Alternatives to Martian

Mixtral 8x7B has open weights and is a high quality sparse mixture expert model (SMoE). Licensed under Apache 2.0. Mixtral outperforms Llama 70B in most benchmarks, with 6x faster Inference. It is the strongest model with an open-weight license and the best overall model in terms of cost/performance tradeoffs. It matches or exceeds GPT-3.5 in most standard benchmarks.

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.

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

Jamba is a powerful and efficient long context model that is open to builders, but built for enterprises. Jamba's latency is superior to all other leading models of similar size. Jamba's 256k window is the longest available. Jamba's Mamba Transformer MoE Architecture is designed to increase efficiency and reduce costs. Jamba includes key features from OOTB, including function calls, JSON output, document objects and citation mode. Jamba 1.5 models deliver high performance throughout the entire context window. Jamba 1.5 models score highly in common quality benchmarks. Secure deployment tailored to your enterprise. Start using Jamba immediately on our production-grade SaaS Platform. Our strategic partners can deploy the Jamba model family. For enterprises who require custom solutions, we offer VPC and on-premise deployments. We offer hands-on management and continuous pre-training for enterprises with unique, bespoke needs.

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.

Entry Point AI is a modern AI optimization platform that optimizes proprietary and open-source language models. Manage prompts and fine-tunes in one place. We make it easy to fine-tune models when you reach the limits. Fine-tuning involves showing a model what to do, not telling it. It works in conjunction with prompt engineering and retrieval augmented generation (RAG) in order to maximize the potential of AI models. Fine-tuning your prompts can help you improve their quality. Imagine it as an upgrade to a few-shot model that incorporates the examples. You can train a model to perform at the same level as a high-quality model for simpler tasks. This will reduce latency and costs. For safety, to protect the brand, or to get the formatting correct, train your model to not respond in a certain way to users. Add examples to your dataset to cover edge cases and guide model behavior.

Our models are deployed all over the world for the best possible response time. Integrate our OpenAI compatible API seamlessly into your application. Start in minutes and scale seamlessly to serve millions of users. Our API is 4 times cheaper than OpenAI GPT-3.5 API due to our extensive performance and scale optimizations. Our AI model can generate text and chat at ChatGPT performance levels for a fraction of what it costs. Our OpenAI-compatible API makes it easy to get started. Use one of the most powerful AI image models in order to create stunning images, graphics and illustrations.

GPT-3.5 is the next evolution to GPT 3 large language model, OpenAI. GPT-3.5 models are able to understand and generate natural languages. There are four main models available with different power levels that can be used for different tasks. The main GPT-3.5 models can be used with the text completion endpoint. There are models that can be used with other endpoints. Davinci is the most versatile model family. It can perform all tasks that other models can do, often with less instruction. Davinci is the best choice for applications that require a deep understanding of the content. This includes summarizations for specific audiences and creative content generation. These higher capabilities mean that Davinci is more expensive per API call and takes longer to process than other models.

OpenAI o1 mini is a new and cost-effective AI designed to enhance reasoning, especially in STEM fields such as mathematics and coding. It is part of the o1 Series, which focuses on solving problems by spending more "thinking" time through solutions. The o1 mini is 80% cheaper and smaller than its sibling. It performs well in coding and mathematical reasoning tasks.

It's as simple as changing one line in code to switch. Feature parity with industry-standard APIs ensures that your product runs faster and works the same way. GooseAI is a fully managed NLP as-a-Service delivered via API. In this respect, it is comparable to OpenAI. It is compatible with OpenAI’s completion API. Our state-of the-art selection GPT-based language models, uncompromising speed, and flexible alternative to your current provider will give you a jumpstart in your next project. We are proud to be able offer prices that are up to 70% lower than other providers and still deliver the same or better performance. Geese are integral to the ecosystem, just as the Mitochondria powerhouses cells. We were inspired by their beauty and elegance to fly high, just like geese.

OpenAI o1 is a new series AI models developed by OpenAI that focuses on enhanced reasoning abilities. These models, such as o1 preview and o1 mini, are trained with a novel reinforcement-learning approach that allows them to spend more time "thinking through" problems before presenting answers. This allows o1 excel in complex problem solving tasks in areas such as coding, mathematics, or science, outperforming other models like GPT-4o. The o1 series is designed to tackle problems that require deeper thinking processes. This marks a significant step in AI systems that can think more like humans.

