Retirado de www.techtarget.com/whatis/feature/12-of-the-best-large-language-models
BERT (Bidirectional Encoder Representations from Transformers) is a family of LLMs that Google introduced in 2018. BERT is a transformer-based model that can convert sequences of data to other sequences of data. BERT's architecture is a stack of transformer encoders and features 342 million parameters. BERT was pre-trained on a large corpus of data then fine-tuned to perform specific tasks along with natural language inference and sentence text similarity. It was used to improve query understanding in the 2019 iteration of Google search.
The Claude LLM focuses on constitutional AI, which shapes AI outputs guided by a set of principles that help the AI assistant it powers helpful, harmless and accurate. Claude was created by the company Anthropic. The latest iteration of the Claude LLM is Claude 3.5 Sonnet. It understands nuance, humor and complex instructions better than earlier versions of the LLM, and operates at twice the speed of Claude 3 Opus. It’s available for free via Claude.ai and the Claude iOS app.
Cohere is an enterprise AI platform that provides several LLMs including Command, Rerank and Embed. These LLMs can be custom-trained and fine-tuned to a specific company’s use case. The company that created the Cohere LLM was founded by one of the authors of Attention Is All You Need. One of Cohere’s strengths is that it is not tied to one single cloud -- unlike OpenAI, which is bound to Microsoft Azure.
Ernie is Baidu’s large language model which powers the Ernie 4.0 chatbot. The bot was released in August 2023 and has garnered more than 45 million users. Ernie is rumored to have 10 trillion parameters. The bot works best in Mandarin but is capable in other languages.
Falcon 40B is a transformer-based, causal decoder-only model developed by the Technology Innovation Institute. It is open source and was trained on English data. The model is available in two smaller variants as well: Falcon 1B and Falcon 7B (1 billion and 7 billion parameters). Amazon has made Falcon 40B available on Amazon SageMaker. It is also available for free on GitHub.
Gemini is Google's family of LLMs that power the company's chatbot of the same name. The model replaced Palm in powering the chatbot, which was rebranded from Bard to Gemini upon the model switch. Gemini models are multimodal, meaning they can handle images, audio and video as well as text. Gemini is also integrated in many Google applications and products. It comes in three sizes -- Ultra, Pro and Nano. Ultra is the largest and most capable model, Pro is the mid-tier model and Nano is the smallest model, designed for efficiency with on-device tasks. Gemini outperforms GPT-4 on most evaluated benchmarks.
Gemma is a family of open-source language models from Google that were trained on the same resources as Gemini. Gemma comes in two sizes - a 2 billion parameter model and a 7 billion parameter model. Gemma models can be run locally on a personal computer, and surpass similarly sized Llama 2 models on several evaluated benchmarks.
GPT-3 is OpenAI's large language model with more than 175 billion parameters, released in 2020. GPT-3 uses a decoder-only transformer architecture. In September 2022, Microsoft announced it had exclusive use of GPT-3's underlying model. GPT-3 is 10 times larger than its predecessor. GPT-3's training data includes Common Crawl, WebText2, Books1, Books2 and Wikipedia.
GPT-3 is the last of the GPT series of models in which OpenAI made the parameter counts publicly available. The GPT series was first introduced in 2018 with OpenAI's paper "Improving Language Understanding by Generative Pre-Training."
GPT-3.5 is an upgraded version of GPT-3 with fewer parameters. GPT-3.5 was fine-tuned using reinforcement learning from human feedback. GPT-3.5 is the version of GPT that powers ChatGPT. There are several models, with GPT-3.5 turbo being the most capable, according to OpenAI. GPT-3.5's training data extends to September 2021.
It was also integrated into the Bing search engine but has since been replaced with GPT-4.
GPT-4 is the largest model in OpenAI's GPT series, released in 2023. Like the others, it's a transformer-based model. Unlike the others, its parameter count has not been released to the public, though there are rumors that the model has more than 170 trillion. OpenAI describes GPT-4 as a multimodal model, meaning it can process and generate both language and images as opposed to being limited to only language. GPT-4 also introduced a system message, which lets users specify tone of voice and task.
