Alternatives to Upstage

Command R+, Cohere's latest large language model, is optimized for conversational interactions and tasks with a long context. It is designed to be extremely performant and enable companies to move from proof-of-concept into production. We recommend Command R+ when working with workflows that rely on complex RAG functionality or multi-step tool usage (agents). Command R is better suited for retrieval augmented creation (RAG) tasks and single-step tool usage, or applications where cost is a key consideration.

ChatGPT is an OpenAI language model. It can generate human-like responses to a variety prompts, and has been trained on a wide range of internet texts. ChatGPT can be used to perform natural language processing tasks such as conversation, question answering, and text generation. ChatGPT is a pretrained language model that uses deep-learning algorithms to generate text. It was trained using large amounts of text data. This allows it to respond to a wide variety of prompts with human-like ease. It has a transformer architecture that has been proven to be efficient in many NLP tasks. ChatGPT can generate text in addition to answering questions, text classification and language translation. This allows developers to create powerful NLP applications that can do specific tasks more accurately. ChatGPT can also process code and generate it.

A small model with superior textual Intelligence and multimodal reasoning. GPT-4o Mini's low cost and low latency enable a wide range of tasks, including applications that chain or paralelize multiple model calls (e.g. calling multiple APIs), send a large amount of context to the models (e.g. full code base or history of conversations), or interact with clients through real-time, fast text responses (e.g. customer support chatbots). GPT-4o Mini supports text and vision today in the API. In the future, it will support text, image and video inputs and outputs. The model supports up to 16K outputs tokens per request and has knowledge until October 2023. It has a context of 128K tokens. The improved tokenizer shared by GPT-4o makes it easier to handle non-English text.

Qwen LLM is a family of large-language models (LLMs), developed by Damo Academy, an Alibaba Cloud subsidiary. These models are trained using a large dataset of text and codes, allowing them the ability to understand and generate text that is human-like, translate languages, create different types of creative content and answer your question in an informative manner. Here are some of the key features of Qwen LLMs. Variety of sizes: Qwen's series includes sizes ranging from 1.8 billion parameters to 72 billion, offering options that meet different needs and performance levels. Open source: Certain versions of Qwen have open-source code, which is available to anyone for use and modification. Qwen is multilingual and can translate multiple languages including English, Chinese and Japanese. Qwen models are capable of a wide range of tasks, including text summarization and code generation, as well as generation and translation.

Samsung Gauss, a new AI-model developed by Samsung Electronics, is a powerful AI tool. It is a large-language model (LLM) which has been trained using a massive dataset. Samsung Gauss can generate text, translate different languages, create creative content and answer questions in a helpful way. Samsung Gauss, which is still in development, has already mastered many tasks, including Follow instructions and complete requests with care. Answering questions in an informative and comprehensive way, even when they are open-ended, challenging or strange. Creating different creative text formats such as poems, code, musical pieces, emails, letters, etc. Here are some examples to show what Samsung Gauss is capable of: Translation: Samsung Gauss is able to translate text between many languages, including English and German, as well as Spanish, Chinese, Japanese and Korean. Coding: Samsung Gauss can generate code.

With just a few lines, you can integrate natural language understanding and generation into the product. The Cohere API allows you to access models that can read billions upon billions of pages and learn the meaning, sentiment, intent, and intent of every word we use. You can use the Cohere API for human-like text. Simply fill in a prompt or complete blanks. You can create code, write copy, summarize text, and much more. Calculate the likelihood of text, and retrieve representations from your model. You can filter text using the likelihood API based on selected criteria or categories. You can create your own downstream models for a variety of domain-specific natural languages tasks by using representations. The Cohere API is able to compute the similarity of pieces of text and make categorical predictions based on the likelihood of different text options. The model can see ideas through multiple lenses so it can identify abstract similarities between concepts as distinct from DNA and computers.

GPT-4 (Generative Pretrained Transformer 4) a large-scale, unsupervised language model that is yet to be released. GPT-4, which is the successor of GPT-3, is part of the GPT -n series of natural-language processing models. It was trained using a dataset of 45TB text to produce text generation and understanding abilities that are human-like. GPT-4 is not dependent on additional training data, unlike other NLP models. It can generate text and answer questions using its own context. GPT-4 has been demonstrated to be capable of performing a wide range of tasks without any task-specific training data, such as translation, summarization and sentiment analysis.

