# Language Model

Search-R1 is a reinforcement learning framework designed to train large language models (LLMs) capable of reasoning and calling search engines. Built upon veRL, it supports various reinforcement learning methods and different LLM architectures, enabling efficiency and scalability in tool-augmented reasoning research and development.

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

Fin-R1 is a large language model designed specifically for the financial field, aimed at enhancing financial reasoning capabilities. Jointly developed by Shanghai University of Finance and Economics and Caiyue Xingchen, it is fine-tuned and reinforced learning based on Qwen2.5-7B-Instruct, possessing efficient financial reasoning capabilities and is suitable for core financial scenarios such as banking and securities. This model is free and open-source, facilitating user adoption and improvement.

Jamba 1.6 is AI21's latest language model, designed for private enterprise deployment. It excels in long-text processing, handling context windows up to 256K. Employing a hybrid SSM-Transformer architecture, it efficiently and accurately processes long-text question-answering tasks. This model surpasses similar models from Mistral, Meta, and Cohere in quality, while supporting flexible deployment options, including private deployment on-premise or in a VPC, ensuring data security. It offers enterprises a solution that doesn't compromise between data security and model quality, suitable for scenarios requiring extensive data and long-text processing, such as R&D, legal, and finance. Jamba 1.6 is currently used in several enterprises, such as Fnac for data classification and Educa Edtech for building personalized chatbots.

Inception Labs is a company focused on developing diffusion-based large language models (dLLMs). Its technology is inspired by advanced image and video generation systems such as Midjourney and Sora. Through diffusion models, Inception Labs offers speeds 5-10 times faster than traditional autoregressive models, higher efficiency, and stronger generative control. Its models support parallel text generation, can correct errors and hallucinations, are suitable for multimodal tasks, and excel in reasoning and structured data generation. The company is composed of researchers and engineers from Stanford, UCLA, and Cornell University and is a pioneer in the field of diffusion models.

OpenManus is an open-source intelligent agent project aimed at achieving functionalities similar to Manus through open-source means, but without requiring an invitation code. Developed collaboratively by multiple developers, it leverages a powerful language model and a flexible plugin system to quickly implement various complex tasks. The main advantages of OpenManus are that it is open-source, free, and easily extensible, making it suitable for developers and researchers for secondary development and research. The project background stems from a need to improve existing intelligent agent tools, with the goal of creating a completely open and easy-to-use intelligent agent platform.

Instella is a series of high-performance open-source language models developed by the AMD GenAI team, trained on AMD Instinct? MI300X GPUs. This model significantly outperforms other open-source language models of the same size and is comparable in functionality to models like Llama-3.2-3B and Qwen2.5-3B. Instella provides model weights, training code, and training data, aiming to promote the development of open-source language models. Its main advantages include high performance, open-source availability, and optimized support for AMD hardware.

GPT-4.5 is the latest language model released by OpenAI, representing the forefront of current unsupervised learning technology. Trained on massive computation and data, the model has enhanced its understanding of world knowledge and pattern recognition capabilities, reduced hallucination, and enables more natural human interaction. It excels in writing, programming, and problem-solving tasks, particularly suitable for scenarios requiring high creativity and emotional understanding. GPT-4.5 is currently in research preview and is open to Pro users and developers to explore its potential.

Gemini 2.0 Flash-Lite is a highly efficient language model from Google, optimized for long-text processing and complex tasks. It excels in inference, multi-modality, mathematical, and factuality benchmark tests, featuring a simplified pricing strategy that makes million-context windows more affordable. Gemini 2.0 Flash-Lite is fully available in Google AI Studio and Vertex AI, suitable for enterprise-level production use.

Phi-4-mini-instruct is a lightweight, open-source language model from Microsoft, belonging to the Phi-4 model family. Trained on synthetic data and curated data from publicly available websites, it focuses on high-quality, inference-intensive data. The model supports 128K token context length and enhances instruction following capabilities and safety through supervised fine-tuning and direct preference optimization. Phi-4-mini-instruct excels in multilingual support, inference capabilities (especially mathematical and logical reasoning), and low-latency scenarios, making it suitable for resource-constrained environments. Released in February 2025, it supports multiple languages including English, Chinese, and Japanese.

DeepSeek is an advanced language model developed by a Chinese AI lab supported by the High-Flyer Foundation. It focuses on open-source models and innovative training methods. Its R1 series models demonstrate exceptional performance in logical reasoning and problem-solving, employing reinforcement learning and a mixture-of-experts framework to optimize performance and achieve efficient training at low cost. DeepSeek's open-source strategy has fostered community innovation while sparking industry discussion on AI competition and the impact of open-source models. Its free and registration-free access further lowers the barrier to entry, making it suitable for a wide range of applications.

