List of 100+ Notable Large Language Model (LLMs) 🤖

Large Language Models (LLMs) have revolutionized the field of natural language processing (NLP), enabling machines to understand, generate, and interact with human languages in unprecedented ways. Here's a list of 100+ notable LLMs along with brief descriptions. GPT-4o: OpenAI's new flagship model that can reason across audio, vision, and text in real time. MiniCPM-V: A GPT-4V Level Multimodal LLM on Your Phone. llama3-from-scratch: llama3 implementation one matrix multiplication at a time. ChatGPT Desktop Application: ChatGPT Desktop Application (Mac, Windows and Linux). llm.c: LLM training in simple, raw C/CUDA. GPT-3 (Generative Pre-trained Transformer 3): Developed by OpenAI, GPT-3 is one of the most advanced LLMs available. It can generate text that closely resembles human writing on a wide range of topics. GPT-3 has been used for various applications, including content creation, answering questions, and even coding assistance. BERT (Bidirectional Encoder Representations from Transformers): Introduced by Google AI, BERT is designed to understand the context of words in sentences by looking at the words that come before and after them. This bidirectional approach allows BERT to perform exceptionally well on tasks like sentiment analysis, question answering, and named entity recognition. T5 (Text-to-Text Transfer Transformer): Also developed by Google AI, T5 treats every NLP task as a text generation problem. It converts all inputs and outputs into a single format, making it versatile across different types of NLP tasks such as translation, summarization, and classification. RoBERTa (Robustly Optimized BERT Pretraining Approach): A variant of BERT, RoBERTa was introduced by Facebook AI. It modifies key hyperparameters in BERT, including removing BERT’s next-sentence pretraining objective, and training with much larger mini-batches and learning rates. These changes result in better performance on several benchmarks. XLNet: XLNet combines the best aspects of BERT and autoregressive models. It uses a permutation-based training strategy to predict tokens in a sentence, allowing it to capture both the bidirectional context of BERT and the autoregressive capabilities of models like GPT-2. This makes XLNet highly effective for understanding complex language structures. ALBERT (A Lite BERT): ALBERT is a lighter version of BERT, designed to reduce model size while maintaining similar performance levels. It introduces parameter-reduction techniques that lower memory consumption and increase training speed without significantly affecting accuracy. DistilBERT: DistilBERT is a smaller, faster, cheaper, and lighter version of BERT, retaining over 95% of its performances while being 60% smaller and 60% faster. It's particularly useful for deploying BERT-like models on devices with limited computational resources. ELECTRA: ELECTRA proposes a new pre-training task called replaced token detection, which is more sample-efficient than masked language modeling used in BERT. It trains a discriminator to distinguish “real” input tokens from “fake” ones generated by another neural network, leading to improved efficiency and performance. GPT-J: GPT-J is an open-source alternative to GPT-3, offering similar capabilities in text generation, completion, and understanding. It's based on the GPT architecture but is trained independently by EleutherAI, providing access to a large-scale transformer model outside of commercial platforms. LaBSE (Language-agnostic BERT Sentence Embeddings): LaBSE extends BERT to support multilingual and cross-lingual tasks by aligning embeddings in a shared space. It enables tasks like machine translation, cross-lingual information retrieval, and zero-shot learning across many languages. Megatron-LM: Developed by NVIDIA, Megatron-LM scales up the GPT architecture to train on massive datasets, achieving state-of-the-art performance on several benchmarks. It demonstrates the power of scaling in improving model capabilities. CTRL (Conditional Transformer Language Model): CTRL, by Salesforce Research, allows users to control the style, content, and task-specific behavior of the generated text through control codes. This makes it highly adaptable for various writing styles and formats. BigBird: BigBird, introduced by Google Research, addresses the limitation of self-attention mechanisms in handling long sequences by introducing sparse attention patterns. This allows it to process longer documents effectively. DeBERTa (Decoding-enhanced BERT with disentangled attention): DeBERTa improves upon BERT by enhancing the model's ability to focus on relevant parts of the input data through disentangled attention mechanisms, leading to better performance on NLP tasks. Longformer: Longformer, by Allen Institute for AI, is designed to handle very long documents by using a sliding window attention mechanism, making it suitable for tasks requiring understanding of lengthy texts. Reformer: The Reformer model, also by Google Research, introduces efficient attention mechanisms that allow it to scale to extremely large contexts, making it practical for real-time applications involving long sequences. RealFormer: RealFormer simplifies the architecture of transformers by removing certain components while maintaining or even improving performance, focusing on efficiency and ease of implementation. Linformer: Linformer approximates the self-attention mechanism in transformers to make it linear time complexity, allowing it to process longer sequences