Qwen2.5, an advanced multimodal AI system, is designed to provide highly accurate responses that are context-aware across a variety of applications. It builds on its predecessors' capabilities, integrating cutting edge natural language understanding, enhanced reasoning, creativity and multimodal processing. Qwen2.5 is able to analyze and generate text as well as interpret images and interact with complex data in real-time. It is highly adaptable and excels at personalized assistance, data analytics, creative content creation, and academic research. This makes it a versatile tool that can be used by professionals and everyday users. Its user-centric approach emphasizes transparency, efficiency and alignment with ethical AI.

PaLM 2 is Google's next-generation large language model, which builds on Google’s research and development in machine learning. It excels in advanced reasoning tasks including code and mathematics, classification and question-answering, translation and multilingual competency, and natural-language generation better than previous state-of the-art LLMs including PaLM. It is able to accomplish these tasks due to the way it has been built - combining compute-optimal scale, an improved dataset mix, and model architecture improvement. PaLM 2 is based on Google's approach for building and deploying AI responsibly. It was rigorously evaluated for its potential biases and harms, as well as its capabilities and downstream applications in research and product applications. It is being used to power generative AI tools and features at Google like Bard, the PaLM API, and other state-ofthe-art models like Sec-PaLM and Med-PaLM 2.

Claude Pro is a large language model that can handle complex tasks with a friendly and accessible demeanor. It is trained on high-quality, extensive data and excels at understanding contexts, interpreting subtleties, and producing well structured, coherent responses to a variety of topics. Claude Pro is able to create detailed reports, write creative content, summarize long documents, and assist with coding tasks by leveraging its robust reasoning capabilities and refined knowledge base. Its adaptive algorithms constantly improve its ability learn from feedback. This ensures that its output is accurate, reliable and helpful. Whether Claude Pro is serving professionals looking for expert support or individuals seeking quick, informative answers - it delivers a versatile, productive conversational experience.

We've developed a model, called ChatGPT, that interacts in a conversational manner. ChatGPT can use the dialogue format to answer questions, admit mistakes, challenge incorrect premises and reject inappropriate requests. ChatGPT is the sibling model of InstructGPT. InstructGPT is trained to follow a prompt, and then provide a detailed answer. ChatGPT Plus, a subscription plan to ChatGPT, a conversational AI. ChatGPT Plus is $20/month and subscribers receive a variety of benefits. - ChatGPT is available to all users, even at peak times - Faster response time Access to GPT-4 ChatGPT plugins Chat with Web-browsingGPT - Priority access for new features and improvements ChatGPT Plus will be available to all customers in the United States. We will begin inviting people on our waitlist within the next few weeks. We plan to extend access and support to other countries and regions in the near future.

CodeQwen, developed by the Qwen Team, Alibaba Cloud, is the code version. It is a transformer based decoder only language model that has been pre-trained with a large number of codes. A series of benchmarks shows that the code generation is strong and that it performs well. Supporting long context generation and understanding with a context length of 64K tokens. CodeQwen is a 92-language coding language that provides excellent performance for text-to SQL, bug fixes, and more. CodeQwen chat is as simple as writing a few lines of code using transformers. We build the tokenizer and model using pre-trained methods and use the generate method for chatting. The chat template is provided by the tokenizer. Following our previous practice, we apply the ChatML Template for chat models. The model will complete the code snippets in accordance with the prompts without any additional formatting.

Mistral Large 2 comes with a 128k window that supports dozens of different languages, including French, German and Spanish. It also supports Arabic, Hindi, Russian and Chinese. It also supports 80+ programming languages, including Python, Java and C++. Mistral Large 2 was designed with single-node applications in mind. Its size of 123 million parameters allows it to run fast on a single computer. Mistral Large 2 is released under the Mistral Research License which allows modification and usage for research and noncommercial purposes.

Ernie Bot (Wenxin Yiyan), a Baidu conversational AI chatbot, is a new chatbot that can answer any type of question a user may have.

