# Dialogue System

Amazon Nova Sonic is a cutting-edge foundational model that integrates speech understanding and generation, enhancing the natural fluency of human-computer dialogue. This model overcomes the complexities of traditional voice applications, achieving a deeper level of communication understanding through a unified architecture. It is suitable for AI applications across multiple industries and holds significant commercial value. As AI technology continues to develop, Nova Sonic will provide customers with better voice interaction experiences and improved service efficiency.

DeepSeek-V3-0324 is an advanced text generation model with 68.5 billion parameters, using BF16 and F32 tensor types, enabling efficient inference and text generation. The model's main advantages lie in its powerful generation capabilities and open-source nature, allowing it to be widely applied to various natural language processing tasks. The model is positioned to provide developers and researchers with a powerful tool to help them achieve breakthroughs in the field of text generation.

Meta Llama 3.3 is a state-of-the-art multilingual large pre-trained language model (LLM) with 70 billion parameters, specifically optimized for multilingual dialogue use cases. It outperforms many existing open-source and proprietary chat models on common industry benchmarks. The model utilizes an optimized Transformer architecture, along with supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF) to enhance its usefulness and safety according to human preferences.

Ferret-UI is the first multimodal large language model (MLLM) centered on user interfaces, specifically designed for gesture expression, localization, and reasoning tasks. Built on Gemma-2B and Llama-3-8B, it is capable of performing complex user interface tasks. This version aligns with Apple's research paper and serves as a powerful tool for image-to-text tasks, excelling in dialogue and text generation.

Developed by Meta, Meta-spirit-lm is an advanced natural language processing model released on the Hugging Face platform. This model excels in handling language-related tasks such as text generation, translation, and question answering. Its significance lies in its ability to understand and generate natural language, significantly advancing AI in the field of language understanding. The model has garnered extensive attention in the open-source community and can be used for research and commercial purposes, provided that the FAIR Noncommercial Research License is adhered to.

MiniCPM3-4B is the third generation of the MiniCPM series, demonstrating overall performance that exceeds both Phi-3.5-mini-Instruct and GPT-3.5-Turbo-0125, comparable to many recent 7B to 9B models. Compared to its predecessors, MiniCPM3-4B features enhanced versatility, supporting function calling and code interpretation, making it applicable across a wider range of scenarios. Additionally, MiniCPM3-4B boasts a 32k context window, which, combined with LLMxMapReduce technology, theoretically enables the processing of infinite context without requiring extensive memory.

InternLM-XComposer-2.5 is a multifunctional large visual language model that supports long context input and output. It excels in various text-image understanding and generation applications, achieving performance comparable to GPT-4V while utilizing only 7B parameters for its LLM backend. Trained on 24K interleaved image-text context, the model seamlessly scales to 96K long context through RoPE extrapolation. This long context capability makes it particularly adept at tasks requiring extensive input and output context. Furthermore, it supports ultra-high resolution understanding, fine-grained video understanding, multi-turn multi-image dialogue, web page creation, and writing high-quality text-image articles.

Nemotron-4-340B-Instruct is a large language model (LLM) developed by NVIDIA, specifically optimized for English single-turn and multi-turn dialogue scenarios. This model supports a context length of 4096 tokens and has undergone additional alignment steps such as supervised fine-tuning (SFT), direct preference optimization (DPO), and reward-aligned preference optimization (RPO). Based on approximately 20K manually annotated data points, the model leveraged a data synthesis pipeline to generate over 98% of the data used for supervised fine-tuning and preference fine-tuning. This enables the model to exhibit strong performance in human-like conversational preferences, mathematical reasoning, coding, and instruction following, and it can also generate high-quality synthetic data for various use cases.

Dolphin 2.9.1 Mixtral 1x22b is a carefully trained and curated AI model by the Cognitive Computations team, based on the Dolphin-2.9-Mixtral-8x22b version. It is licensed under Apache-2.0. This model boasts a 64k context window, fine-tuned with full weights across a 16k sequence length, achieving 27 hours of training on 8 H100 GPUs. Dolphin 2.9.1 possesses diverse instruction following, dialogue, and coding abilities, along with preliminary agent capabilities and function call support. The model has not been reviewed, and the dataset has been filtered to remove alignment and bias, enhancing its compliance. It is recommended to implement your own alignment layer before making it publicly available as a service.