efficiently without sacrificing too much quality. Perceiver: Perceiver, by DeepMind, generalizes the transformer model to handle arbitrary modalities of data, not just text, by using a cross-attention mechanism between latent variables and inputs. Vision Transformer (ViT): ViT applies transformer models to image classification tasks, treating images as sequences of patches and applying self-attention mechanisms to achieve state-of-the-art performance in computer vision. DALL·E: DALL·E, by OpenAI, is a transformer-based model that generates images from textual descriptions, showcasing the versatility of LLMs beyond text processing. CLIP (Contrastive Language–Image Pretraining): CLIP, also by OpenAI, learns to associate images with textual descriptions through contrastive learning, enabling zero-shot transfer across a variety of visual recognition tasks. Wav2Vec 2.0: Wav2Vec 2.0, by Facebook AI, is a speech recognition model that leverages self-supervised learning from raw audio data, significantly outperforming traditional supervised methods. mBART (Multilingual Denoising Pre-training for Neural Machine Translation): mBART is a sequence-to-sequence model pretrained on multilingual denoising objectives, excelling in machine translation among other tasks. XLM-R (Cross-lingual Language Model Roberta): XLM-R extends RoBERTa to be cross-lingually aligned, enabling high-performance multilingual NLP tasks across 100+ languages. ERNIE (Enhanced Representation through kNowledge IntEgration): ERNIE, by Baidu, integrates knowledge graphs into pre-training, enhancing the model's understanding of entities and relations in text. NEZHA (Named Entity Enhanced Pre-training and Zero-shot Learning Framework): NEZHA focuses on named entity recognition and relation extraction, incorporating entity-level information into the pre-training process. UniLM (Unified Language Model): UniLM unifies three types of language model pre-training objectives—unidirectional, bidirectional, and sequence-to-sequence—into a single framework, improving performance across various NLP tasks. MASS (Masked Sequence to Sequence Pre-training for Language Generation): MASS, by Alibaba, introduces a novel pre-training method that masks an entire span of tokens and predicts them in an auto-regressive manner, enhancing language generation capabilities. PEGASUS (Pre-training with Extracted Gap-sentences for Abstractive SUmmarization Sequence-to-sequence): PEGASUS is specifically designed for abstractive text summarization, using a gap-sentence generation pre-training objective to improve summary quality. GPT-Neo: An independent replication of GPT-3 by EleutherAI, GPT-Neo offers similar capabilities in text generation and understanding, demonstrating the community's efforts to democratize access to powerful LLMs. GPT-NeoX: Building on GPT-Neo, NeoX aims to push the limits of model size and performance, exploring the frontiers of what's achievable with current hardware and training methodologies. FlauBERT: FlauBERT is a French language model based on BERT, tailored for understanding and generating text in French, showcasing the adaptability of LLM architectures to specific languages. CamemBERT: CamemBERT is another model focused on the French language, employing strategies similar to RoBERTa to enhance performance on a wide range of French NLP tasks. RuDALLE: RuDALLE is a Russian adaptation of the DALL·E model, capable of generating images from textual descriptions in Russian, illustrating the potential for creative applications of LLMs in different linguistic contexts. ByT5: ByT5 adapts the T5 model for byte-level operations, enabling it to handle a broader range of data types and encoding schemes, including those used in programming and web development. CodeBERT: CodeBERT is designed for programming language understanding and generation, leveraging the BERT architecture to assist in code completion, bug fixing, and documentation tasks. GraphCodeBERT: GraphCodeBERT extends CodeBERT by integrating graph neural networks, improving the model's ability to understand the structure of code and its dependencies. Salesforce CodeGen: Salesforce CodeGen focuses on generating code snippets and solutions for software development problems, utilizing a mix of LLM technologies to aid programmers. DeepCode: DeepCode learns from a vast corpus of public code repositories to provide suggestions and corrections for coding issues, leveraging AI to enhance developer productivity. PaLM (Pathways Language Model): PaLM, by Google Research, represents a significant advancement in scaling and integration, combining large-scale LLM capabilities with Google's Pathways architecture for more generalized intelligence. Chinchilla: Chinchilla explores the relationship between model size, dataset size, and compute budget, aiming to optimize these factors for maximum performance gains in LLMs. Gopher: Gopher is a large-scale model developed by DeepMind, showcasing advancements in training efficiency and performance across a broad spectrum of NLP benchmarks. LAMA (Language Model Analysis): LAMA evaluates the factual and commonsense reasoning capabilities of LLMs, providing insights into how well these models understand and apply knowledge. REALM (Retrieval-Augmented Language Model): REALM enhances language models by integrating document retrieval into the pre-training process, enabling models to access and utilize external knowledge bases. KILT (Knowledge Intensive Language Tasks): KILT is a benchmark suite for evaluating the performance of LLMs on tasks that require