AlphaMaze is a project focused on enhancing the visual reasoning abilities of Large Language Models (LLMs). It trains models through maze tasks described in text format, enabling them to understand and plan in spatial structures. This method avoids complex image processing and directly assesses the model's spatial understanding through text descriptions. Its main advantage is the ability to reveal how the model thinks about spatial problems, rather than simply whether it can solve them. The model is based on open-source frameworks and aims to promote research and development of language models in the field of visual reasoning.

AlphaMaze is a decoder language model designed specifically for solving visual reasoning tasks. It demonstrates the potential of language models in visual reasoning through training on maze-solving tasks. The model is built upon the 1.5 billion parameter Qwen model and is trained with Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL). Its main advantage lies in its ability to transform visual tasks into text format for reasoning, thereby compensating for the lack of spatial understanding in traditional language models. The development background of this model is to improve AI performance in visual tasks, especially in scenarios requiring step-by-step reasoning. Currently, AlphaMaze is a research project, and its commercial pricing and market positioning have not yet been clearly defined.

Smithery is a platform based on the Model Context Protocol that allows users to extend the functionality of language models by connecting various servers. It provides a flexible toolkit enabling users to dynamically enhance their language models' capabilities to better accomplish a variety of tasks. The core strengths of the platform lie in its modularity and scalability, allowing users to integrate suitable servers according to their needs.

Moonlight-16B-A3B is a large-scale language model developed by Moonshot AI, trained using the advanced Muon optimizer. By optimizing training efficiency and performance, this model significantly enhances language generation capabilities. Key advantages include an efficient optimizer design, fewer training FLOPs, and superior performance. The model is suitable for scenarios requiring efficient language generation, such as natural language processing, code generation, and multilingual dialogue. Its open-source implementation and pre-trained models provide powerful tools for researchers and developers.

DeepHermes 3 is an advanced language model developed by NousResearch, designed to enhance answer accuracy through systematic reasoning. It supports both a reasoning mode and a regular response mode, which users can switch between using system prompts. This model excels in multi-turn conversations, role-playing, and reasoning tasks, aiming 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, and function calling.

Lora is a local language model optimized for mobile devices, which can be quickly integrated into mobile applications via its SDK. Supporting iOS and Android platforms, its performance is comparable to GPT-4o-mini, featuring a 1.5GB size and 2.4 billion parameters, and is specifically optimized for real-time mobile inference. Lora’s key advantages include low energy consumption, lightweight design, and rapid response times. Compared to other models, it demonstrates significant advantages in energy consumption, size, and speed. Provided by PeekabooLabs, Lora primarily targets developers and enterprise clients, helping them quickly integrate advanced language model capabilities into mobile applications to enhance user experience and application competitiveness.

PaliGemma 2 mix is an upgraded vision language model from Google, belonging to the Gemma family. It can handle various vision and language tasks, such as image segmentation, video captioning, and scientific question answering. The model provides pre-trained checkpoints in different sizes (3B, 10B, and 28B parameters), making it easy to fine-tune for a variety of visual language tasks. Its main advantages are versatility, high performance, and developer-friendliness, supporting multiple frameworks (such as Hugging Face Transformers, Keras, PyTorch, etc.). This model is suitable for developers and researchers who need to efficiently process vision and language tasks, significantly improving development efficiency.

Mistral Saba is Mistral AI's first customized language model specifically for the Middle East and South Asia. This 24 billion-parameter model is trained on a carefully curated dataset, providing more accurate, relevant, and cost-effective responses compared to similar large models. It supports Arabic and various Indic languages, excelling in South Indian languages such as Tamil. Mistral Saba is suitable for scenarios requiring precise language understanding and cultural context support. Available via API and for local deployment, it features a lightweight design, single GPU system deployment, and rapid response, making it ideal for enterprise-level applications.

OLMoE, developed by Ai2, is an open-source language model application aimed at providing researchers and developers a fully open toolkit for conducting AI experiments on-device. The app supports offline operation on iPhone and iPad, ensuring complete privacy for user data. Built upon the efficient OLMoE model, it maintains high performance on mobile devices through optimization and quantization. The open-source nature of the application makes it a vital foundation for the research and development of the next generation of on-device AI applications.

Developed by the xwen-team, Xwen-Chat is created to meet the demand for high-quality Chinese dialogue models, filling a gap in the field. With several versions available, it has robust language comprehension and generation capabilities, capable of handling complex language tasks and generating natural dialogue content. This model is suitable for scenarios such as smart customer service and is available for free on the Hugging Face platform.

The Exa & Deepseek Chat App is an open-source chat application designed to perform real-time web searches through Exa's API, combined with the Deepseek R1 language model for inference, to offer a more accurate chatting experience. Built with Next.js, TailwindCSS, and TypeScript, and hosted on Vercel, it allows users to access the latest web information during chats and engage in intelligent conversations powered by a robust language model. This application is freely available as open-source, suitable for both developers and business users, and can serve as a foundation for developing chat tools.