Claude 3.5 Sonnet is a new benchmark for the industry in terms of graduate-level reasoning (GPQA), undergrad-level knowledge (MMLU), as well as coding proficiency (HumanEval). It is exceptional in writing high-quality, relatable content that is written with a natural and relatable tone. It also shows marked improvements in understanding nuance, humor and complex instructions. Claude 3.5 Sonnet is twice as fast as Claude 3 Opus. Claude 3.5 Sonnet is ideal for complex tasks, such as providing context-sensitive support to customers and orchestrating workflows. Claude 3.5 Sonnet can be downloaded for free from Claude.ai and Claude iOS, and subscribers to the Claude Pro and Team plans will have access to it at rates that are significantly higher. It is also accessible via the Anthropic AI, Amazon Bedrock and Google Cloud Vertex AI. The model costs $3 for every million input tokens. It costs $15 for every million output tokens. There is a 200K token window.

Phi-2 is a 2.7-billion-parameter language-model that shows outstanding reasoning and language-understanding capabilities. It represents the state-of-the art performance among language-base models with less than thirteen billion parameters. Phi-2 can match or even outperform models 25x larger on complex benchmarks, thanks to innovations in model scaling. Phi-2's compact size makes it an ideal playground for researchers. It can be used for exploring mechanistic interpretationability, safety improvements or fine-tuning experiments on a variety tasks. We have included Phi-2 in the Azure AI Studio catalog to encourage research and development of language models.

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.

Gemini models are a family of light-open, state-of-the art models that was created using the same research and technology as Gemini models. These models include comprehensive security measures, and help to ensure responsible and reliable AI through selected data sets. Gemma models have exceptional comparative results, even surpassing some larger open models, in their 2B and 7B sizes. Keras 3.0 offers seamless compatibility with JAX TensorFlow PyTorch and JAX. Gemma 2 has been redesigned to deliver unmatched performance and efficiency. It is optimized for inference on a variety of hardware. The Gemma models are available in a variety of models that can be customized to meet your specific needs. The Gemma models consist of large text-to text lightweight language models that have a decoder and are trained on a large set of text, code, or mathematical content.

OpenPipe provides fine-tuning for developers. Keep all your models, datasets, and evaluations in one place. New models can be trained with a click of a mouse. Automatically record LLM responses and requests. Create datasets using your captured data. Train multiple base models using the same dataset. We can scale your model to millions of requests on our managed endpoints. Write evaluations and compare outputs of models side by side. You only need to change a few lines of code. OpenPipe API Key can be added to your Python or Javascript OpenAI SDK. Custom tags make your data searchable. Small, specialized models are much cheaper to run than large, multipurpose LLMs. Replace prompts in minutes instead of weeks. Mistral and Llama 2 models that are fine-tuned consistently outperform GPT-4-1106 Turbo, at a fraction the cost. Many of the base models that we use are open-source. You can download your own weights at any time when you fine-tune Mistral or Llama 2.

GPT-J, a cutting edge language model developed by EleutherAI, is a leading-edge language model. GPT-J's performance is comparable to OpenAI's GPT-3 model on a variety of zero-shot tasks. GPT-J, in particular, has shown that it can surpass GPT-3 at tasks relating to code generation. The latest version of this language model is GPT-J-6B and is built on a linguistic data set called The Pile. This dataset is publically available and contains 825 gibibytes worth of language data organized into 22 subsets. GPT-J has some similarities with ChatGPT. However, GPTJ is not intended to be a chatbot. Its primary function is to predict texts. Databricks made a major development in March 2023 when they introduced Dolly, an Apache-licensed model that follows instructions.

A marketplace of LLMs created by developers on technical topics, and managed by a community. Outperforms standalone AI models. Lunes, which are constantly updated on the latest technical knowledge sources, such as Github repositories and documentation, can reduce hallucinations when it comes to technical queries. You can get references back, just like with Perplexity. Use hundreds of Lunes created by other users, ranging from Lunes that are trained on open-source software to curated collections based on tech blog posts. Get exposure by creating one using a variety sources, such as your own projects. Our API can be hot-swapped with OpenAI's. Integrate with Cursor and Continue, as well as other tools that support OpenAI compatible models. You can continue your conversation from your IDE on Lune Web anytime. Get paid for each approved comment you make directly in the chat. Create a public Lune, share it and get paid based on its popularity.

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.

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.

