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On Fri, 2 Aug, 12:06 AM UTC
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GitHub introduces AI model playground for developers to test and compare LLMs - SiliconANGLE
At launch, GitHub Models will provide access to popular models, big and small, including Meta Platforms Inc.'s Llama 3.1, OpenAI's GPT-4o and GPT-4o mini, Cohere Inc.'s Command and Mistral AI's Mistral Large 2. Developers will be able to deploy models via a built-in playground, test different prompts and model parameters and launch them into developer environments including GitHub Codespaces or Visual Studio Code. "Increasingly, developers are building generative AI applications where the full stack contains backend and frontend code plus one or more models," said GitHub Chief Executive Thomas Dohmke. "But a vast segment of developers still lack easy access to open and closed models. This changes today." For many developers, learning to code doesn't happen just in the classroom, especially with rapidly evolving technologies - such as AI. It happens right in front of the computer screen with fingers on the keyboard trying to disentangle documentation and with the models themselves on a local machine or in the cloud. Dohmke said that Models will bring AI large language models directly to developers, students, hobbyists, engineers and startups via an interactive playground and others via a few clicks and keystrokes. This will allow users to experiment, compare, test and deploy AI applications with ease right where they already manage their source code. For example, a developer might be interested in discovering how a particular model might work with a new AI application they want to launch but they don't know which model they want to use. Using Models, they can spin up GPT-4o to make use of its multimodal capabilities for ingesting audio, video and text in real time and compare its performance and cost per token to Mistral. This also enables developers to experiment with different integration techniques and modes such as retrieval-augmented generation, which uses real-time trusted data to augment prompts to increase the accuracy of AI responses, or to test the efficacy of guardrails and other adjustments before the next phase of development. All that can be done without ever needing to load up a model on a developer's local machine, the company said. The local machine might not be able to handle larger models, or in the cloud where it would cost them money to experiment, but with the power of GitHub Codespaces, a secure cloud-based coding environment, developers can experiment with model inference before dropping it into a project. By applying sample code chosen from a variety of different languages and frameworks, developers can be ready to go by the time they're prepared to deploy. "Now with GitHub Models, more than 100 million developers can access and experiment with new AI models where they already manage their source code, issues, pull requests, workflows and repositories -- directly on GitHub," Dohmke added. Finally, GitHub said, the process of deploying to the cloud is simple for developers and engineers. Taking a model from development in GitHub to Azure AI means just switching out a personal access token with an Azure subscription and credential. "As an AI startup founder and open-source maintainer, GitHub Models enables my team to access and experiment with various LLMs in one place," said FirstQuadrant co-founder Anand Chowdhary. "This streamlines our development and lowers the entry barrier for building AI apps."
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Introducing GitHub Models: A new generation of AI engineers on GitHub
Every developer can be an AI engineer with the right tools and training. From playground to coding with the model in Codespaces to production deployment via Azure, GitHub Models shows developers how simple it can be GitHub, the world's leading AI-powered developer platform, today announced the launch of GitHub Models, enabling more than 100 million developers to become AI engineers and build with industry-leading AI models. From Llama 3.1, to GPT-4o and GPT-4o mini, to Phi 3 or Mistral Large 2, developers can access each model via a built-in playground that lets them test different prompts and model parameters, for free right in GitHub. In the new interactive model playground, students, hobbyists, startups, and more can explore the most popular private and open models from Meta, Mistral, Azure OpenAI Service, Microsoft, and others with just a few clicks and keystrokes. Developers can experiment, compare, test, and deploy AI applications right where they manage their source code. In alignment with GitHub and Microsoft's continued commitment to privacy and security, no prompts or outputs in GitHub Models will be shared with model providers, nor used to train or improve the models. "Today, we democratise AI for the many. With GitHub Models, more than 100 million developers can now access and experiment with new AI models where their workflow is -- directly on GitHub," said GitHub CEO, Thomas Dohmke."This means that every developer in India on GitHub can become an AI engineer, generating a new wave of AI applications that stand to accelerate India's competitive advantage in the age of AI. "In the years ahead, we will continue to democratise access to AI technologies to generate a groundswell of one billion developers. By doing so, we will enable 10% of the world's population to build and advance breakthroughs that will accelerate human progress for us all," added Dohmke. GitHub has also created a glide path to bring the models to a developer's environment in Codespaces and VS Code. Once a developer is ready to go to production, Azure AI offers built-in responsible AI, enterprise-grade security & data privacy, and global availability, with provisioned throughput and availability in over 25 Azure regions for some models. Test and compare different models Every piece of software is unique. And likewise, every model is unique in its capabilities, performance, and cost. Mistral offers low latency, while GPT-4o is excellent at building multimodal applications that might demand audio, vision, and text in real time. Some advanced scenarios might require the integration of different modes, such as an embeddings model for Retrieval Augmented Generation (RAG). With the suite of models, developers will have all the options they need to stay in the flow, experiment more, and learn faster than ever before. And this is just the first wave. In the months ahead, as the general availability of GitHub Models approaches, GitHub will continue to add more language, vision, and other models to the platform. Spin up Codespaces to bring ideas to life With the power of Codespaces, GitHub has created a