPR, which contains approximately 3 million samples. Through effective data construction processes and hybrid preference optimization technology, the model's reasoning capabilities and answer quality are improved.

InternLM3-8B-Instruct is a large language model developed by the InternLM team with excellent reasoning capabilities and knowledge-intensive task processing capabilities. While using only 4 trillion high-quality words for training, this model achieves a training cost that is more than 75% lower than models of the same level. At the same time, it surpasses models such as Llama3.1-8B and Qwen2.5-7B in multiple benchmark tests. It supports deep thinking mode, can solve complex reasoning tasks through long thinking chains, and also has smooth user interaction capabilities. This model is open source based on the Apache-2.0 license and is suitable for various application scenarios that require efficient reasoning and knowledge processing.

Eurus-2-7B-SFT is a large language model fine-tuned based on the Qwen2.5-Math-7B model, focusing on improving mathematical reasoning and problem-solving capabilities. This model learns reasoning patterns through imitation learning (supervised fine-tuning), and can effectively solve complex mathematical problems and programming tasks. Its main advantage lies in its strong reasoning ability and accurate processing of mathematical problems, and is suitable for scenarios that require complex logical reasoning. This model was developed by the PRIME-RL team and aims to improve the model's reasoning capabilities through implicit rewards.

HuatuoGPT-o1-70B is a large-scale language model (LLM) in the medical field developed by Freedom Intelligence, specially designed for complex medical reasoning. The model generates a complex thought process that reflects and refines its reasoning before providing a final response. HuatuoGPT-o1-70B is able to handle complex medical problems and provide thoughtful answers, which is crucial to improving the quality and efficiency of medical decision-making. The model is based on the LLaMA-3.1-70B architecture, supports English, and can be deployed on a variety of tools, such as vllm or Sglang, or directly for inference.

HuatuoGPT-o1-7B is a large-scale language model (LLM) in the medical field developed by Freedom Intelligence, specially designed for advanced medical reasoning. The model generates complex thought processes that reflect and refine its reasoning before providing a final answer. HuatuoGPT-o1-7B supports Chinese and English, can handle complex medical problems, and outputs results in a 'think-and-answer' format, which is crucial for improving the transparency and reliability of medical decision-making. The model is based on Qwen2.5-7B and has been specially trained to adapt to the needs of the medical field.

HuatuoGPT-o1-8B is a large language model (LLM) in the medical field designed for advanced medical reasoning. It generates a complex thought process that reflects and refines its reasoning before providing a final response. The model is built based on LLaMA-3.1-8B, supports English, and adopts the 'thinks-before-it-answers' method. The output format includes the reasoning process and final response. This model is of great significance in the medical field because of its ability to handle complex medical problems and provide thoughtful answers, which is crucial to improving the quality and efficiency of medical decision-making.

InternVL2-8B-MPO is a multimodal large language model (MLLM) that enhances the model's multimodal reasoning capabilities by introducing a mixed preference optimization (MPO) process. This model designed an automated preference data construction pipeline in terms of data, and built MMPR, a large-scale multi-modal reasoning preference data set. In terms of models, InternVL2-8B-MPO is initialized based on InternVL2-8B and fine-tuned using the MMPR data set, showing stronger multi-modal reasoning capabilities and fewer hallucinations. The model achieved an accuracy of 67.0% on MathVista, surpassing InternVL2-8B by 8.7 points, and its performance was close to InternVL2-76B, which is 10 times larger.

Gemini 2.0 Flash Thinking Mode is an experimental AI model launched by Google, designed to generate the model's "thinking process" during the response process. Compared with the basic Gemini 2.0 Flash model, Thinking Mode shows stronger reasoning capabilities in response. This model is available in both Google AI Studio and Gemini API. It is an important technical achievement of Google in the field of artificial intelligence. It provides developers and researchers with a powerful tool to explore and implement complex AI applications.