Developed by HPAI, Aloe is a medical language model optimized based on Meta Llama 3 8B. Through model fusion and advanced prompting strategies, it achieves state-of-the-art performance comparable to its scale. Aloe scores high on ethical and factual metrics, thanks to the combination of red teaming and alignment work. The model provides medical-specific risk assessments to promote the safe and responsible use and deployment of these systems.

DeepSeek-V2 is a mixed expert (MoE) language model consisting of 236B parameters, activated with 21B parameters per token. While maintaining cost-efficient training and efficient inference, it activates each token with 21B parameters. Compared to the previous DeepSeek 67B, DeepSeek-V2 offers superior performance while saving 42.5% of training costs, reducing 93.3% of KV cache, and increasing the maximum generation throughput by 5.76 times. The model has been pretrained on an 8.1 trillion token high-quality corpus and further optimized through supervised fine-tuning (SFT) and reinforcement learning (RL), performing exceptionally well in standard benchmark tests and open-source generation evaluations.

Llama-3 70B Instruct Gradient 1048k is an advanced language model developed by the Gradient AI team. By extending the context length to over 1048K, it demonstrates that SOTA (State of the Art) language models can learn to process long text after appropriate adjustments. The model employs NTK-aware interpolation and RingAttention technology, along with the EasyContext Blockwise RingAttention library, to efficiently train on high-performance computing clusters. It has widespread application potential in commercial and research applications, especially in scenarios requiring long text processing and generation.

gpt2-chatbot is a large language model based on the GPT-4 architecture, trained by OpenAI. It excels in dialogue and provides structured, in-depth answers while demonstrating excellent knowledge storage. The model is available for use in LMSYS's Direct Chat and Arena (Battle) modes, allowing users to communicate and evaluate without login.

Llama-3 8B Instruct 262k is a text generation model developed by the Gradient AI team, extending the context length of Llama-3 8B to over 160K and demonstrating the potential of state-of-the-art large language models in handling long text. This model achieves efficient learning on long texts through proper adjustment of the RoPE theta parameter, combined with NTK-aware interpolation and data-driven optimization techniques. Additionally, it is built upon the EasyContext Blockwise RingAttention library to support scalable and efficient training on high-performance hardware.

llama3-Chinese-chat is the first Chinese conversational version of llama3, designed specifically for Chinese users, supporting high-quality multi-turn dialogue. It has been trained on 170k+ Chinese conversation data and possesses features such as role-playing and agent ability enhancement. Detailed training and inference tutorials are also provided. Furthermore, the project plans to open-source a browser extension, adding AI note-taking and mind mapping functionalities to further enhance user experience.

Baidu Smart Cloud Kusuite, based on Baidu's Wenxin Yiyan large model, comprehensively reconstructs the intelligent customer service product series, covering customer service, intelligent marketing, and intelligent communication to meet enterprises' full-scenario intelligent customer service needs. Main products include: Intelligent Dialogue Platform (providing AI-powered high-intelligence customer service robots), Intelligent Outbound Platform (highly human-like voice dialogue marketing), Dialogue Insight Platform (dialogue data analysis insights and optimization recommendations), and Intelligent Communication Platform (integrated communication resource API access). Product advantages include: dialogue accuracy, friendliness, comprehensiveness based on large models, efficient and rapid online operation, seamless multi-channel integration and adaptability.

KwaiAgents is a series of open-source intelligent agent works from Kuaishou Technology's KwaiKEG. The open-source contents include: KAgentSys-Lite system: A streamlined version of the KAgentSys system in the paper; KAgentLMs series models: Large language models with agent functions such as planning, reflection, and tool usage; KAgentInstruct: Thousands of refined agent instruction data from the paper; KAgentBench: Over 3,000 human-assessed datasets for testing agents' planning, tool usage, reflection, summarization, and descriptive abilities, etc.

MiniGPT-5 employs an interleaved visual language generation technology based on generative vokens. It is capable of simultaneously generating textual narratives and corresponding images. The model adopts a two-stage training strategy, where the first stage focuses on undescribed multimodal generation training and the second stage on multimodal learning. The model has achieved good results in multimodal dialogue generation tasks.