zero-friction path for users to experiment with the model inference code before dropping it into the project. With sample code for a variety of languages and frameworks of all types ready to go, developers can try out various scenarios without ever hitting "works on my machine" problems. Then, once a developer is ready, it's a breeze to get things running in the project. They can use the knowledge gained from the playground and Codespaces to set up a prototype or proof-of-concept within their own applications. Run prompt evals in GitHub Actions with a series of JSON files that users just pipe in the GitHub Models command within the GitHub CLI. Or they can leverage GitHub Models to build a GitHub Copilot Extension, extending GitHub's platform ecosystem for every stage of software development. And finally, developers go to production with Azure AI by replacing their GitHub personal access token with an Azure subscription and credential. "As an AI startup founder and open source maintainer, GitHub Models enables my team to access and experiment with various LLMs in one place. This streamlines our development and lowers the entry barrier for building AI apps," said Anand Chowdhary, Co-founder of FirstQuadrant. The creator network for the age of AI From the creation of AI through open source collaboration, to the creation of software with the power of AI, to enabling the rise of the AI engineer with GitHub Models-GitHub is the creator network for the age of AI. The path to artificial general intelligence (AGI) will not be built without the source code and collaboration of the interconnected community on GitHub. GitHub Copilot is foundationally changing the speed of software production, already writing nearly 50% of code in files where it's enabled. With GitHub Copilot Workspace, GitHub envisions a world where millions of novice, hobbyist, and professional developers alike can code with entirely human language. And now with GitHub Models, more than 100 million developers can access and experiment with new AI models where they already manage their source code, issues, pull requests, workflows, and repositories -- directly on GitHub. Today, GitHub begins the limited public beta for GitHub Models. Developers can sign up now here. About GitHub As the global home for all developers, GitHub is the world's leading AI-powered developer platform to build, scale, and deliver secure software. Over 100 million people, including developers from 90 of the Fortune 100 companies, use GitHub to build amazing things together across 420+ million repositories. With all the collaborative features of GitHub, it's never been easier for individuals and teams to write faster, better code.
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GitHub Models gives developers new power to experiment with Gen AI
Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More GitHub is no stranger to the world of AI for development, but to date it hasn't been as easy as it could be for developers to try out new gen AI models. That's starting to change today. GitHub is launching a new effort called GitHub Models in a bid to provide an easier onramp for enterprise developers to try out and build applications with gen AI. GitHub is an early pioneer in the use of gen AI, particularly with its GitHub Copilot service. With GitHub Copilot developers get code completion and suggestion capabilities to build applications. GitHub Copilot is currently powered by a single model that GitHub has carefully curated and evaluated. GitHub Models, on the other hand, is a new initiative that provides developers with direct access to a wider range of AI models including Meta's Llama 3.1, OpenAI's GPT-4o, Mistral Large 2, AI21's Jamba-Instruct, Microsoft Phi-3 as well as models from Cohere. The goal with the new service is to allow developers to experiment with and integrate gen AI models into their own applications, beyond just code completion. "Every single app that is probably going to be created in the coming months and years is going to have intelligence attached to it as well," Mario Rodriguez, senior vice-president of product at GitHub told VentureBeat. "It's no longer enough for you to have an application, you're going to have to have an application that is powered by intelligence." Reducing AI friction for developers A key focus of the GitHub models initiative is to reduce the friction developers face when trying to experiment with and integrate AI models into their applications. Rodriguez noted that previously developers had to jump between a lot of sites and create multiple accounts just to play with different models. Rodriguez said that for GitHub's users it was previously impossible to easily explore and access a broad array of gen AI models, using just a GitHub identity. For developers that use GitHub, the identity provides access to an array of capabilities and makes it easier to develop code. "We just wanted to make it extremely simple, you know, AI is not a fad, it's here to stay," Rodriguez said. "So we just have to get that friction to be zero, if we want to continue to have that market grow." The GitHub Models initiative aims to reduce AI friction for developers by providing a centralized catalog of AI models that developers can access and experiment with directly within the GitHub platform, using their existing GitHub identity. GitHub Models provides a developer path to enterprise AI deployment While reducing friction to help developers try out and experiment with gen AI models is a core goal of GitHub Models, it's not the only one. GitHub is also providing a path for its users to easily move from experimentation to production deployment of AI-powered applications. That path leads to Microsoft's Azure. GitHub is of course, part of Microsoft as well so it's not surprising that's the direction. The way it works is users will first experiment with the AI models in the GitHub Models playground to evaluate their capabilities and performance. From there, a developer would transition to a GitHub Codespace or VS code developer environment and access an Azure SDK (software development kit) to obtain the necessary tokens and API keys to connect to the Azure platform. Experimentation is the key to overcoming enterprise AI challenges The path to enterprise AI deployment is also about overcoming challenges. Rodriguez identified three key challenges that developers face when working with AI models: latency, quality of responses and cost. Part of the goal with GitHub Models is to help developers navigate these challenges by providing an environment for testing and comparison. While industry benchmarks for various gen AI models are useful, Rodriguez noted that they do not tell the full story. "You really have to rely on your offline evaluation and online evaluation to make the best decision," he said.