With our enterprise Gen AI stack you can go beyond your imagination. You can instantly offload manual tasks and give them to powerful assistants. The sky is the limit. For enterprises that place data security first, fine-tune generative AI models and deploy them on your own cloud securely.

You can use advanced language models and coding to solve a variety of problems. To build cutting-edge applications, leverage large-scale, generative AI models that have deep understandings of code and language to allow for new reasoning and comprehension. These coding and language models can be applied to a variety use cases, including writing assistance, code generation, reasoning over data, and code generation. Access enterprise-grade Azure security and detect and mitigate harmful use. Access generative models that have been pretrained with trillions upon trillions of words. You can use them to create new scenarios, including code, reasoning, inferencing and comprehension. A simple REST API allows you to customize generative models with labeled information for your particular scenario. To improve the accuracy of your outputs, fine-tune the hyperparameters of your model. You can use the API's few-shot learning capability for more relevant results and to provide examples.

Amazon Nova is the new generation of foundation models (FMs), which are state-of-the art (SOTA), and offer industry-leading price-performance. They are available exclusively through Amazon Bedrock. Amazon Nova Micro and Amazon Nova Lite are understanding models which accept text, images, or videos as inputs and produce text output. They offer a wide range of capabilities, accuracy, speed and cost operation points. Amazon Nova Micro, a text-only model, delivers the lowest latency at a very low price. Amazon Nova Lite, a multimodal model with a low cost, is lightning-fast at processing text, image, and video inputs. Amazon Nova Pro is an extremely capable multimodal model that offers the best combination of accuracy and speed for a variety of tasks. Amazon Nova Pro is a powerful model that can handle almost any task. Its speed and cost efficiency are industry-leading.

Jurassic-2 is the latest generation AI21 Studio foundation models. It's a game changer in the field AI, with new capabilities and top-tier quality. We're also releasing task-specific APIs with superior reading and writing capabilities. AI21 Studio's focus is to help businesses and developers leverage reading and writing AI in order to build real-world, tangible products. The release of Task-Specific and Jurassic-2 APIs marks two significant milestones. They will enable you to bring generative AI into 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 offer developers industry-leading APIs for performing specialized reading and/or writing tasks.

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.

GradientJ gives you everything you need to create large language models in minutes, and manage them for life. Save versions of prompts and compare them with benchmark examples to discover and maintain the best prompts. Chaining prompts and knowledge databases into complex APIs allows you to orchestrate and manage complex apps. Integrating your proprietary data with your models will improve their accuracy.

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.

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.

Klu.ai, a Generative AI Platform, simplifies the design, deployment, and optimization of AI applications. Klu integrates your Large Language Models and incorporates data from diverse sources to give your applications unique context. Klu accelerates the building of applications using language models such as Anthropic Claude (Azure OpenAI), GPT-4 (Google's GPT-4), and over 15 others. It allows rapid prompt/model experiments, data collection and user feedback and model fine tuning while cost-effectively optimising performance. Ship prompt generation, chat experiences and workflows in minutes. Klu offers SDKs for all capabilities and an API-first strategy to enable developer productivity. Klu automatically provides abstractions to common LLM/GenAI usage cases, such as: LLM connectors and vector storage, prompt templates, observability and evaluation/testing tools.

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.

Teuken-7B, a multilingual open source language model, was developed under the OpenGPT-X project. It is specifically designed to accommodate Europe's diverse linguistic landscape. It was trained on a dataset that included over 50% non-English text, covering all 24 official European Union languages, to ensure robust performance. Teuken-7B's custom multilingual tokenizer is a key innovation. It has been optimized for European languages and enhances training efficiency. The model comes in two versions: Teuken-7B Base, a pre-trained foundational model, and Teuken-7B Instruct, a model that has been tuned to better follow user prompts. Hugging Face makes both versions available, promoting transparency and cooperation within the AI community. The development of Teuken-7B demonstrates a commitment to create AI models that reflect Europe’s diversity.

LMOps is a stack that allows you to launch production-ready applications for monitoring, model management and more. Portkey is a replacement for OpenAI or any other provider APIs. Portkey allows you to manage engines, parameters and versions. Switch, upgrade, and test models with confidence. View aggregate metrics for your app and users to optimize usage and API costs Protect your user data from malicious attacks and accidental exposure. Receive proactive alerts if things go wrong. Test your models in real-world conditions and deploy the best performers. We have been building apps on top of LLM's APIs for over 2 1/2 years. While building a PoC only took a weekend, bringing it to production and managing it was a hassle! We built Portkey to help you successfully deploy large language models APIs into your applications. We're happy to help you, regardless of whether or not you try Portkey!