Gemini 2.0 is the latest AI model launched by Google DeepMind, aiming to provide support for the "intelligent assistant era". The model has been upgraded in multi-modal capabilities, including native image and audio output and tool usage capabilities, making building a new AI smart assistant closer to the vision of a universal assistant. The release of Gemini 2.0 marks Google's in-depth exploration and continuous innovation in the field of AI. By providing more powerful information processing and output capabilities, it makes information more useful and brings users a more efficient and convenient experience.

MAmmoTH-VL is a large-scale multi-modal reasoning platform that significantly improves the performance of multi-modal large language models (MLLMs) in multi-modal tasks through instruction tuning technology. The platform uses open models to create a dataset of 12 million command-response pairs, covering diverse, inference-intensive tasks with detailed and faithful justification. MAmmoTH-VL achieved state-of-the-art performance on benchmarks such as MathVerse, MMMU-Pro and MuirBench, demonstrating its importance in education and research.

Deepthought-8B is a small but powerful inference model built on LLaMA-3.1 8B and aims to make AI inference more transparent and controllable. Although the model is relatively small, it achieves complex inference capabilities comparable to larger models. Designed with its unique problem-solving approach, the model breaks down its thinking process into clear, unique, documented steps, and outputs the reasoning process in a structured JSON format, making it easy to understand and verify its decision-making process.

Skywork-o1-Open-Llama-3.1-8B is a series of models developed by Kunlun Technology’s Skywork team that combine o1-style slow thinking and reasoning capabilities. This family of models not only exhibits innate thinking, planning and reflection abilities in their output, but also significantly improves their reasoning skills on standard benchmark tests. This series represents a strategic advance in AI capabilities, pushing an otherwise weak base model to the state of the art (SOTA) for inference tasks.

QwQ-32B-Preview is an experimental research model developed by the Qwen team to improve the reasoning capabilities of artificial intelligence. The model demonstrates promising analytical capabilities but also has some important limitations. The model performed well in mathematics and programming, but there was room for improvement in common sense reasoning and nuanced language understanding. The model uses a transformers architecture with 32.5B parameters, 64 layers, and 40 attention heads (GQA). Product background information shows that QwQ-32B-Preview is a further development based on the Qwen2.5-32B model and has deeper language understanding and generation capabilities.

DeepSeek-R1-Lite-Preview is an AI model focused on improving reasoning capabilities. It has demonstrated excellent performance in the AIME and MATH benchmarks. The model has a real-time and transparent thinking process, and there are plans to launch open source models and APIs. The reasoning ability of DeepSeek-R1-Lite-Preview steadily improves with the increase of thinking length, showing better performance. Product background information shows that DeepSeek-R1-Lite-Preview is the latest product launched by DeepSeek, aiming to improve users' work efficiency and problem-solving abilities through artificial intelligence technology. Currently, the product offers a free trial, and specific pricing and positioning information has not yet been announced.

Mistral-Large-Instruct-2411 is a large language model with 123B parameters provided by Mistral AI. It has the most advanced capabilities in reasoning, knowledge, encoding, etc. The model supports multiple languages ​​and is trained on more than 80 programming languages, including but not limited to Python, Java, C, C++, etc. It is agent-centric and has native function calling and JSON output capabilities, making it an ideal choice for scientific research and development.

Hermes 3 is the latest version of the Hermes series of large language models (LLM) launched by Nous Research. Compared with Hermes 2, it has significant improvements in agent capabilities, role playing, reasoning, multi-turn dialogue, and long text coherence. The core concept of the Hermes series of models is to align LLM with users, giving end users powerful guidance capabilities and control. Based on Hermes 2, Hermes 3 further enhances function calling and structured output capabilities, and improves general assistant capabilities and code generation skills.