ChatGLM-6B, a Chinese-English bilingual dialogue model based on General Language Model architecture (GLM), has 6.2 billion parameters. Users can deploy model quantization locally on consumer-grade graphic cards (only 6GB video memory required at INT4 quantization levels). ChatGLM-6B is based on technology similar to ChatGPT and optimized for Chinese dialogue and Q&A. After approximately 1T identifiers for Chinese and English bilingual training and supplemented with supervision and fine-tuning as well as feedback self-help and human feedback reinforcement learning, ChatGLM-6B, with 6.2 billion parameters, has been able generate answers that are in line with human preference.

Magic's LTM-1 provides context windows 50x larger than transformers. Magic has trained a Large Language Model that can take in huge amounts of context to generate suggestions. Magic, our coding assistant can now see all of your code. AI models can refer to more factual and explicit information with larger context windows. They can also reference their own actions history. This research will hopefully improve reliability and coherence.

Alpa aims automate large-scale distributed training. Alpa was originally developed by people at UC Berkeley's Sky Lab. Alpa's advanced techniques were described in a paper published by OSDI'2022. Google is adding new members to the Alpa community. A language model is a probabilistic distribution of probability over a sequence of words. It uses all the words it has seen to predict the next word. It is useful in a variety AI applications, including the auto-completion of your email or chatbot service. You can find more information on the language model Wikipedia page. GPT-3 is a large language model with 175 billion parameters that uses deep learning to produce text that looks human-like. GPT-3 was described by many researchers and news articles as "one the most important and interesting AI systems ever created." GPT-3 is being used as a backbone for the latest NLP research.

Sparrow is a research model that serves as a proof of concept. It was created with the goal to train dialogue agents to be more helpful and correct. Sparrow helps us understand how to train agents to be more helpful and safer, and ultimately to help create safer and more useful artificial intelligence (AGI). Sparrow is currently not available for public use. Because it is difficult to determine what makes a conversation successful, training conversational AI can be a challenging problem. We use reinforcement learning (RL) to address this problem. This is a form that uses people's feedback and the preference feedback of study participants to train a model about how useful an answer is. We show participants multiple models of the same question, and ask them which one they prefer.

LaMDA, our most recent research breakthrough, adds pieces of the most intriguing piece of that puzzle: Conversation. Although conversations are more focused on specific topics, they can also be open-ended and lead to completely new areas. Talking to a friend about a TV program could turn into a conversation about the country in which the show was shot. Then, the conversation could lead to a debate about the best regional cuisine in that country. Modern chatbots, also known as chatbots, can be a bit stumped by this wandering quality. They tend to follow pre-determined paths and narrow conversations. LaMDA, which stands for "Language Model for Dialog Applications", can engage in a free-flowing manner about seemingly endless topics. This ability could open up new ways to interact with technology and help you find more useful applications.

Our new flagship model, the Small Language Model (SLM), has all the power and performance that you would expect from a leading LLM. Excels at generalized tasks, instruction-following, and human preferences. The best 70B model available. SuperNova is a generalized task-based AI that can be used for any generalized task. It's similar to Open AI's GPT4o and Claude Sonnet 3.5. SuperNova is trained with the most advanced optimization & learning techniques to generate highly accurate responses. It is the most flexible, cost-effective, and secure language model available. Customers can save up to 95% in total deployment costs when compared with traditional closed-source models. SuperNova can be used to integrate AI in apps and products, as well as for general chat and a variety of other uses. Update your models regularly with the latest open source tech to ensure you're not locked into a single solution. Protect your data using industry-leading privacy features.

Small language models (SLMs), a powerful family of small language models, with low cost and low-latency performance. Maximize AI capabilities and lower resource usage, while ensuring cost-effective generative AI implementations across your applications. Accelerate response time in real-time interaction, autonomous systems, low latency apps, and other critical scenarios. Phi-3 can be run in the cloud, on the edge or on the device. This allows for greater flexibility in deployment and operation. Phi-3 models have been developed according to Microsoft AI principles, including accountability, transparency and fairness, reliability, safety and security, privacy, and inclusivity. Operate efficiently in offline environments, where data privacy or connectivity are limited. Expanded context window allows for more accurate, contextually relevant and coherent outputs. Deploy at edge to deliver faster response.