Lunary is a platform for AI developers that helps AI teams to manage, improve and protect chatbots based on Large Language Models (LLM). It includes features like conversation and feedback tracking as well as analytics on costs and performance. There are also debugging tools and a prompt directory to facilitate team collaboration and versioning. Lunary integrates with various LLMs, frameworks, and languages, including OpenAI, LangChain and JavaScript, and offers SDKs in Python and JavaScript. Guardrails to prevent malicious prompts or sensitive data leaks. Deploy Kubernetes/Docker in your VPC. Your team can judge the responses of your LLMs. Learn what languages your users speak. Experiment with LLM models and prompts. Search and filter everything in milliseconds. Receive notifications when agents do not perform as expected. Lunary's core technology is 100% open source. Start in minutes, whether you want to self-host or use the cloud.

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.

CodeGen is a model for program synthesis that is open-source. Trained on TPU v4. OpenAI Codex is competitive with TPU-v4.

The prompt engineering platform allows you to experiment with different prompt versions. You can also evaluate and compare prompts in a series of tests, optimize prompts by one click, share and more. Optimize your AI development workflow. Key features that help you identify and get the best prompts for production use cases. Evaluation allows for a side-by-side comparison between prompts in test cases. Import test cases from CSV and define custom metrics for evaluation. Automatic template and prompt optimization can improve LLM results. View and manage all versions of the prompt and create OpenAI Functions. You can access all your prompts programmatically. This includes observability and analytics. Calculate the cost, latency and effectiveness of each prompt. Parea can help you improve your prompt engineering workflow. Parea helps developers improve the performance of LLM apps by implementing rigorous testing and versioning.

Learn how to choose the right LLM based on your needs, and how you can optimize quality, speed and cost-efficiency. With a single API and standard API, you can access all LLMs from all providers. Set your own constraints for output speed, latency and cost. Define your own quality metric. Personalize your router for your requirements. Send your queries to the fastest providers based on the latest benchmark data for the region you are in, updated every 10 minutes. Unify's dedicated walkthrough will help you get started. Discover the features that you already have and our upcoming roadmap. Create a Unify Account to access all models supported by all providers using a single API Key. Our router balances output speed, quality, and cost according to user preferences. The quality of the output is predicted using a neural scoring system, which predicts each model's ability to respond to a given prompt.

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.

Monitor your LLMs during production and discover and correct hallucinations and errors related to accuracy and quality with LLM outputs. Check your outputs to see if they contain hallucinations, misinformation or other issues. Configurable for any LLM application. Segment data to analyze in depth your cost, accuracy and response times. To debug generation, you can search, sort and filter your inference calls and trace your queries, retrievals and responses. Explore your conversations to learn what your users feel and what they are saying. You can also find out which conversations were unsuccessful. Compare your performance metrics between different models and prompts. Our insights will guide you to the best model for each use case. Our evaluators analyze and improve the outputs by using your data, configurations and feedback.

Substrate is a platform for agentic AI. Elegant abstractions, high-performance components such as optimized models, vector databases, code interpreter and model router, as well as vector databases, code interpreter and model router. Substrate was designed to run multistep AI workloads. Substrate will run your task as fast as it can by connecting components. We analyze your workload in the form of a directed acyclic network and optimize it, for example merging nodes which can be run as a batch. Substrate's inference engine schedules your workflow graph automatically with optimized parallelism. This reduces the complexity of chaining several inference APIs. Substrate will parallelize your workload without any async programming. Just connect nodes to let Substrate do the work. Our infrastructure ensures that your entire workload runs on the same cluster and often on the same computer. You won't waste fractions of a sec per task on unnecessary data transport and cross-regional HTTP transport.

Pre-trained models of language have achieved state-of the-art results for various Natural Language Processing (NLP). GPT-3 has demonstrated that scaling up language models pre-trained can further exploit their immense potential. Recently, a framework named ERNIE 3.0 for pre-training large knowledge enhanced models was proposed. This framework trained a model that had 10 billion parameters. ERNIE 3.0 performed better than the current state-of-the art models on a variety of NLP tasks. In order to explore the performance of scaling up ERNIE 3.0, we train a hundred-billion-parameter model called ERNIE 3.0 Titan with up to 260 billion parameters on the PaddlePaddle platform. We also design a self supervised adversarial and a controllable model language loss to make ERNIE Titan generate credible texts.