GPT-4 demonstrated human-level performance in multiple academic exams. At the model's release, some speculated that GPT-4 came close to artificial general intelligence (AGI), which means it is as smart or smarter than a human. GPT-4 powers Microsoft Bing search, is available in ChatGPT Plus and will eventually be integrated into Microsoft Office products.
GPT-4 Omni (GPT-4o) is OpenAI’s successor to GPT-4 and offers several improvements over the previous model. GPT-4o creates a more natural human interaction for ChatGPT and is a large multimodal model, accepting various inputs including audio, image and text. The conversations let users engage as they would in a normal human conversation, and the real-time interactivity can also pick up on emotions. GPT-4o can see photos or screens and ask questions about them during interaction.
GPT-4o can respond in 232 milliseconds, similar to human response time and faster than GPT-4 Turbo. The GPT-4o model is free and will be available for developer and customer products.
Lamda (Language Model for Dialogue Applications) is a family of LLMs developed by Google Brain announced in 2021 (now G. DeepMind). Lamda used a decoder-only transformer language model and was pre-trained on a large corpus of text. In 2022, LaMDA gained widespread attention when then-Google engineer Blake Lemoine went public with claims that the program was sentient. It was built on the Seq2Seq architecture.
Large Language Model Meta AI (Llama) is Meta's LLM released in 2023. The largest version is 65 billion parameters in size. Llama was originally released to approved researchers and developers but is now open source. Llama comes in smaller sizes that require less computing power to use, test and experiment with.
Llama uses a transformer architecture and was trained on a variety of public data sources, including webpages from CommonCrawl, GitHub, Wikipedia and Project Gutenberg. Llama was effectively leaked and spawned many descendants, including Vicuna and Orca.
Mistral is a 7 billion parameter language model that outperforms Llama's language model of a similar size on all evaluated benchmarks. Mistral also has a fine-tuned model that is specialized to follow instructions. Its smaller size enables self-hosting and competent performance for business purposes. It was released under the Apache 2.0 license.
Orca was developed by Microsoft and has 13 billion parameters, meaning it's small enough to run on a laptop. It aims to improve on advancements made by other open source models by imitating the reasoning procedures achieved by LLMs. Orca achieves the same performance as GPT-4 with significantly fewer parameters and is on par with GPT-3.5 for many tasks. Orca is built on top of the 13 billion parameter version of LLaMA.
The Pathways Language Model is a 540 billion parameter transformer-based model from Google powering its AI chatbot Bard. It was trained across multiple TPU 4 Pods -- Google's custom hardware for machine learning. Palm specializes in reasoning tasks such as coding, math, classification and question answering. Palm also excels at decomposing complex tasks into simpler subtasks.
PaLM gets its name from a Google research initiative to build Pathways, ultimately creating a single model that serves as a foundation for multiple use cases. There are several fine-tuned versions of Palm, including Med-Palm 2 for life sciences and medical information as well as Sec-Palm for cybersecurity deployments to speed up threat analysis.
Phi-1 is a transformer-based language model from Microsoft. At just 1.3 billion parameters, Phi-1 was trained for four days on a collection of textbook-quality data. Phi-1 is an example of a trend toward smaller models trained on better quality data and synthetic data.
"We'll probably see a lot more creative scaling down work: prioritizing data quality and diversity over quantity, a lot more synthetic data generation, and small but highly capable expert models," wrote Andrej Karpathy, former director of AI at Tesla and OpenAI employee, in a tweet. Phi-1 specializes in Python coding and has fewer general capabilities because of its smaller size.
StableLM is a series of open source language models developed by Stability AI, the company behind image generator Stable Diffusion. There are 3 billion and 7 billion parameter models available and 15 billion, 30 billion, 65 billion and 175 billion parameter models in progress at time of writing. StableLM aims to be transparent, accessible and supportive.
Vicuna is another influential open source LLM derived from Llama. It was developed by LMSYS and was fine-tuned using data from sharegpt.com. It is smaller and less capable that GPT-4 according to several benchmarks, but does well for a model of its size. Vicuna has only 33 billion parameters, whereas GPT-4 has trillions.