GPT-5 is OpenAI's Generative Pretrained Transformer. It is a large-language model (LLM), which is still in development. LLMs have been trained to work with massive amounts of text and can generate realistic and coherent texts, translate languages, create different types of creative content and answer your question in a way that is informative. It's still not available to the public. OpenAI has not announced a release schedule, but some believe it could launch in 2024. It's expected that GPT-5 will be even more powerful. GPT-4 has already proven to be impressive. It is capable of writing creative content, translating languages and generating text of human-quality. GPT-5 will be expected to improve these abilities, with improved reasoning, factual accuracy and ability to follow directions.

Megatron-Turing Natural Language Generation Model (MT-NLG) is the largest and most powerful monolithic English language model. It has 530 billion parameters. This 105-layer transformer-based MTNLG improves on the previous state-of-the art models in zero, one, and few shot settings. It is unmatched in its accuracy across a wide range of natural language tasks, including Completion prediction and Reading comprehension. NVIDIA has announced an Early Access Program for its managed API service in MT-NLG Mode. This program will allow customers to experiment with, employ and apply a large language models on downstream language tasks.

GPT-4o (o for "omni") is an important step towards a more natural interaction between humans and computers. It accepts any combination as input, including text, audio and image, and can generate any combination of outputs, including text, audio and image. It can respond to audio in as little as 228 milliseconds with an average of 325 milliseconds. This is similar to the human response time in a conversation (opens in new window). It is as fast and cheaper than GPT-4 Turbo on text in English or code. However, it has a significant improvement in text in non-English language. GPT-4o performs better than existing models at audio and vision understanding.

Baichuan-13B, a large-scale language model with 13 billion parameters that is open source and available commercially by Baichuan Intelligent, was developed following Baichuan -7B. It has the best results for a language model of the same size in authoritative Chinese and English benchmarks. This release includes two versions of pretraining (Baichuan-13B Base) and alignment (Baichuan-13B Chat). Baichuan-13B has more data and a larger size. It expands the number parameters to 13 billion based on Baichuan -7B, and trains 1.4 trillion coins on high-quality corpus. This is 40% more than LLaMA-13B. It is open source and currently the model with the most training data in 13B size. Support Chinese and English bi-lingual, use ALiBi code, context window is 4096.

OpenAI's mission, which is to ensure artificial general intelligence (AGI), benefits all people. This refers to highly autonomous systems that outperform humans in most economically valuable work. While we will try to build safe and useful AGI, we will also consider our mission accomplished if others are able to do the same. Our API can be used to perform any language task, including summarization, sentiment analysis and content generation. You can specify your task in English or use a few examples. Our constantly improving AI technology is available to you with a simple integration. These sample completions will show you how to integrate with the API.

Mixtral 8x22B is our latest open model. It sets new standards for performance and efficiency in the AI community. It is a sparse Mixture-of-Experts model (SMoE), which uses only 39B active variables out of 141B. This offers unparalleled cost efficiency in relation to its size. It is fluently bilingual in English, French Italian, German and Spanish. It has strong math and coding skills. It is natively able to call functions; this, along with the constrained-output mode implemented on La Plateforme, enables application development at scale and modernization of tech stacks. Its 64K context window allows for precise information retrieval from large documents. We build models with unmatched cost-efficiency for their respective sizes. This allows us to deliver the best performance-tocost ratio among models provided by the Community. Mixtral 8x22B continues our open model family. Its sparse patterns of activation make it faster than any 70B model.

Opus, our intelligent model, is superior to its peers in most of the common benchmarks for AI systems. These include undergraduate level expert knowledge, graduate level expert reasoning, basic mathematics, and more. It displays near-human levels in terms of comprehension and fluency when tackling complex tasks. This is at the forefront of general intelligence. All Claude 3 models have increased capabilities for analysis and forecasting. They also offer nuanced content generation, code generation and the ability to converse in non-English language such as Spanish, Japanese and French.

Mistral NeMo, our new best small model. A state-of the-art 12B with 128k context and released under Apache 2.0 license. Mistral NeMo, a 12B-model built in collaboration with NVIDIA, is available. Mistral NeMo has a large context of up to 128k Tokens. Its reasoning, world-knowledge, and coding precision are among the best in its size category. Mistral NeMo, which relies on a standard architecture, is easy to use. It can be used as a replacement for any system that uses Mistral 7B. We have released Apache 2.0 licensed pre-trained checkpoints and instruction-tuned base checkpoints to encourage adoption by researchers and enterprises. Mistral NeMo has been trained with quantization awareness to enable FP8 inferences without performance loss. The model was designed for global applications that are multilingual. It is trained in function calling, and has a large contextual window. It is better than Mistral 7B at following instructions, reasoning and handling multi-turn conversation.