accessing and manipulating knowledge from external sources. BLURB (Benchmarking Language Understanding and Reasoning Benchmarks): BLURB provides a comprehensive evaluation framework for assessing the capabilities of LLMs across a wide range of language understanding and reasoning tasks. BIG-Bench: BIG-Bench is a collaborative effort to create a diverse set of benchmarks for evaluating the performance, robustness, and limitations of large-scale LLMs. GLUE (General Language Understanding Evaluation): GLUE offers a collection of resources for training, evaluating, and analyzing the performance of models on a variety of language understanding tasks. SuperGLUE: SuperGLUE is an upgraded version of GLUE, featuring more challenging tasks and stricter evaluation criteria to push the boundaries of what LLMs can achieve. EleutherAI's GPT-3.5 Turbo: An enhanced version of GPT-3, optimized for efficiency and cost-effectiveness, making it accessible for a wider range of applications and users. Fairseq: Fairseq is a general-purpose library for sequence-to-sequence learning, supporting a wide array of models including BART, T2T, and more, facilitating research and deployment. Hugging Face Transformers: While not a model itself, Hugging Face's Transformers library is pivotal in the LLM ecosystem, providing easy access to hundreds of pre-trained models and tools for fine-tuning and deployment. ULMFiT (Universal Language Model Fine-Tuning): ULMFiT introduces a methodology for fine-tuning language models on specific tasks, significantly improving performance with minimal additional training data. ELMo (Embeddings from Language Models): ELMo was one of the first models to demonstrate the effectiveness of deep contextualized word representations, paving the way for later advancements like BERT. OpenWebText: OpenWebText is a dataset derived from the Common Crawl project, serving as a foundation for training large-scale language models in an open-source manner. Common Crawl: Common Crawl provides a vast repository of web crawl data, which is crucial for training models on real-world internet text. GPT-2: Preceding GPT-3, GPT-2 showcased the potential of transformer-based models for generating coherent and contextually relevant text. Transformer-XL: Transformer-XL addresses the issue of context length in transformers, enabling models to handle longer sequences and dependencies. Sparse Transformer: Sparse Transformer introduces sparsity into the self-attention mechanism, allowing for more efficient processing of long sequences. Switch Transformer: Switch Transformer, or SwiT, scales up models by using a mixture of experts, where different subsets of parameters are activated for different inputs. EfficientNet: Although primarily known for image classification, EfficientNet principles have inspired efficient architectures in LLMs, emphasizing scalability and performance. MobileBERT: MobileBERT is a compact version of BERT optimized for mobile and edge devices, balancing performance with resource constraints. TinyBERT: TinyBERT further reduces the size of BERT, making it suitable for deployment on devices with very limited computational resources. DistilGPT-2: DistilGPT-2 is a distilled version of GPT-2, offering similar capabilities with reduced model size and computational requirements. Quantum Language Processing (QLP): QLP explores the intersection of quantum computing and NLP, investigating how quantum algorithms might enhance language processing tasks. BioBERT: BioBERT is a domain-specific model trained on biomedical literature, aiding in tasks related to medical research and healthcare. SciBERT: SciBERT is tailored for scientific text, improving performance on tasks involving academic papers and technical documentation. LegalBERT: LegalBERT focuses on legal documents and statutes, assisting in legal research and analysis tasks. FinBERT: FinBERT is designed for financial text, helping with tasks such as sentiment analysis of financial news and market prediction. GeoBERT: GeoBERT incorporates geographical knowledge into its training, enhancing understanding and generation of location-related text. SentiBERT: SentiBERT specializes in sentiment analysis, providing nuanced understanding of emotions and opinions expressed in text. VideoBERT: VideoBERT extends BERT to video understanding, processing both visual and textual information to comprehend video content. AudioBERT: AudioBERT adapts BERT for audio signals, enabling tasks such as speech recognition and audio classification. VisualBERT: VisualBERT combines visual and textual inputs, improving performance on tasks that require understanding of both modalities, such as visual question answering. GPT-4: As a successor to GPT-3, GPT-4 would hypothetically bring further improvements in language understanding, generation, and efficiency, although details remain speculative as of my last update. DialoGPT: DialoGPT is a variant of GPT-2 fine-tuned for conversational responses, making it suitable for developing chatbots and interactive agents. Meena: Meena is a chatbot developed by Google, boasting 2.6 billion parameters and designed to engage in free-flowing conversations on a wide range of topics. BlenderBot: BlenderBot, by Facebook AI, is a series of models aimed at improving conversational AI, with versions that incorporate various sizes and capabilities for dialogue management. DrQA: DrQA is a system for reading comprehension applied to open-domain question answering, leveraging a document retriever and a document reader built on deep learning models. BERT