This product is a 4-bit quantized language model based on Qwen2.5-32B, achieving efficient inference and low resource consumption through GPTQ technology. It significantly reduces the model's storage and computational demands while maintaining high performance, making it suitable for use in resource-constrained environments. The model primarily targets applications requiring high-performance language generation, including intelligent customer service, programming assistance, and content creation. Its open-source license and flexible deployment options offer broad prospects for application in both commercial and research fields.

ReaderLM v2, introduced by Jina AI, is a small language model with 1.5 billion parameters, specifically designed for converting HTML to Markdown and extracting HTML to JSON with exceptional accuracy. The model supports 29 languages and can handle input/output combinations of up to 512,000 tokens in length. It employs a new training paradigm and higher-quality training data, making significant advances over its predecessor in handling long text and generating Markdown syntax, allowing for proficient use of Markdown syntax and the creation of complex elements. Additionally, ReaderLM v2 features direct HTML to JSON generation capabilities, enabling users to extract specific information from raw HTML based on a provided JSON schema, eliminating the need for intermediate Markdown conversion.

MiniMax-01 is a robust language model with a total of 456 billion parameters, where each token activates 45.9 billion parameters. It employs a hybrid architecture that combines lightning attention, softmax attention, and mixture of experts (MoE). Through advanced parallel strategies and innovative computation-communication overlap methods, such as Linear Attention Sequence Parallelism (LASP+), variable-length ring attention, and expert tensor parallelism (ETP), it extends the training context length to 1 million tokens and can process contexts of up to 4 million tokens during inference. MiniMax-01 has demonstrated top-tier model performance across multiple academic benchmarks.

MiniCPM-o 2.6 is the latest and most powerful model in the MiniCPM-o series. Built upon SigLip-400M, Whisper-medium-300M, ChatTTS-200M, and Qwen2.5-7B, it boasts 8 billion parameters. It excels in visual understanding, speech interaction, and multimodal live broadcasting, supporting real-time voice conversations and diverse live streaming features. The model performs excellently in the open-source community, surpassing several well-known models. Its strengths include efficient inference speed, low latency, and minimal memory and power consumption, allowing for effective multimodal live streaming on devices such as iPads. Moreover, MiniCPM-o 2.6 is user-friendly, supporting multiple usage approaches including CPU inference with llama.cpp, quantized models in int4 and GGUF formats, and high-throughput inference with vLLM.

MiniCPM-o 2.6 is the latest multimodal large language model (MLLM) developed by the OpenBMB team, featuring 8 billion parameters and capable of high-quality visual, voice, and multimodal interactions on edge devices like smartphones. This model is built on SigLip-400M, Whisper-medium-300M, ChatTTS-200M, and Qwen2.5-7B, trained in an end-to-end manner, and performs comparably to GPT-4o-202405. Its main advantages include leading visual capabilities, advanced voice functionality, powerful multimodal streaming abilities, impressive OCR performance, and superior efficiency. The model is open-source and free to use for academic research and commercial purposes.

The PatronusAI/Llama-3-Patronus-Lynx-70B-Instruct is a large language model built on the Llama-3 architecture, designed to address hallucination issues in RAG settings. By analyzing provided documents, questions, and answers, this model assesses whether the answers are faithful to the document's content. Its primary advantages include high precision in hallucination detection and strong language comprehension capabilities. Developed by Patronus AI, this model is well-suited for scenarios necessitating high-precision information verification, such as financial analysis and medical research. It is currently free to use, but specific commercial applications may require direct contact with the developers.

CAG (Cache-Augmented Generation) is an innovative enhancement technique for language models aimed at addressing issues such as retrieval delays, errors, and complexity inherent in traditional RAG (Retrieval-Augmented Generation) methods. By preloading all relevant resources and caching their runtime parameters within the model context, CAG can generate responses directly during inference without requiring real-time retrieval. This approach significantly reduces latency, increases reliability, and simplifies system design, making it a practical and scalable alternative. As the context window of large language models (LLMs) continues to expand, CAG is expected to be applicable in more complex scenarios.

PRIME-RL/Eurus-2-7B-PRIME is a language model with 7 billion parameters, trained on the PRIME methodology with the aim of improving reasoning abilities via online reinforcement learning. Starting from the Eurus-2-7B-SFT model, this model was fine-tuned using the Eurus-2-RL-Data dataset. The PRIME methodology employs an implicit reward system, fostering an emphasis on the reasoning process during output generation, rather than focusing solely on the results. This model has demonstrated exceptional performance in various reasoning benchmark tests, achieving an average improvement of 16.7% over its SFT version. Key advantages include enhanced reasoning capabilities, lower data and resource requirements, and outstanding performance in mathematical and programming tasks. It is well-suited for scenarios requiring complex reasoning abilities, such as programming and mathematical problem solving.