PygmalionAI, a community of open-source projects based upon EleutherAI’s GPT-J 6B models and Meta’s LLaMA model, was founded in 2009. Pygmalion AI is designed for roleplaying and chatting. The 7B variant of the Pygmalion AI is currently actively supported. It is based on Meta AI’s LLaMA AI model. Pygmalion's chat capabilities are superior to larger language models that require much more resources. Our curated datasets of high-quality data on roleplaying ensure that your bot is the best RP partner. The model weights as well as the code used to train the model are both open-source. You can modify/re-distribute them for any purpose you like. Pygmalion and other language models run on GPUs because they require fast memory and massive processing to produce coherent text at a reasonable speed.

Palmyra is an enterprise-ready suite of Large Language Models. These models are excellent at tasks like image analysis, question answering, and supporting over 30 languages. They can be fine-tuned for industries such as healthcare and finance. Palmyra models are notable for their top rankings in benchmarks such as Stanford HELM and PubMedQA. Palmyra Fin is the first model that passed the CFA Level III examination. Writer protects client data by not using it to train or modify models. They have a zero-data retention policy. Palmyra includes specialized models, such as Palmyra X 004, which has tool-calling abilities; Palmyra Med for healthcare; Palmyra Fin for finance; and Palmyra Vision for advanced image and video processing. These models are available via Writer's full stack generative AI platform which integrates graph based Retrieval augmented Generation (RAG).

Use generative language models for improving and optimizing your web services and applications. Get a consolidated result of textual data, whether it is information from chats at work, user reviews or other types. YandexGPT can help summarize and interpret information. Improve the quality and style of your text to speed up the creation process. Create templates for newsletters, product description for online stores, and other applications. Create a chatbot to help your customer service. Teach the bot how to answer common and complex questions. Use the API to automate processes and integrate the service into your applications.

Marco-o1 is an advanced AI model that is designed for high-performance problem solving and natural language processing. It is designed to deliver precise, contextually rich answers by combining deep language understanding with a streamlined architectural design for speed and efficiency. Marco-o1 is a versatile AI system that excels at a wide range of tasks, including conversational AI. It also excels at content creation, technical assistance, and decision-making. It adapts seamlessly to the needs of diverse users. Marco-o1 is a cutting edge solution for individuals and organisations seeking intelligent, adaptive and scalable AI tools. It focuses on intuitive interactions, reliability and ethical AI principles. MCTS allows for the exploration of multiple reasoning pathways using confidence scores derived by softmax-applied logging probabilities of the top k alternative tokens. This guides the model to optimal solution.

Hermes 3 contains advanced long-term context retention and multi-turn conversation capabilities, complex roleplaying and internal monologue abilities, and enhanced agentic function-calling. Hermes 3 has advanced long-term contextual retention, multi-turn conversation capabilities, complex roleplaying, internal monologue, and enhanced agentic functions-calling. Our training data encourages the model in a very aggressive way to follow the system prompts and instructions exactly and in a highly adaptive manner. Hermes 3 was developed by fine-tuning Llama 3.0 8B, 70B and 405B and training with a dataset primarily containing synthetic responses. The model has a performance that is comparable to Llama 3.1, but with deeper reasoning and creative abilities. Hermes 3 is an instruct and tool-use model series with strong reasoning and creativity abilities.

Multilingual T5 is a massively pretrained text-totext transformer model that has been trained using a similar recipe to T5. This repo can used to reproduce the experiments described in the mT5 article. The mC4 corpus covers 101 languages. Afrikaans, Albanian, Amharic, Arabic, Armenian, Azerbaijani, Basque, Belarusian, Bengali, Bulgarian, Burmese, Catalan, Cebuano, Chichewa, Chinese, Corsican, Czech, Danish, Dutch, English, Esperanto, Estonian, Filipino, Finnish, French, Galician, Georgian, German, Greek, Gujarati, Haitian Creole, Hausa, Hawaiian, Hebrew, Hindi, Hmong, Hungarian, Icelandic, Igbo, Indonesian, Irish, Italian, Japanese, Javanese, Kannada, Kazakh, Khmer, Korean, Kurdish, Kyrgyz, Lao, Latin, Latvian, Lithuanian, Luxembourgish, Macedonian, Malagasy, Malay, Malayalam, Maltese, Maori, Marathi, Mongolian, Nepali, Norwegian, Pashto, Persian, Polish, Portuguese, Punjabi, Romanian, Russian, Samoan, Scottish Gaelic, Serbian, Shona, Sindhi, and more.