for Coreference Resolution: This adaptation of BERT is specifically tuned for coreference resolution tasks, identifying when two or more expressions in a text refer to the same entity. SpanBERT: SpanBERT extends BERT by masking contiguous spans of tokens rather than random tokens, improving performance on span selection tasks. KnowBERT: KnowBERT integrates knowledge graphs directly into the BERT architecture, enhancing the model's ability to reason about entities and their relationships. MT-DNN (Multi-Task Deep Neural Network): MT-DNN is a framework for multi-task learning across various NLP tasks, sharing representations to improve generalization and performance. Grover: Grover is a model for generating fake news articles, highlighting the importance of understanding and mitigating the risks associated with advanced LLMs. CTRL-S: CTRL-S is a controlled text generation model that allows for specifying attributes like sentiment, formality, and length, giving users more control over the output. Turing-NLG: Turing-NLG is Microsoft's large-scale transformer model, designed for a wide range of NLP tasks and showcasing advancements in model scalability and performance. ZEN (Zero-shot Entity Network): ZEN is designed for zero-shot learning tasks, enabling models to recognize and understand entities they haven't seen during training. VQ-VAE (Vector Quantised-Variational AutoEncoder): VQ-VAE is a generative model that compresses data into a discrete latent space, useful for tasks like image and text synthesis. WaveNet: WaveNet, developed by DeepMind, is a deep generative model for producing realistic speech, music, and other audio signals. Tacotron: Tacotron is a text-to-speech synthesis model that converts text into human-like speech, leveraging the power of deep learning. BERT for Question Answering: This specialization of BERT is fine-tuned for question answering tasks, improving the model's ability to extract answers from passages of text. BERT for Named Entity Recognition (NER): Adapted for NER tasks, this version of BERT identifies and classifies named entities in text, such as persons, organizations, and locations. BERT for Sentiment Analysis: Tailored for sentiment analysis, this BERT model determines the emotional tone behind words, phrases, or entire documents. BERT for Text Classification: This adaptation of BERT is optimized for text classification tasks, categorizing text into predefined classes. BERT for Paraphrase Identification: Designed to identify paraphrases, this BERT model assesses whether two pieces of text convey the same meaning. BERT for Relation Extraction: This model extracts semantic relationships between entities in text, identifying connections such as "is a part of" or "works for." BERT for Summarization: Adapted for text summarization, this BERT model generates concise summaries of longer documents or articles. BERT for Translation: Specialized for machine translation, this BERT model translates text from one language to another, leveraging the model's understanding of both source and target languages. BERT for Emotion Detection: This BERT model detects emotions in text, recognizing feelings expressed in written content, from joy and surprise to sadness and anger. GPT-F: Hypothetical future iterations of GPT could introduce GPT-F, focusing on fairness and ethical considerations in AI-generated content, ensuring unbiased and responsible language generation. EcoBERT: EcoBERT could be envisioned as a model specifically designed for environmental science literature, aiding researchers in understanding and summarizing complex ecological studies and reports. JurisBERT: JurisBERT would specialize in legal documents and case law, potentially streamlining legal research and document review processes in the legal profession. MedGPT: MedGPT could be a model dedicated to medical education and practice, generating explanations and summaries of medical procedures, diagnoses, and treatments for educational purposes. ArtiGPT: ArtiGPT might explore the intersection of art history and AI, generating descriptions, analyses, and even synthetic artworks based on historical art movements and styles. PsychoGPT: PsychoGPT could be tailored for psychological research and therapy, generating therapeutic dialogues or analyzing patient narratives to assist mental health professionals. AstroGPT: AstroGPT would focus on astronomy and astrophysics, aiding in the interpretation of celestial phenomena, planetary data, and cosmic events for researchers and enthusiasts alike. AgriGPT: AgriGPT could be designed for agricultural applications, offering insights into crop management, pest control, and sustainable farming practices through analysis of agricultural data and literature. EthnoGPT: EthnoGPT might specialize in ethnography and cultural studies, generating detailed accounts of cultural practices, social norms, and historical contexts to enrich anthropological research. PhilosoGPT: PhilosoGPT could delve into philosophical texts and debates, generating arguments, critiques, and explorations of philosophical ideas, aiding students and scholars in navigating complex theoretical landscapes. These conceptual models illustrate the potential for LLMs to be tailored to specific domains and disciplines, enhancing accessibility and utility across a wide range of professional and academic fields. Each model represents a unique opportunity to leverage the power of artificial intelligence for specialized tasks, contributing to advancements in knowledge and understanding in various sectors.