Llama-3.1-Nemotron-70B-Instruct is a large language model customized by NVIDIA, focusing on improving the helpfulness of answers generated by large language models (LLM). The model performs well on multiple automatic alignment benchmarks, such as Arena Hard, AlpacaEval 2 LC, and GPT-4-Turbo MT-Bench. It is trained on the Llama-3.1-70B-Instruct model by using RLHF (specifically the REINFORCE algorithm), Llama-3.1-Nemotron-70B-Reward and HelpSteer2-Preference hints. This model not only demonstrates NVIDIA's technology in improving the helpfulness of common domain instruction compliance, but also provides a model transformation format that is compatible with the HuggingFace Transformers code library and can be used for free managed inference through NVIDIA's build platform.

Open O1 is an open source project that aims to match the proprietary and powerful O1 model capabilities through open source innovation. The project gives these smaller models more powerful long-term reasoning and problem-solving capabilities by curating a set of O1-style thinking data for training LLaMA and Qwen models. As the Open O1 project progresses, we will continue to push what is possible with large language models, and our vision is to create a model that not only achieves O1-like performance, but also leads in test-time scalability, making advanced AI capabilities available to everyone. Through community-driven development and a commitment to ethical practices, Open O1 will become a cornerstone of AI progress, ensuring that the future development of the technology is open and beneficial to all.

Show-Me is an open source application designed to provide a visual and transparent alternative to traditional large language model interactions such as ChatGPT. It enables users to understand the step-by-step thought process of language models by decomposing complex problems into a series of reasoning subtasks. The application uses LangChain to interact with language models and visualize the inference process through a dynamic graphical interface.

Phi-3.5-MoE-instruct is a lightweight, multi-language AI model developed by Microsoft. It is built based on high-quality, inference-intensive data and supports a context length of 128K. The model undergoes a rigorous enhancement process, including supervised fine-tuning, proximal policy optimization, and direct preference optimization to ensure precise instruction following and strong safety measures. It is designed to accelerate research on language and multimodal models as building blocks for generative AI capabilities.

Phi-3.5-mini-instruct is a lightweight, multi-language advanced text generation model built by Microsoft based on high-quality data. It focuses on providing high-quality inference-intensive data, supports a token context length of 128K, and undergoes a rigorous enhancement process, including supervised fine-tuning, proximal policy optimization and direct preference optimization to ensure precise instruction compliance and strong security measures.

Grok-2 is xAI’s cutting-edge language model with state-of-the-art inference capabilities. This release includes two members of the Grok family: Grok-2 and Grok-2 mini. Both models are now released on the 𝕏 platform for Grok users. Grok-2 is a significant advancement from Grok-1.5, with cutting-edge capabilities in chatting, programming, and inference. At the same time, xAI introduces the Grok-2 mini, a small but powerful brother model of the Grok-2. An early version of Grok-2 has been tested on the LMSYS leaderboard under the name "sus-column-r". It surpasses Claude 3.5 Sonnet and GPT-4-Turbo in terms of overall Elo score.

Tost AI is a free, non-profit, open source service that provides inference services for the latest AI papers, using non-profit GPU clusters. Tost AI does not store any inference data and all data expires within 12 hours. Additionally, Tost AI provides the option to send data to a Discord channel. Each account is provided with 100 free wallet balances per day. If you want to get 1100 wallet balances per day, you can subscribe to GitHub Patron or Patreon. Tost AI sends all profits from the demo to the paper's first author, with a budget supported by corporate and individual sponsors.

Mistral-Large-Instruct-2407 is an advanced large language model (LLM) with 123B parameters, equipped with the latest reasoning, knowledge and programming capabilities. It supports multiple languages, including ten languages ​​including Chinese, English, French, etc., and is trained on more than 80 programming languages, such as Python, Java, etc. In addition, it has agent-centered abilities and advanced math and reasoning abilities.

