Best Large Language Models (LLMs) in 2024

In this article, we are getting an overview of LLM and some of the best Large Language Models that exist today.

In 2024, Artificial Intelligence (AI) is a hot topic, captivating millions of people worldwide. AI’s remarkable language capabilities, driven by advancements in Natural Language Processing (NLP) and Large Language Models (LLMs) like ChatGPT from OpenAI, have contributed to its popularity.

LLM, like ChatGPT, LaMDA, PaLM, etc., are advanced computer programs trained on vast textual data. They excel in tasks like text generation, speech-to-text, and sentiment analysis, making them valuable tools in NLP. The model’s parameters enhance its ability to predict word sequences, improving accuracy and handling complex relationships.

Introducing large language models in NLP

Natural Language Processing (NLP) has seen a surge in popularity due to computers’ capacity to handle vast amounts of natural text data. NLP has been applied in technologies like speech recognition and chatbots. Combining NLP with advanced Machine Learning techniques led to the emergence of powerful Large Language Models (LLMs).

Trained on massive datasets of text, reaching millions or billions of data points, these models demand significant computing power. To put it simply, if regular language models are like gardens, Large Language Models are like dense forests.

How do large language models do their work?

LLMs, powered by the transformative architecture of Transformers, work wonders with textual data. These Neural Networks are adept at tasks like language translation, text generation, and answering questions. Transformers can efficiently scale and handle vast text corpora, even in the billions or trillions.

Unlike sequential RNNs, they can be trained in parallel, utilizing multiple resources simultaneously for faster learning. A standout feature of Transformers is their self-attention mechanism, enabling them to understand language meaningfully, grasping grammar, semantics, and context from extensive text data.

The invention of Transformers revolutionized AI and NLP, leading to the creation of numerous LLMs utilized in various applications like chat support, voice assistants, chatbots, and more. In this article, we’ll explore five of the most advanced LLMs in the world as of 2023.

Best large language models (LLMs) in 2024

1. GPT-4

GPT-4 is the latest and most advanced large language model from OpenAI. It has over 1 trillion parameters, making it one of the largest language models ever created. GPT-4 is capable of a wide range of tasks, including text generation, translation, summarization, and question answering. It is also able to learn from and adapt to new information, making it a powerful tool for research and development.

Key features of GPT-4

What sets GPT-4 apart is its human-level performance on a wide array of tasks, making it a game-changer for businesses seeking automation solutions. With its unique multimodal capabilities, GPT-4 can process both text and images, making it perfect for tasks like image captioning and visual question answering. Boasting over 1 trillion parameters, GPT-4 possesses an unparalleled learning capacity, surpassing all other language models.

Moreover, it addresses the accuracy challenge by being trained on a massive dataset of text and code, reducing inaccuracies and providing more factual information. Finally, GPT-4’s impressive fluency and creativity in generating text make it a versatile tool for tasks ranging from writing news articles and generating marketing copy to crafting captivating poems and stories.

Applications of GPT-4

• Research: GPT-4 is a valuable tool for research in areas such as artificial intelligence, natural language processing, and machine learning.
• Development: GPT-4 can be used to generate code in a variety of programming languages, which makes it a valuable tool for developers.
• Business: GPT-4 can be used to automate tasks that are currently performed by humans, which can save businesses time and money.
• Education: GPT-4 can be used to help students learn about different subjects.
• Entertainment: GPT-4 can be used to generate creative text formats, such as poems, code, scripts, musical pieces, emails, letters, etc.

2. GPT-3.5

GPT-3.5 is a smaller version of GPT-4, with around 175 billion parameters. It is still a powerful language model, but it is not as large or as advanced as GPT-4. GPT-3.5 is still under development, but it has already been shown to be capable of a wide range of tasks, including text generation, translation, summarization, and question-answering.

Key features of GPT-3.5

GPT-3.5 is a fast and versatile language model, outpacing GPT-4 in speed and applicable to a wide range of tasks. It excels in creative endeavors, effortlessly generating poems, code, scripts, musical pieces, emails, letters, and more. Additionally, GPT-3.5 proves adept at addressing coding questions. However, it has encountered challenges with hallucinations and generating false information. Like many language models, GPT-3.5 may produce text that is factually inaccurate or misleading, an issue researchers are actively working to improve.

