Best Large Language Models in the Middle East

ChatGPT is a language model developed by OpenAI. It has been trained on a diverse range of internet text, allowing it to generate human-like responses to a variety of prompts. ChatGPT can be used for various natural language processing tasks, such as question answering, conversation, and text generation. ChatGPT is a pre-trained language model that uses deep learning algorithms to generate text. It was trained on a large corpus of text data, allowing it to generate human-like responses to a wide range of prompts. The model has a transformer architecture, which has been shown to be effective in many NLP tasks. In addition to generating text, ChatGPT can also be fine-tuned for specific NLP tasks such as question answering, text classification, and language translation. This allows developers to build powerful NLP applications that can perform specific tasks more accurately. ChatGPT can also process and generate code.

OpenAI’s mission is to ensure that artificial general intelligence (AGI)—by which we mean highly autonomous systems that outperform humans at most economically valuable work—benefits all of humanity. We will attempt to directly build safe and beneficial AGI, but will also consider our mission fulfilled if our work aids others to achieve this outcome. Apply our API to any language task — semantic search, summarization, sentiment analysis, content generation, translation, and more — with only a few examples or by specifying your task in English. One simple integration gives you access to our constantly-improving AI technology. Explore how you integrate with the API with these sample completions.

Gemini was created from the ground up to be multimodal, highly efficient at tool and API integrations and built to enable future innovations, like memory and planning. While still early, we’re already seeing impressive multimodal capabilities not seen in prior models. Gemini is also our most flexible model yet — able to efficiently run on everything from data centers to mobile devices. Its state-of-the-art capabilities will significantly enhance the way developers and enterprise customers build and scale with AI. We’ve optimized Gemini 1.0, our first version, for three different sizes: Gemini Ultra — our largest and most capable model for highly complex tasks. Gemini Pro — our best model for scaling across a wide range of tasks. Gemini Nano — our most efficient model for on-device tasks.

Our GPT-3 models can understand and generate natural language. We offer four main models with different levels of power suitable for different tasks. Davinci is the most capable model, and Ada is the fastest. The main GPT-3 models are meant to be used with the text completion endpoint. We also offer models that are specifically meant to be used with other endpoints. Davinci is the most capable model family and can perform any task the other models can perform and often with less instruction. For applications requiring a lot of understanding of the content, like summarization for a specific audience and creative content generation, Davinci is going to produce the best results. These increased capabilities require more compute resources, so Davinci costs more per API call and is not as fast as the other models.

GPT-4 is a large multimodal model (accepting text or image inputs and outputting text) that can solve difficult problems with greater accuracy than any of our previous models, thanks to its broader general knowledge and advanced reasoning capabilities. GPT-4 is available in the OpenAI API to paying customers. Like gpt-3.5-turbo, GPT-4 is optimized for chat but works well for traditional completions tasks using the Chat Completions API. GPT-4 is the latest GPT-4 model with improved instruction following, JSON mode, reproducible outputs, parallel function calling, and more. Returns a maximum of 4,096 output tokens. This preview model is not yet suited for production traffic.

Build natural language understanding and generation into your product with a few lines of code. The Cohere API provides access to models that read billions of web pages and learn to understand the meaning, sentiment, and intent of the words we use. Use the Cohere API to write human-like text by completing a prompt or filling in blanks. You can write copy, generate code, summarize text, and more. Compute the likelihood of text and retrieve representations from the model. Use the likelihood API to filter text based on chosen categories or selected criteria. With representations, you can train your own downstream models on a wide variety of domain-specific natural language tasks. The Cohere API can compute the similarity between pieces of text, and make categorical predictions by comparing the likelihood of different text options. The model has multiple lenses through which to view ideas, so that it can recognize abstract similarities between concepts as distinct as DNA and computers.

GPT-4 (Generative Pre-trained Transformer 4) is a large-scale unsupervised language model, yet to be released by OpenAI. GPT-4 is the successor to GPT-3 and part of the GPT-n series of natural language processing models, and was trained on a dataset of 45TB of text to produce human-like text generation and understanding capabilities. Unlike most other NLP models, GPT-4 does not require additional training data for specific tasks. Instead, it can generate text or answer questions using only its own internally generated context as input. GPT-4 has been shown to be able to perform a wide variety of tasks without any task specific training data such as translation, summarization, question answering, sentiment analysis and more.