Mathstral 7B is a model focused on math and science tasks, based on Mistral 7B. The model excels in text generation and reasoning in mathematics and science, and is suitable for application scenarios that require highly precise and complex calculations. The model's development team includes a number of experts, ensuring its leading position and reliability in the industry.

vLLM is a fast, easy-to-use, and efficient library for inferring and serving large language models (LLM). It provides high-performance inference services by using the latest service throughput technology, efficient memory management, continuous batch processing of requests, CUDA/HIP graph fast model execution, quantization technology, optimized CUDA kernels, etc. vLLM supports seamless integration with the popular HuggingFace model, supports multiple decoding algorithms, including parallel sampling, beam search, etc., supports tensor parallelism, is suitable for distributed reasoning, supports streaming output, and is compatible with the OpenAI API server. Additionally, vLLM supports NVIDIA and AMD GPUs, as well as experimental prefix caching and multi-LORA support.

"Soup is Hot" is an AI-driven Turtle Soup game platform designed to provide users with a game experience full of suspense and reasoning fun. Users can deduce the truth behind the story by asking questions, challenging their logical thinking and imagination. Part of the story contains elements of horror and gore, adding to the excitement of the game.

Higgs-Llama-3-70B is a post-training model based on Meta-Llama-3-70B, specifically optimized for role-playing while remaining competitive in general domain instruction execution and reasoning. Through supervised fine-tuning, the model combines human annotators and private large-scale language models to build preference pairs, and performs iterative preference optimization to align model behavior and make it closer to system messages. The Higgs model follows its roles more closely than other imperative models.

Phi-3 Vision is a lightweight, state-of-the-art open multi-modal model built on datasets including synthetic data and filtered publicly available websites, focusing on very high-quality inference-intensive data for text and vision. This model belongs to the Phi-3 model family. The multi-modal version supports 128K context length (in tokens). It has undergone a rigorous enhancement process that combines supervised fine-tuning and direct preference optimization to ensure precise instruction following and strong security measures.

Fireworks partners with the world's leading generative AI researchers to deliver the best models at the fastest speed. Get models carefully selected and optimized by Fireworks, along with enterprise-grade throughput and professional technical support. Positioned as the fastest and most reliable AI platform.

Grok-1.5 is an advanced large-scale language model with excellent long text understanding and reasoning capabilities. It can handle long contexts of up to 128,000 tokens, far exceeding the capabilities of previous models. In tasks such as math and coding, Grok-1.5 performed extremely well, achieving extremely high scores on multiple recognized benchmarks. The model is built on a powerful distributed training framework to ensure an efficient and reliable training process. Grok-1.5 aims to provide users with powerful language understanding and generation capabilities to assist various complex language tasks.

OpenDiT is an open source project that provides a high-performance implementation of Colossal-AI-based Diffusion Transformer (DiT), specifically designed to enhance training and inference efficiency for DiT applications, including text-to-video generation and text-to-image generation. OpenDiT improves performance through the following technologies: up to 80% acceleration and 50% memory reduction on the GPU; including FlashAttention, Fused AdaLN and Fused layernorm core optimization; including hybrid parallel methods of ZeRO, Gemini and DDP, as well as sharding the ema model to further reduce memory costs; FastSeq: a novel sequence parallel method, especially suitable for workloads such as DiT, where the activation size is large but the parameter size is small; single-node sequence parallelism can save up to 48% of communication costs; breaking through a single GPU memory constraints, reducing overall training and inference time; Huge performance improvements with small code modifications; Users do not need to know the implementation details of distributed training; Complete text-to-image and text-to-video generation process; Researchers and engineers can easily use and adapt our process to practical applications without modifying the parallel part; Perform text-to-image training on ImageNet and publish checkpoints.

ReadAgent is a simple prompting system that leverages the advanced language capabilities of large language models (LLMs) to decide what content to store in memory sets, condenses these memory sets into short recollections called gist memories, and takes action to consult the original text when the ReadAgent needs to remind itself of relevant details to complete a task. ReadAgent can effectively reason about very long contexts by using gist memory to capture global context and focus on local details. It is efficient in terms of the amount of information that needs to be processed at once, which is also important for understanding.