Applications of GPT-3.5

• Creative tasks: GPT-3.5 can be used to generate creative text formats, such as poems, code, scripts, musical pieces, emails, letters, etc.
• Coding questions: GPT-3.5 can be used to answer coding questions.
• Education: GPT-3.5 can be used to help students learn about different subjects.
• Business: GPT-3.5 can be used to automate tasks that are currently performed by humans, which can save businesses time and money.

3. PaLM 2

PaLM 2 (Bison-001) is a large language model from Google AI. It is focused on commonsense reasoning and advanced coding. PaLM 2 has been shown to outperform GPT-4 in reasoning evaluations, and it can also generate code in multiple languages.

Key features of PaLM 2

PaLM 2 is an exceptional language model equipped with commonsense reasoning capabilities, enabling it to draw inferences from extensive data and conduct valuable research in artificial intelligence, natural language processing, and machine learning. Moreover, it boasts advanced coding skills, proficiently generating code in various programming languages like Python, Java, and C++, making it an invaluable asset for developers seeking efficient and rapid code generation.

Another notable feature of PaLM 2 is its multilingual competence, as it can comprehend and generate text in more than 20 languages. Furthermore, PaLM 2 is quick and highly responsive, capable of swiftly and accurately addressing queries. This responsiveness renders it indispensable for businesses aiming to provide excellent customer support and promptly answer employee questions. PaLM 2’s combined attributes make it a powerful and versatile tool with a multitude of applications across various domains.

Applications of PaLM 2

• Research: PaLM 2 is a valuable tool for research in areas such as artificial intelligence, natural language processing, and machine learning.
• Development: PaLM 2 can be used to generate code in a variety of programming languages, which makes it a valuable tool for developers.
• Business: PaLM 2 can be used to automate tasks that are currently performed by humans, which can save businesses time and money.
• Customer support: PaLM 2 can be used to provide customer support or answer questions from employees.

4. Claude v1

Claude v1 is a large language model from Anthropic. It is backed by Google, and it is designed to be a powerful LLM for AI assistants. Claude v1 has a context window of 100k tokens, which makes it capable of understanding and responding to complex queries.

Key features of Claude v1

Furthermore, Claude v1 boasts a 100k token context window, surpassing other language models, allowing it to handle complex queries adeptly. It excels in benchmarks, ranking among the most powerful LLMs. Comparable to GPT-4 in performance, Claude v1 serves as a strong alternative for businesses seeking a potent LLM solution.

Applications of Claude v1

• AI assistants: Claude v1 is designed to be a powerful LLM for AI assistants. It can be used to answer questions, generate text, and complete tasks.
• Research: Claude v1 can be used for research in areas such as artificial intelligence, natural language processing, and machine learning.
• Business: Claude v1 can be used by businesses to automate tasks, generate text, and improve customer service.

Read more –> Introducing Claude 2: Dominating conversational AI with revolutionary redefinition

5. Cohere

Cohere is a company that provides accurate and robust models for enterprise generative AI. Its Cohere Command model stands out for accuracy, making it a great option for businesses.

Key features of Cohere

Moreover, Cohere offers accurate and robust models, trained on extensive text and code datasets. The Cohere Command model, tailored for enterprise generative AI, is accurate, robust, and user-friendly. For businesses seeking reliable generative AI models, Cohere proves to be an excellent choice.

Applications of Cohere

• Research: Cohere models can be used for research in areas such as artificial intelligence, natural language processing, and machine learning.
• Business: Cohere models can be used by businesses to automate tasks, generate text, and improve customer service.

6. Falcon

Falcon is the first open-source large language model on this list, and it has outranked all the open-source models released so far, including LLaMA, StableLM, MPT, and more. It has been developed by the Technology Innovation Institute (TII), UAE.