The next generation of the large language model. This release includes modelweights and starting code to pretrained and fine tuned Llama languages models, ranging from 7B-70B parameters. Llama 1 models have a context length of 2 trillion tokens. Llama 2 models have a context length double that of Llama 1. The fine-tuned Llama 2 models have been trained using over 1,000,000 human annotations. Llama 2, a new open-source language model, outperforms many other open-source language models in external benchmarks. These include tests of reasoning, coding and proficiency, as well as knowledge tests. Llama 2 has been pre-trained using publicly available online data sources. Llama-2 chat, a fine-tuned version of the model, is based on publicly available instruction datasets, and more than 1 million human annotations. We have a wide range of supporters in the world who are committed to our open approach for today's AI. These companies have provided early feedback and have expressed excitement to build with Llama 2

PanGu Chat, an AI chatbot created by Huawei, is a powerful AI. PanGu Chat can answer questions and converse with you like ChatGPT.

BLOOM (autoregressive large language model) is trained to continue text using a prompt on large amounts of text data. It uses industrial-scale computational resources. It can produce coherent text in 46 languages and 13 programming language, which is almost impossible to distinguish from text written by humans. BLOOM can be trained to perform text tasks that it hasn’t been explicitly trained for by casting them as text generation jobs.

Gemini Flash, a large language model from Google, is specifically designed for low-latency, high-speed language processing tasks. Gemini Flash, part of Google DeepMind’s Gemini series is designed to handle large-scale applications and provide real-time answers. It's ideal for interactive AI experiences such as virtual assistants, live chat, and customer support. Gemini Flash is built on sophisticated neural structures that ensure contextual relevance, coherence, and precision. Google has built in rigorous ethical frameworks as well as responsible AI practices to Gemini Flash. It also equipped it with guardrails that manage and mitigate biased outcomes, ensuring alignment with Google's standards of safe and inclusive AI. Google's Gemini Flash empowers businesses and developers with intelligent, responsive language tools that can keep up with fast-paced environments.

Claude is an artificial intelligence language model that can generate text with human-like processing. Anthropic is an AI safety company and research firm that focuses on building reliable, interpretable and steerable AI systems. While large, general systems can provide significant benefits, they can also be unpredictable, unreliable and opaque. Our goal is to make progress in these areas. We are currently focusing on research to achieve these goals. However, we see many opportunities for our work in the future to create value both commercially and for the public good.

Experience JinaChat - a LLM service designed for professionals. JinaChat is a multimodal chat service that goes beyond text and includes images. Enjoy our free short interactions below 100 tokens. Our API allows developers to build complex applications by leveraging long conversation histories. JinaChat is the future of LLM, with multimodal conversations that are long-memory and affordable. Modern LLM applications are often based on long prompts or large memory, which can lead to high costs if the same prompts are sent repeatedly to the server. JinaChat API solves this issue by allowing you to carry forward previous conversations, without having to resend the entire prompt. This is a great way to save both time and money when developing complex applications such as AutoGPT.

With T5, we propose re-framing all NLP into a unified format where the input and the output are always text strings. This is in contrast to BERT models which can only output a class label, or a span from the input. Our text-totext framework allows us use the same model and loss function on any NLP task. This includes machine translation, document summary, question answering and classification tasks. We can also apply T5 to regression by training it to predict a string representation of a numeric value instead of the actual number.

DataGemma is a pioneering project by Google that aims to improve the accuracy and reliability large language models (LLMs), when dealing with numerical and statistical data. DataGemma, launched as a collection of open models, leverages Google's Data Commons - a vast repository for public statistical data - to ground its responses in actual facts. This initiative uses two innovative approaches, Retrieval Interleaved Generation and Retrieval Augmented Generation. RIG integrates real-time checks of data during the generation process, ensuring factual accuracy. RAG retrieves pertinent information before generating answers, reducing the likelihood that AI hallucinations will occur. DataGemma's goal is to provide users with factual and trustworthy answers. This marks a significant step in reducing the amount of misinformation that AI-generated content contains.