Claude is an artificial intelligence large language model that can process and generate human-like text. Anthropic is an AI safety and research company that’s working to build reliable, interpretable, and steerable AI systems. Large, general systems of today can have significant benefits, but can also be unpredictable, unreliable, and opaque: our goal is to make progress on these issues. For now, we’re primarily focused on research towards these goals; down the road, we foresee many opportunities for our work to create value commercially and for public benefit.

GPT-3.5 is the next evolution of GPT 3 large language model from OpenAI. GPT-3.5 models can understand and generate natural language. We offer four main models with different levels of power suitable for different tasks. The main GPT-3.5 models are meant to be used with the text completion endpoint. We also offer models that are specifically meant to be used with other endpoints. Davinci is the most capable model family and can perform any task the other models can perform and often with less instruction. For applications requiring a lot of understanding of the content, like summarization for a specific audience and creative content generation, Davinci is going to produce the best results. These increased capabilities require more compute resources, so Davinci costs more per API call and is not as fast as the other models.

BLACKBOX.AI is a Coding LLM designed to transform the way we build software. By building BLACKBOX.AI, our goal is to: - Accelerate the pace of innovation within companies by making engineers 10X faster in building and releasing products - Accelerate the growth in software engineers around the world and 10X the number of engineers from ~100M to 1B Capabilities: 1. Natural Language to Code 2. Real-Time Knowledge 3. Code Completion 4. VISION 5. Code Commenting 6. Commit Message Generation 7. Chat with your Code Files BLACKBOX is built to answer coding questions and assist you write code faster. Whether you are fixing a bug, building a new feature or refactoring your code, ask BLACKBOX to help. BLACKBOX has real-time knowledge of the world, making it able to answer questions about recent events, technological breakthroughs, product releases, API documentations & more BLACKBOX integrates directly with VSCode to automatically suggests the next lines of code.

We’ve trained a model called ChatGPT which interacts in a conversational way. The dialogue format makes it possible for ChatGPT to answer followup questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests. ChatGPT is a sibling model to InstructGPT, which is trained to follow an instruction in a prompt and provide a detailed response. ChatGPT Plus is a subscription plan for ChatGPT a conversational AI. ChatGPT Plus costs $20/month, and subscribers will receive a number of benefits: - General access to ChatGPT, even during peak times - Faster response times - GPT-4 access - ChatGPT plugins - Web-browsing with ChatGPT - Priority access to new features and improvements ChatGPT Plus is available to customers in the United States, and we will begin the process of inviting people from our waitlist over the coming weeks. We plan to expand access and support to additional countries and regions soon.

BERT is a large language model and a method of pre-training language representations. Pre-training refers to how BERT is first trained on a large source of text, such as Wikipedia. You can then apply the training results to other Natural Language Processing (NLP) tasks, such as question answering and sentiment analysis. With BERT and AI Platform Training, you can train a variety of NLP models in about 30 minutes.

Switching is as easy as changing one line of code. Feature parity with industry standard APIs means your product works the same but faster. GooseAI is a fully managed NLP-as-a-Service, delivered via API. It is comparable to OpenAI in this regard. And even more, it is fully compatible with OpenAI's completion API! Our state-of-the-art selection of GPT-based language models and uncompromising speed will give you a jumpstart when starting your next project or offer a flexible alternative to your current provider. We're proud to be able to offer costs that are up to 70% cheaper than other providers, at the same or even better performance. Like the Mitochondria is the powerhouse of the cell, geese are an integral part of the ecosystem. Their beauty and elegance inspired us to fly high - like geese.

Stable LM: Stability AI Language Models. The release of Stable LM builds on our experience in open-sourcing earlier language models with EleutherAI, a nonprofit research hub. These language models include GPT-J, GPT-NeoX, and the Pythia suite, which were trained on The Pile open-source dataset. Many recent open-source language models continue to build on these efforts, including Cerebras-GPT and Dolly-2. Stable LM is trained on a new experimental dataset built on The Pile, but three times larger with 1.5 trillion tokens of content. We will release details on the dataset in due course. The richness of this dataset gives Stable LM surprisingly high performance in conversational and coding tasks, despite its small size of 3 to 7 billion parameters (by comparison, GPT-3 has 175 billion parameters). Stable LM 3B is a compact language model designed to operate on portable digital devices like handhelds and laptops, and we’re excited about its capabilities and portability.