Instruction-following models such as GPT-3.5 (text-DaVinci-003), ChatGPT, Claude, and Bing Chat have become increasingly powerful. These models are now used by many users, and some even for work. However, despite their widespread deployment, instruction-following models still have many deficiencies: they can generate false information, propagate social stereotypes, and produce toxic language. It is vital that the academic community engages in order to make maximum progress towards addressing these pressing issues. Unfortunately, doing research on instruction-following models in academia has been difficult, as there is no easily accessible model that comes close in capabilities to closed-source models such as OpenAI's text-DaVinci-003. We are releasing our findings about an instruction-following language model, dubbed Alpaca, which is fine-tuned from Meta's LLaMA 7B model.

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.

Information overload is a major barrier to scientific progress. The explosion of scientific literature and data makes it harder to find useful insights among a vast amount of information. Search engines are used to access scientific knowledge today, but they cannot organize it. Galactica is an extensive language model which can store, combine, and reason about scientific information. We train using a large corpus of scientific papers, reference material and knowledge bases, among other sources. We outperform other models in a variety of scientific tasks. Galactica performs better than the latest GPT-3 on technical knowledge probes like LaTeX Equations by 68.2% to 49.0%. Galactica is also good at reasoning. It outperforms Chinchilla in mathematical MMLU with a score between 41.3% and 35.7%. And PaLM 540B in MATH, with a score between 20.4% and 8.8%.

With just one line of code, you can deploy all major ML frameworks like Pytorch and Onnx. Do you not have your own models? Prebuilt models can be deployed to reduce latency and cost. You can fine-tune models for specific tasks to reduce latency and costs while increasing performance. It's easy to do and you don't have to worry about infrastructure. Integrate with the top ML observability platform to be alerted on feature or prediction drift, compare models versions, and resolve issues quickly. To resolve model performance problems, discover the root causes of prediction and feature drift. Find out which features contribute the most to your model's performance.

Akira AI provides the best explainability, accuracy and scalability in their application. Responsible AI can help you create applications that are transparent, robust, reliable, and fair. Transforming enterprise work with computer vision techniques, machine learning solutions and end-to-end deployment of models. ML model problems can be solved with actionable insights. Build AI systems that are compliant and responsible with proactive bias monitoring capabilities. Open the AI blackbox to optimize and understand the correct inner workings. Intelligent automation-enabled process reduce operational hindrances, and optimize workforce productivity. Build AI-quality AI solutions that optimize, monitor, and explain ML models. Improve performance, transparency and robustness. Model velocity can improve AI outcomes and model performance.

XLNet, a new unsupervised language representation method, is based on a novel generalized Permutation Language Modeling Objective. XLNet uses Transformer-XL as its backbone model. This model is excellent for language tasks that require long context. Overall, XLNet achieves state of the art (SOTA) results in various downstream language tasks, including question answering, natural languages inference, sentiment analysis and document ranking.

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

GPT-4, a large multimodal (accepting text and image inputs) model that can solve complex problems with greater accuracy thanks to its advanced reasoning abilities and broader general knowledge than any of our other models. GPT-4 can be found in the OpenAI API for paying customers. GPT-4, like gpt 3.5-turbo is optimized for chat, but also works well with traditional completion tasks using the Chat Completions API. Our GPT guide will teach you how to use GPT-4. GPT-4 is a newer GPT-4 model that features improved instruction following, JSON Mode, reproducible outputs and parallel function calls. Returns up to 4,096 tokens. This preview model has not yet been adapted for production traffic.

Falcon-40B is a 40B parameter causal decoder model, built by TII. It was trained on 1,000B tokens from RefinedWeb enhanced by curated corpora. It is available under the Apache 2.0 licence. Why use Falcon-40B Falcon-40B is the best open source model available. Falcon-40B outperforms LLaMA, StableLM, RedPajama, MPT, etc. OpenLLM Leaderboard. It has an architecture optimized for inference with FlashAttention, multiquery and multiquery. It is available under an Apache 2.0 license that allows commercial use without any restrictions or royalties. This is a raw model that should be finetuned to fit most uses. If you're looking for a model that can take generic instructions in chat format, we suggest Falcon-40B Instruct.