Key features of Falcon

• Apache 2.0 license: Falcon has been open-sourced with Apache 2.0 license, which means you can use the model for commercial purposes. There are no royalties or restrictions either.
• 40B and 7B parameter models: The TII has released two Falcon models, which are trained on 40B and 7B parameters.
• Fine-tuned for chatting: The Falcon-40B-Instruct model is fine-tuned for most use cases, including chat.
• Works in multiple languages: The Falcon model has been primarily trained in English, German, Spanish, and French, but it can also work in Italian, Portuguese, Polish, Dutch, Romanian, Czech, and Swedish languages.

7. Gemini

Gemini, a model developed by Google, is notable for its multimodal capabilities. This means Gemini can interpret and respond to various types of content, including text, video, audio, and code.

The architecture and training strategies of Gemini emphasize extensive contextual understanding, a feature that sets it apart from many other models. These capabilities make Gemini versatile, suitable for applications requiring a nuanced understanding of different data formats

8. LLaMA

LLaMA is a series of the best large language models developed by Meta. The models are trained on a massive dataset of text and code, and they can perform a variety of tasks, including text generation, translation, summarization, and question-answering.

Key features of LLaMA

• 13B, 26B, and 65B parameter models: Meta has released LLaMA models in various sizes, from 13B to 65B parameters.
• Outperforms GPT-3: Meta claims that its LLaMA-13B model outperforms the GPT-3 model from OpenAI which has been trained on 175 billion parameters.
• Released for research only: LLaMA has been released for research only and can’t be used commercially.

9. Guanaco-65B

Guanaco-65B is an open-source large language model that has been derived from LLaMA. It has been fine-tuned on the OASST1 dataset by Tim Dettmers and other researchers.

Key features of Guanaco-65B

• Outperforms ChatGPT: Guanaco-65B outperforms even ChatGPT (GPT-3.5 model) with a much smaller parameter size.
• Trained on a single GPU: The 65B model has trained on a single GPU having 48GB of VRAM in just 24 hours.
• Available for offline use: Guanaco models can be used offline, which makes them a good option for businesses that need to comply with data privacy regulations.

10. Vicuna 33B

Vicuna is another open-source large language model that has been derived from LLaMA. It has been fine-tuned using supervised instruction and the training data has been collected from sharegpt.com, a portal where users share their incredible ChatGPT conversations.

Key features of Vicuna 33B

• 33 billion parameters: Vicuna is a 33 billion parameter model, which makes it a powerful tool for a variety of tasks.
• Performs well on MT-Bench and MMLU tests: Vicuna has performed well on the MT-Bench and MMLU tests, which are benchmarks for evaluating the performance of large language models.
• Available for demo: You can try out Vicuna by interacting with the chatbot on the LMSYS website.

11. MPT-30B

MPT-30B is another open-source large language model that has been developed by Mosaic ML. It has been fine-tuned on a large corpus of data from different sources, including ShareGPT-Vicuna, Camel-AI, GPTeacher, Guanaco, Baize, and other sources.

Key features of MPT-30B

• 8K token context length: MPT-30B has a context length of 8K tokens, which makes it a good choice for tasks that require long-range dependencies.
• Outperforms GPT-3: MPT-30B outperforms the GPT-3 model by OpenAI on the MT-Bench test.
• Available for local use: MPT-30B can be used locally, that makes it a good option for businesses that need to comply with data privacy regulations.

12. Cohere

Cohere, on the other hand, focuses on providing enterprise LLM solutions that can be custom-trained and fine-tuned for specific company use cases.

Cohere’s models can be trained and tailored to suit a wide range of applications, from blogging and content writing to more complex tasks requiring deep contextual understanding. The company offers a range of models, including Cohere Generate, Embed, and Rerank, each designed for different aspects of language processing. Cohere stands out for its adaptability and ease of integration into various business processes, offering solutions that solve real-world problems with advanced AI capabilities

What are open-source large language models?

Open-source large language models refer to sophisticated AI systems like GPT-3.5, which have been developed to comprehend and produce human-like text by leveraging patterns and knowledge acquired from extensive training data.

Constructed using deep learning methods, these models undergo training on massive datasets comprising diverse textual sources, such as books, articles, websites, and various written materials.

Top open-source large language models