🤔 **多模态 LLM** 能够整合多种类型的输入，包括文本、照片和视频，代表着人工智能的一项重大进步。这些模型在各种应用中具有极高的适应性，因为它们可以理解和生成跨多个模态的材料。例如，OpenAI 的 Sora 在文本到视频生成方面取得了重大进展，它使用多种视频和图像数据来训练文本条件扩散模型。Google 的 Gemini 多模态模型系列擅长理解和生成基于文本、音频、视频和图像的材料。LLaVA 是一种先进的 AI 模型，通过增强多模态学习能力来弥合语言和视觉理解之间的差距。

🔓 **开源 LLM** 使全球社区能够访问复杂的模型以及背后的训练过程，从而使 AI 研究民主化。例如，LLM360 致力于通过促进模型创建的完全透明度来改变 LLM 领域。LLaMA 是开源 LLM 的重大改进，它提供了一系列模型，参数范围从 7B 到 65B。AI2 的 OLMo（开放语言模型）为 7B 规模的模型提供了对训练代码、数据和模型权重的完全访问权限。Meta Llama-3 推出了 8B 和 70B 参数模型，针对各种应用进行了优化，并在推理和其他任务方面取得了最先进的性能。

🎯 **特定领域 LLM** 通过利用特定领域的数据和微调策略（例如编程和生物医学）来设计，以在特定任务中表现更好。这些模型不仅提高了工作效率，还展示了如何利用 AI 来解决各种专业领域中的复杂问题。例如，BioGPT 具有针对生物医学领域的独特架构，可以改善生物医学信息提取和文本合成等活动。StarCoder 专注于理解编程语言和生成代码。MathVista 解决了视觉理解和数学思维的融合。

🤖 **LLM 代理** 是由大型语言模型驱动的复杂 AI 系统。它们利用强大的语言能力在内容创作和客户服务等工作中蓬勃发展。例如，ChemCrow 将 18 种专门工具整合到一个平台中，改变了计算化学。ToolLLM 通过强调工具的可用性来改进开源 LLM。OS-Copilot 通过与操作系统交互来扩展 LLM 的功能，并创建了 FRIDAY，这是一种能够出色完成各种工作的自治代理。

🤏 **小型 LLM（包括量化 LLM）** 适用于资源受限的设备部署，因为它们服务于需要较低精度或较少参数的应用程序。例如，BitNet 是一种 1 位 LLM，它大大提高了成本效益。Gemma 1B 是基于 Gemini 系列技术的现代轻量级开放变体。Lit-LLaMA 旨在提供 LLaMA 源代码的原始、完全开放和安全的实现。

🧠 **非 Transformer LLM** 通过经常引入循环神经网络 (RNN) 等组件来偏离传统的 Transformer 架构。这些方法解决了 Transformer 的一些主要缺点和问题，例如昂贵的计算成本和对顺序数据的处理效率低下。例如，Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) are prominent examples of non-transformer architectures. These models address limitations of transformers in handling sequential data and long-term dependencies. They excel in tasks like natural language processing, time series analysis, and machine translation.

Large Language Models (LLMs) are rapidly developing with advances in both the models’ capabilities and applications across multiple disciplines. In a recent LinkedIn post, a user discussed recent trends in LLM research, including various types of LLMs and their examples. 

Multi-Modal LLMs 

With the ability to integrate several types of input, including text, photos, and videos, multimodal LLMs constitute a major advancement in artificial intelligence. These models are extremely adaptable for various applications since they can comprehend and generate material across multiple modalities. Multimodal LLMs are built to perform more complex and nuanced tasks, such as answering questions about images or producing in-depth video material based on textual descriptions, by utilizing large-scale training on a variety of datasets.

Examples – 

OpenAI’s Sora – Significant progress has been made in AI with OpenAI’s Sora, especially in text-to-video generation. This model uses a variety of video and image data, such as different durations, resolutions, and aspect ratios, to train text-conditional diffusion models. Sora generates high-fidelity films for up to one minute by processing spacetime patches of video and image latent codes using an advanced transformer architecture. 