GPT4All is an ecosystem to train and deploy powerful and customized large language models that run locally on consumer-grade CPUs. The goal is simple - be the best instruction-tuned assistant-style language model that any person or enterprise can freely use, distribute and build on. A GPT4All model is a 3GB - 8GB file that you can download and plug into the GPT4All open-source ecosystem software. Nomic AI supports and maintains this software ecosystem to enforce quality and security alongside spearheading the effort to allow any person or enterprise to easily train and deploy their own on-edge large language models. Data is one the most important ingredients to successfully building a powerful, general-purpose large language model. The GPT4All community has built the GPT4All open source data lake as a staging ground for contributing instruction and assistant tuning data for future GPT4All model trains.

Qwen-7B is the 7B-parameter version of the large language model series, Qwen (abbr. Tongyi Qianwen), proposed by Alibaba Cloud. Qwen-7B is a Transformer-based large language model, which is pretrained on a large volume of data, including web texts, books, codes, etc. Additionally, based on the pretrained Qwen-7B, we release Qwen-7B-Chat, a large-model-based AI assistant, which is trained with alignment techniques. The features of the Qwen-7B series include: Trained with high-quality pretraining data. We have pretrained Qwen-7B on a self-constructed large-scale high-quality dataset of over 2.2 trillion tokens. The dataset includes plain texts and codes, and it covers a wide range of domains, including general domain data and professional domain data. Strong performance. In comparison with the models of the similar model size, we outperform the competitors on a series of benchmark datasets, which evaluates natural language understanding, mathematics, coding, etc. And more.

ChatGLM-6B is an open-source, Chinese-English bilingual dialogue language model based on the General Language Model (GLM) architecture with 6.2 billion parameters. Combined with model quantization technology, users can deploy locally on consumer-grade graphics cards (only 6GB of video memory is required at the INT4 quantization level). ChatGLM-6B uses technology similar to ChatGPT, optimized for Chinese Q&A and dialogue. After about 1T identifiers of Chinese and English bilingual training, supplemented by supervision and fine-tuning, feedback self-help, human feedback reinforcement learning and other technologies, ChatGLM-6B with 6.2 billion parameters has been able to generate answers that are quite in line with human preferences.

PygmalionAI is a community dedicated to creating open-source projects based on EleutherAI's GPT-J 6B and Meta's LLaMA models. In simple terms, Pygmalion makes AI fine-tuned for chatting and roleplaying purposes. The current actively supported Pygmalion AI model is the 7B variant, based on Meta AI's LLaMA model. With only 18GB (or less) VRAM required, Pygmalion offers better chat capability than much larger language models with relatively minimal resources. Our curated dataset of high-quality roleplaying data ensures that your bot will be the optimal RP partner. Both the model weights and the code used to train it are completely open-source, and you can modify/re-distribute it for whatever purpose you want. Language models, including Pygmalion, generally run on GPUs since they need access to fast memory and massive processing power in order to output coherent text at an acceptable speed.

The complete LLM platform with a superior developer experience and robust infrastructure. Build, deploy, and manage hyper-personalized, streamlined, and trusted generative AI apps. Langbase is an open source OpenAI alternative, a new inference engine & AI tool for any LLM. The most "developer-friendly" LLM platform to ship hyper-personalized AI apps in seconds.

We’ve integrated Llama 3 into Meta AI, our intelligent assistant, that expands the ways people can get things done, create and connect with Meta AI. You can see first-hand the performance of Llama 3 by using Meta AI for coding tasks and problem solving. Whether you're developing agents, or other AI-powered applications, Llama 3 in both 8B and 70B will offer the capabilities and flexibility you need to develop your ideas. With the release of Llama 3, we’ve updated the Responsible Use Guide (RUG) to provide the most comprehensive information on responsible development with LLMs. Our system-centric approach includes updates to our trust and safety tools with Llama Guard 2, optimized to support the newly announced taxonomy published by MLCommons expanding its coverage to a more comprehensive set of safety categories, code shield, and Cybersec Eval 2.

Alpa aims to automate large-scale distributed training and serving with just a few lines of code. Alpa was initially developed by folks in the Sky Lab, UC Berkeley. Some advanced techniques used in Alpa have been written in a paper published in OSDI'2022. Alpa community is growing with new contributors from Google. A language model is a probability distribution over sequences of words. It predicts the next word based on all the previous words. It is useful for a variety of AI applications, such the auto-completion in your email or chatbot service. For more information, check out the language model wikipedia page. GPT-3 is very large language model, with 175 billion parameters, that uses deep learning to produce human-like text. Many researchers and news articles described GPT-3 as "one of the most interesting and important AI systems ever produced". GPT-3 is gradually being used as a backbone in the latest NLP research and applications.