ReFT is a simple and effective way to enhance the inference capabilities of large language models (LLMs). It first warms up the model through supervised fine-tuning (SFT), and then uses online reinforcement learning, specifically the PPO algorithm in this article, to further fine-tune the model. ReFT significantly outperforms SFT by automatically sampling a large number of reasoning paths for a given problem and naturally deriving rewards from real answers. The performance of ReFT may be further improved by incorporating inference-time strategies such as majority voting and re-ranking. It is important to note that ReFT improves by learning the same training problem as SFT without relying on additional or enhanced training problems. This shows that ReFT has stronger generalization ability.

AlphaGeometry is an AI system for geometric problems that surpasses the current state of the art. It can solve complex geometric problems by combining the predictive power of neural language models and a rule-driven reasoning engine. The system uses a neurosemiotic approach, consisting of a neural language model and a symbolic reasoning engine, which work together to find proofs of complex geometric theorems. By generating 1 billion random geometric object shapes and deriving all relationships from them, we ended up with 100 million unique training samples, 9 million of which contained additional constructs. AlphaGeometry's language model can make good suggestions when facing the geometry problems of the International Mathematical Olympiad. The system has become the world's first AI model capable of reaching the bronze medal level of the International Mathematical Olympiad.

This is an efficient LLM inference solution implemented on Intel GPUs. By simplifying the LLM decoder layer, using a segmented KV caching strategy and a custom Scaled-Dot-Product-Attention kernel, the solution achieves up to 7x lower token latency and 27x higher throughput on Intel GPUs compared to the standard HuggingFace implementation. Please refer to the official website for detailed functions, advantages, pricing and positioning information.

Phi-2 is a language model with 270 million parameters. Through high-quality data and innovative technology, it achieves performance beyond the scale of the model. In complex language understanding and reasoning tests, it can match or exceed models that are 25 times larger in size.

Google Gemini is a multimodal-based AI model that can seamlessly reason about images, video, audio, and code. Gemini is DeepMind’s most advanced AI model, capable of outperforming human experts in various tests including MMLU (Massive Multi-Task Language Understanding). Gemini has excellent inference capabilities and achieves state-of-the-art performance in a variety of multi-modal tasks.

Orca 2 is an assistant for research purposes that helps with reasoning and comprehension tasks such as data reasoning, reading comprehension, mathematical problem solving, and text summarization by providing single-turn responses. The model is particularly good at reasoning. We are publicly releasing Orca 2 to stimulate further research in developing, evaluating, and aligning smaller language models.

Flash-Decoding is a technology for long-context reasoning that can significantly accelerate the attention mechanism in reasoning, resulting in 8 times faster generation. This technique enables faster inference by loading keys and values ​​in parallel, then rescaling and combining the results separately to maintain the correct attention output. Flash-Decoding is suitable for large language models and can handle long contexts such as long documents, long conversations, or entire code bases. Flash-Decoding is already provided in the FlashAttention package and xFormers, which can automatically select the Flash-Decoding or FlashAttention method, or use the efficient Triton kernel.

MathCoder is a mathematical reasoning tool based on an open source language model. Through fine-tune models and the generation of high-quality data sets, it realizes the alternation of natural language, code and execution results, and improves mathematical reasoning capabilities. MathCoder models achieve state-of-the-art top scores on MATH and GSM8K datasets, far outperforming other open source alternatives. The MathCoder model not only surpasses ChatGPT-3.5 and PaLM-2 on GSM8K and MATH, but also surpasses GPT-4 on the competition-level MATH data set.

Stability AI generative model is an open source generative model library that provides training, inference and application functions for various generative models. The library supports the training of various generative models, including training based on PyTorch Lightning, and provides rich configuration options and modular design. Users can use this library to train generative models and perform inference and application through the provided models. The library also provides sample training configuration and data processing functions to facilitate users to quickly get started and customize.