StarCoderBase and StarCoder are Large Language Models (Code LLMs), trained on permissively-licensed data from GitHub. This includes data from 80+ programming language, Git commits and issues, Jupyter Notebooks, and Git commits. We trained a 15B-parameter model for 1 trillion tokens, similar to LLaMA. We refined the StarCoderBase for 35B Python tokens. The result is a new model we call StarCoder. StarCoderBase is a model that outperforms other open Code LLMs in popular programming benchmarks. It also matches or exceeds closed models like code-cushman001 from OpenAI, the original Codex model which powered early versions GitHub Copilot. StarCoder models are able to process more input with a context length over 8,000 tokens than any other open LLM. This allows for a variety of interesting applications. By prompting the StarCoder model with a series dialogues, we allowed them to act like a technical assistant.

PanGu-a was developed under MindSpore, and trained on 2048 Ascend AI processors. The MindSpore Auto-parallel parallelism strategy was implemented to scale the training task efficiently to 2048 processors. This includes data parallelism as well as op-level parallelism. We pretrain PanGu-a with 1.1TB of high-quality Chinese data collected from a variety of domains in order to enhance its generalization ability. We test the generation abilities of PanGua in different scenarios, including text summarizations, question answering, dialog generation, etc. We also investigate the effects of model scaling on the few shot performances across a wide range of Chinese NLP task. The experimental results show that PanGu-a is superior in performing different tasks with zero-shot or few-shot settings.

Smaug 72B is an open-source large-language model (LLM), which is known for its key features. High Performance: It is currently ranked first on the Hugging face Open LLM leaderboard. This model has surpassed models such as GPT-3.5 across a range of benchmarks. This means that it excels in tasks such as understanding, responding to and generating text similar to human speech. Open Source: Smaug-72B, unlike many other advanced LLMs is available to anyone for free use and modification, fostering collaboration, innovation, and creativity in the AI community. Focus on Math and Reasoning: It excels at handling mathematical and reasoning tasks. This is attributed to the unique fine-tuning technologies developed by Abacus, the creators Smaug 72B. Based on Qwen 72B: This is a finely tuned version of another powerful LLM, called Qwen 72B, released by Alibaba. It further improves its capabilities. Smaug-72B is a significant advance in open-source AI.

We are proud to introduce Codestral, the first code model we have ever created. Codestral is a generative AI model that is open-weight and specifically designed for code generation. It allows developers to interact and write code using a shared API endpoint for instructions and completion. It can be used for advanced AI applications by software developers as it is able to master both code and English. Codestral has been trained on a large dataset of 80+ languages, including some of the most popular, such as Python and Java. It also includes C, C++ JavaScript, Bash, C, C++. It also performs well with more specific ones, such as Swift and Fortran. Codestral's broad language base allows it to assist developers in a variety of coding environments and projects.

Vicuna-13B, an open-source chatbot, is trained by fine-tuning LLaMA using user-shared conversations from ShareGPT. Vicuna-13B's preliminary evaluation using GPT-4, as a judge, shows that it achieves a quality of more than 90%* for OpenAI ChatGPT or Google Bard and outperforms other models such as LLaMA or Stanford Alpaca. Vicuna-13B costs around $300 to train. The online demo and the code, along with weights, are available to non-commercial users.

Our fastest model, which delivers advanced coding, tool usage, and reasoning for an affordable price Claude 3.5 Haiku, our next-generation model, is our fastest. Claude 3.5 Haiku is faster than Claude 3 Haiku and has improved in every skill set. It also surpasses Claude 3 Opus on many intelligence benchmarks. Claude 3.5 Haiku can be accessed via our first-party APIs, Amazon Bedrock and Google Cloud Vertex AI. Initially, it is available as a text only model, with image input coming later.

LFM-3B offers incredible performance for its small size. It is ranked first among 3B parameter transforms, hybrids and RNN models. It also outperforms previous generations of 7B and13B models. It is also comparable to Phi-3.5 mini on multiple benchmarks while being 18.4% smaller. LFM-3B can be used for mobile applications and other text-based edge applications.