Large language models (LLMs) are advanced artificial intelligence models that process, analyze, and create natural language. LLMs are fundamentally distinct from typical natural language processing (NLP) techniques, which frequently rely on manually created rules to analyze and interpret text.
LLMs, on the other hand, are meant to learn and recognize patterns in language by analyzing huge volumes of text data. They use neural networks to understand how words are used together and to construct an internal representation of language that may be used for a variety of language-related tasks.
You can use LLM in numerous fields, here are some examples of common use cases:
While comparing LLM APIs, it is crucial to consider different aspects, among others, cost security and privacy. LLM experts at Eden AI tested, compared, and used many LLM APIs of the market. Here are some actors that perform well (in alphabetical order):
Jurassic-2 (or J2) is the next generation of foundation models, featuring major quality improvements and additional capabilities like as zero-shot instruction-following, reduced latency, and multi-language compatibility. J2 provides a more advanced baseline model, making it one of the market's most advanced large language models. J2 supports a number of languages other than English, including Spanish, French, German, Portuguese, Italian, and Dutch. J2's models can perform up to 30% faster than earlier models in terms of latency.
Amazon Web Services (AWS) offers a comprehensive suite of large language model APIs, providing businesses with access to cutting-edge natural language processing capabilities. Leveraging AWS's extensive infrastructure and advanced machine learning technologies, these APIs enable organizations to develop and deploy large language models for a wide range of applications, including text generation, language translation, sentiment analysis, and more.
AWS's large language model APIs stand out for their scalability, reliability, and seamless integration with other AWS services, empowering businesses to harness the power of language models for enhanced productivity, improved customer experiences, and innovative AI-driven solutions.
Claude 2 is a next-generation AI assistant based on Anthropic's research into developing AI systems that are helpful, honest, and harmless. In terms of performance, Claude 2 is a viable alternative to ChatGPT. Claude 2 scored 76.5 percent on the multiple choice section of the Bar exam and in the 90th percentile on the reading and writing portion of the GRE. Its coding skills have improved from its predecessor scoring 71.2 % on a Python coding test compared to Claude's 56 %.
Clarifai's large language model APIs offer businesses advanced natural language processing capabilities, enabling the development of sophisticated language models for various applications. Leveraging Clarifai's AI platform, these APIs empower organizations to build and deploy large language models that excel in tasks such as text generation, language understanding, sentiment analysis, and more. Clarifai's large language model APIs differentiate themselves through their user-friendly interface, robust model training capabilities, and support for diverse use cases, providing businesses with the tools to leverage language models for enhanced productivity, data insights, and innovative AI-driven solutions.
Cohere is another player in the realm of huge language models. This innovative solution enables developers and organizations to create great products using world-class natural language processing (NLP) technology while keeping their data private and secure.
Cohere enables companies of all sizes to explore, develop, and search for information in novel ways. Because the models have been pre-trained on billions of words, the API is simple to use and to configure. This implies that even small enterprises can now benefit from this cutting-edge technology without breaking the budget.
Falcon-40B is a fundamental LLM with 40B parameters that trains on one trillion tokens. It is an autoregressive decoder-only model. An autoregressive decoder-only model is trained to predict the next token in a sequence given the preceding tokens. Its architecture has been proved to outperform GPT-3. Falcon 40B, like other LLMs, can develop creative material, solve complex problems, customer service operations, virtual assistants, language translation, and much more!
At present, there is no API available for Falcon. However, you’ll soon be able to access it via Replicate on Eden AI.
Bard is a Google AI chatbot that generates human-like text and visuals using the Large Language Model (LLM) and LaMDA (Language Model for Dialogue Applications). Bard, unlike Google Search, is conversational, which means that users can type a question and receive a personalized response in normal language.
Bard exemplifies how LLMs can be utilized to develop great conversational AI experiences. The system may generate text and graphics that are adapted to a single user's input in a natural and engaging manner.
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PaLM, which stands for Pathways Language Model, is one of Google's in-house Large Language Models. It excels in numerous tasks, including generating codes, understanding other languages, reasoning skills, and much more. PaLM powers Bard and combines it with Google Services such as Gmail, Google Docs, and Google Sheets, allowing Bard to deliver data directly to these services.
Llama, which stands for Language Learning and Multimodal Analytics, is an innovative concept that deserves to be mentioned in the discussion of LLMs. The Meta AI team created Llama particularly to handle the difficulty of language modeling with limited computational capacity.
Pretrained Llama 2 models are trained using 2 trillion tokens. More than a million annotations by people were used to train its fine-tuned models. On numerous external metrics, including coding, knowledge, competency, and reasoning assessments, Llama 2 exceeds competing open source language models. Compared to Llama 1, it has trained with 40% more data and twice as much context.
Up to this date, there is no API available for Llama2. However, note that you’ll be able to access it via Replicate on Eden AI.
Mistral provides a powerful suite of large language model APIs, equipping businesses with advanced natural language processing capabilities for developing and deploying sophisticated language models. Leveraging state-of-the-art AI technologies, Mistral's APIs enable organizations to build large language models that excel in tasks such as text generation, language understanding, sentiment analysis, and more.
Mistral's large language model APIs stand out for their focus on ethical and responsible AI use, seamless integration with existing workflows, and practical insights for leveraging language models to enhance productivity, improve customer experiences, and drive innovation in AI-driven solutions.
Chatbots are one of the most fascinating applications of LLMs, and ChatGPT is a perfect example for it. ChatGPT is powered by GPT-4 language model, which can hold natural language discussions with users.
ChatGPT's uniqueness relies on the fact that it has been taught on a variety of topics, allowing it to assist with multiple tasks, answer questions, and hold fascinating conversations on a wide range of themes. Using the ChatGPT API, you can easily produce Python code, draft an email, and even adapt to different conversational styles and settings.
LLM API performance can vary depending on a number of variables, including the technology used by the provider, the underlying algorithms, the amount of the dataset, the server architecture, and network latency. Listed below are a few typical performance discrepancies between several LLM APIs:
Companies and developers from a wide range of industries (Social Media, Retail, Health, Finances, Law, etc.) use Eden AI’s unique API to easily integrate LLM tasks in their cloud-based applications, without having to build their own solutions.
Eden AI offers multiple AI APIs on its platform among several technologies: Text-to-Speech, Language Detection, Sentiment Analysis, Face Recognition, Question Answering, Data Anonymization, Speech Recognition, and so forth.
We want our users to have access to multiple LLM engines and manage them in one place so they can reach high performance, optimize cost and cover all their needs. There are many reasons for using multiple APIs :
Eden AI has been made for multiple AI APIs use. Eden AI is the future of AI usage in companies. Eden AI allows you to call multiple AI APIs.
You can see Eden AI documentation here.
The Eden AI team can help you with your LLM integration project. This can be done by :
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