InstructGPT is an open-source framework for training language models to generate natural language instructions from visual input. It uses a generative pre-trained transformer (GPT) model and the state-of-the-art object detector, Mask R-CNN, to detect objects in images and generate natural language sentences that describe the image. InstructGPT is designed to be effective across domains such as robotics, gaming and education; it can assist robots in navigating complex tasks with natural language instructions, or help students learn by providing descriptive explanations of processes or events.

Apply advanced coding and language models to a variety of use cases. Leverage large-scale, generative AI models with deep understandings of language and code to enable new reasoning and comprehension capabilities for building cutting-edge applications. Apply these coding and language models to a variety of use cases, such as writing assistance, code generation, and reasoning over data. Detect and mitigate harmful use with built-in responsible AI and access enterprise-grade Azure security. Gain access to generative models that have been pretrained with trillions of words. Apply them to new scenarios including language, code, reasoning, inferencing, and comprehension. Customize generative models with labeled data for your specific scenario using a simple REST API. Fine-tune your model's hyperparameters to increase accuracy of outputs. Use the few-shot learning capability to provide the API with examples and achieve more relevant results.

Fast and accurate AI models suited for production. Highly-available inference API leveraging the most advanced NVIDIA GPUs. We selected the best open-source natural language processing (NLP) models from the community and deployed them for you. Fine-tune your own models - including GPT-J - or upload your in-house custom models, and deploy them easily to production. Upload or Train/Fine-Tune your own AI models - including GPT-J - from your dashboard, and use them straight away in production without worrying about deployment considerations like RAM usage, high-availability, scalability... You can upload and deploy as many models as you want to production.

AI21 Studio provides API access to Jurassic-1 large-language-models. Our models power text generation and comprehension features in thousands of live applications. Take on any language task. Our Jurassic-1 models are trained to follow natural language instructions and require just a few examples to adapt to new tasks. Use our specialized APIs for common tasks like summarization, paraphrasing and more. Access superior results at a lower cost without reinventing the wheel. Need to fine-tune your own custom model? You're just 3 clicks away. Training is fast, affordable and trained models are deployed immediately. Give your users superpowers by embedding an AI co-writer in your app. Drive user engagement and success with features like long-form draft generation, paraphrasing, repurposing and custom auto-complete.

Announcing the launch of Jurassic-2, the latest generation of AI21 Studio’s foundation models, a game-changer in the field of AI, with top-tier quality and new capabilities. And that's not all, we're also releasing our task-specific APIs, with plug-and-play reading and writing capabilities that outperform competitors. Our focus at AI21 Studio is to help developers and businesses leverage reading and writing AI to build real-world products with tangible value. Today marks two important milestones with the release of Jurassic-2 and Task-Specific APIs, empowering you to bring generative AI to production. Jurassic-2 (or J2, as we like to call it) is the next generation of our foundation models with significant improvements in quality and new capabilities including zero-shot instruction-following, reduced latency, and multi-language support. Task-specific APIs provide developers with industry-leading APIs that perform specialized reading and writing tasks out-of-the-box.

FLAN-T5 was released in the paper Scaling Instruction-Finetuned Language Models - it is an enhanced version of T5 that has been finetuned in a mixture of tasks.

CodeGen is an open-source model for program synthesis. Trained on TPU-v4. Competitive with OpenAI Codex.

An implementation of model parallel autoregressive transformers on GPUs, based on the DeepSpeed library. This repository records EleutherAI's library for training large-scale language models on GPUs. Our current framework is based on NVIDIA's Megatron Language Model and has been augmented with techniques from DeepSpeed as well as some novel optimizations. We aim to make this repo a centralized and accessible place to gather techniques for training large-scale autoregressive language models, and accelerate research into large-scale training.

GPT-J is a cutting-edge language model created by the research organization EleutherAI. In terms of performance, GPT-J exhibits a level of proficiency comparable to that of OpenAI's renowned GPT-3 model in a range of zero-shot tasks. Notably, GPT-J has demonstrated the ability to surpass GPT-3 in tasks related to generating code. The latest iteration of this language model, known as GPT-J-6B, is built upon a linguistic dataset referred to as The Pile. This dataset, which is publicly available, encompasses a substantial volume of 825 gibibytes of language data, organized into 22 distinct subsets. While GPT-J shares certain capabilities with ChatGPT, it is important to note that GPT-J is not designed to operate as a chatbot; rather, its primary function is to predict text. In a significant development in March 2023, Databricks introduced Dolly, a model that follows instructions and is licensed under Apache.