Gemini  – Google’s  Gemini family of multimodal models is highly adept at comprehending and producing text, audio, video, and image-based material. Gemini, which comes in Ultra, Pro, and Nano versions, can handle various applications, from memory-constrained on-device use cases to sophisticated reasoning activities. The results of evaluations show that the Gemini Ultra model improves the state-of-the-art in all 20 multimodal benchmarks evaluated and reaches human-expert performance on the MMLU test benchmark, among other benchmarks, in 30 out of 32. 

LLaVA – LLaVA is an advanced AI model that bridges the gap between linguistic and visual understanding by improving multimodal learning capabilities. It is perfect for applications requiring a deep understanding of both formats since it can analyze and generate content combining text and images by integrating visual data into language models. 

Open-Source LLMs

Large Language Models that are available as open-source software have democratized AI research by enabling the world community to access sophisticated models and the training processes behind them. With this, transparent access is provided to model designs, training data, and code implementations. In addition to fostering cooperation and accelerating discovery, this transparency guarantees reproducibility in AI research. 

Examples 

LLM360  – LLMs are a field that LLM360 seeks to transform by promoting total transparency in model creation. This project exposes training data, code, and intermediate results along with final weights for models such as AMBER and CRYSTALCODER. Setting a new benchmark for ethical AI development, LLM360 encourages reproducibility and collaborative research by making the whole training process open-source.

LLaMA – With models ranging from 7B to 65B parameters, LLaMA is a substantial improvement in open-source LLMs. LLaMA-13B, which was trained only on publicly accessible datasets, has outperformed much bigger proprietary models across a range of benchmarks. This project demonstrates a dedication to openness and community-driven AI research.

OLMo – For 7B-scale models, AI2’s OLMo (Open Language Model) offers complete access to training code, data, and model weights. OLMo encourages advances in language model research by emphasizing openness and reproducibility, enabling researchers and academics to create together.

Llama-3 – Meta Llama, with its 8B and 70B parameter models optimized for various applications, has been introduced in Llama-3. These models set standards for open-source AI development across different fields with their state-of-the-art performance in reasoning and other tasks

Domain-specific LLMs

Domain-specific LLMs are designed to perform better in specialized tasks by utilizing domain-specific data and fine-tuning strategies, such as programming and biomedicine. These models not only enhance work performance but also show how AI may be used to solve complicated problems in a variety of professional fields.

Examples

BioGPT – With its unique architecture for the biomedical sector, BioGPT improves activities like biomedical information extraction and text synthesis. In a number of biomedical natural language processing tasks, it performs better than earlier models, proving its ability to comprehend and produce biomedical text efficiently.

StarCoder – StarCoder concentrates on understanding programming languages and generating code. It is highly proficient in software development activities because of its thorough training on big code datasets. It has strong capabilities for understanding complex programming logic and creating code snippets.

MathVista – MathVista tackles the confluence of visual comprehension and mathematical thinking. It shows improvements in handling mathematical and visual data handling in AI research and offers a standard for assessing LLMs on mathematical tasks. 

LLM Agents 

Large Language Models power LLM Agents, which are sophisticated AI systems. They use their strong language skills to flourish in jobs like content development and customer service. These agents process natural language queries and carry out tasks in various fields, such as making suggestions or producing artistic works. LLM Agents simplify interactions when they are integrated into applications like chatbots and virtual assistants. This shows how versatile they are and how they may improve user experiences in a variety of industries.

Examples

ChemCrow – ChemCrow unifies 18 specialized tools into a single platform, transforming computational chemistry. This LLM-based agent can independently synthesize insect repellents, organocatalysts, and new chromophores. It also excels in chemical synthesis, drug discovery, and materials design. ChemCrow uses external knowledge sources, which improves its performance in challenging chemical jobs, in contrast to standard LLMs. 

ToolLLM – ToolLLM improves on open-source LLMs by emphasizing the usability of tools. It uses ChatGPT for API gathering, instruction generation, and solution route annotation, along with ToolBench, an instruction-tuning dataset. Comparable to closed-source models such as ChatGPT, ToolLLaMA exhibits strong performance in carrying out intricate instructions and generalizing to unknown sources of data. 

OS-Copilot – By interacting with operating systems, OS-Copilot expands the capabilities of LLM and creates FRIDAY, an autonomous agent that performs a variety of jobs well. On GAIA benchmarks, FRIDAY performs better than previous approaches, demonstrating flexible use for tasks like PowerPoint and Excel with less supervision. The framework of OS-Copilot extends AI’s potential in general-purpose computing, indicating substantial progress in autonomous agent development and wider AI studies.

Smaller LLMs (Including Quantized LLMs)

Smaller LLMs, such as quantized versions, are appropriate for resource-constrained device deployment since they serve applications that demand less precision or fewer parameters. These models facilitate deployment in edge computing, mobile devices, and other scenarios requiring effective AI solutions by enabling broader accessibility and application of large-scale language processing capabilities in environments with limited computational resources.

Examples

BitNet  – BitNet is a 1-bit LLM that was first introduced in research as BitNet b1.58. With ternary weights {-1, 0, 1} for each parameter, this model greatly improves cost-efficiency while performing in a manner that is comparable to full-precision models in terms of perplexity and task performance. BitNet is superior in terms of energy consumption, throughput, latency, and memory utilization. It also proposes a new processing paradigm and creates a new scaling law for training high-performance, low-cost LLMs. 

Gemma 1B – Modern, lightweight open variants called Gemma 1B are based on the same technology as the Gemini series. These models perform exceptionally well in language interpretation, reasoning, and safety benchmarks with sizes of 2 billion and 7 billion parameters. Gemma performs better on 11 out of 18 text-based tasks than similarly sized open models. The release emphasizes safety and accountability in the use of AI by including both pretrained and refined checks. T

Lit-LLaMA – Building on nanoGPT, Lit-LLaMA seeks to offer a pristine, completely open, and safe implementation of the LLaMA source code. The project prioritizes community-driven development and simplicity. Therefore, there is no boilerplate code, and the implementation is simple. Effective use on consumer devices is made possible by Lit-LLaMA’s support for parameter-efficient fine-tuning approaches like LLaMA-Adapter and LoRA. Utilizing libraries such as PyTorch Lightning and Lightning Fabric, Lit-LLaMA concentrates on crucial facets of model implementation and training, upholding a single-file methodology to produce the finest LLaMA implementation accessible, completely open-source, and prepared for swift advancement and exploration.

Non-Transformer LLMs

Language models known as Non-Transformer LLMs depart from the conventional transformer architecture by frequently introducing components such as Recurrent Neural Networks (RNNs). Some of the main drawbacks and issues with transformers, like their expensive computing costs and ineffective handling of sequential data, are addressed by these approaches. Non-transformer LLMs provide unique approaches to improve model performance and efficiency by investigating alternative designs. This broadens the range of applications for advanced language processing jobs and increases the number of tools available for AI development.

Examples

Mamba – Because Mamba addresses the computational inefficiencies of the Transformer architecture, especially with extended sequences, it offers a substantial development in foundation models. In contrast to conventional models, Mamba is not constrained by subquadratic-time architectures, which have trouble with content-based reasoning. Examples of these designs are linear attention and recurrent models. Mamba enhances discrete modality processing by allowing of Structured State Space Model (SSM) parameters to function dependent on the input. This breakthrough and a hardware-aware parallel algorithm lead to a simplified neural network architecture that eschews MLP blocks and attention. Across multiple modalities, including language, music, and genomics, Mamba outperforms Transformers of comparable and even greater sizes with a throughput five times higher than Transformers and displaying linear scaling with sequence length.

RWKV – To address the memory and computational difficulties associated with sequence processing, RWKV creatively blends the advantages of Transformers and Recurrent Neural Networks (RNNs). Transformers are quite effective, but their sequence length scaling is quadratic, while RNNs scale linearly but are not parallelizable or scalable. The model can learn like a Transformer and infer like an RNN thanks to the introduction of a linear attention mechanism by RWKV. RWKV can retain constant computational and memory complexity throughout inference with its dual capability. RWKV shows performance comparable to Transformers when scaled up to 14 billion parameters, offering a possible route toward more effective sequence processing models that balance high performance and computational efficiency.

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