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GitHub Thinks It's Hugging Face
With GitHub Models, over 100 million developers can now access and experiment with top AI models where their workflow is - directly on GitHub. Microsoft's GitHub is on a roll. The company recently announced the launch of GitHub Models which will offer developers access to leading LLMs, including Llama 3.1, GPT-4o, GPT-4o Mini, Phi 3, and Mistral Large 2. "You can access each model via a built-in playground that lets you test different prompts and model parameters, for free, right in GitHub," the company said in its blog post. "GitHub Models marks another transformational journey of GitHub. From the creation of AI through open source collaboration, to the creation of software with the power of AI, to enabling the rise of the AI engineer with GitHub Models - GitHub is the creator network for the age of AI," said Github chief Thomas Dohmke. With GitHub Models, the platform seeks to be more than just an AI pair programmer reliant on OpenAI's models. Developers now have access to the latest LLMs and can experiment to find the one that best suits their needs. Just as 'natural language' has become a prominent programming language, GitHub is positioning LLMs as the go-to framework to develop new software and products. With GitHub Models, over 100 million developers can now access and experiment with top AI models where their workflow is - directly on GitHub. This will allow developers to build AI applications whereever they manage their code. "With GitHub Models, developers can now explore these models on GitHub, integrate them into their dev environment in Codespaces and VS Code, and leverage them during CI/CD in Actions - all simply with their GitHub account and free entitlements," explained Dohmke. GitHub is high on confidence as its financial performance has been equally impressive. During Microsoft's recent earnings call, chief Satya Nadella said, "Copilot is driving GitHub growth overall. GitHub's annual revenue run rate is now $2 billion." He further added, "Copilot accounted for over 40% of GitHub's revenue growth this year and is already a larger business than GitHub was when we acquired it." The AI-powered coding assistant now has 1.3 million paid subscribers, marking a 30% increase quarter-over-quarter, according to Nadella. Meanwhile, GitHub Copilot Business has secured 50,000 enterprise customers across various industries. This year, Accenture plans to deploy the tool to 50,000 developers. Other notable enterprise customers include Goldman Sachs, Etsy, and Dell Technologies, Nadella said. Github Models seems to be inspired by Hugging Face. Hugging Face also provides the ability to test out different models. It offers Git-based code repositories, pre-trained models for NLP, computer vision and audio tasks, datasets for translation and speech recognition, and spaces for small-scale demos of machine learning applications. Recently, NVIDIA developed a playground called NVIDIA NIM, which hosts several open-source models covering reasoning, vision, retrieval, biology, and more. LMSYS Chat and Groq also offer playgrounds to try out different LLMs but none of them offer the capability to write code and build apps. Hugging Face recently partnered with NVIDIA to offer inference as a service. These new capabilities will enable developers to rapidly deploy leading LLMs, such as the Llama 3 family and Mistral AI models, with optimisation from NVIDIA NIM microservices running on NVIDIA DGX Cloud. Supposedly, GitHub Models also looks very familiar to Azure AI Studio, with Microsoft bringing it under GitHub so that developers who are active on GitHub can experiment with it. "Seems like this is a sales funnel for Azure's OpenAI/LLM gateway with GitHub as a proxy(?). It's a bit unclear. Regardless, I'd be pretty wary of adding either Azure or GitHub as a core dependency to any of my apps at this point with how poor uptime seems to be at both lately," posted one user on Hacker News. Even if it is true, this definitely seems to be an attempt by Microsoft to make GitHub a one-stop solution for developers to build AI applications. Dohmke quipped, "The path to AGI will not be built without the source code and collaboration of the interconnected community on GitHub." However, during his recent visit to India, he expressed different views on AGI. "I think everybody has a different understanding of what AGI even means and what the G is. I think today I see no sign that machine learning models, or large language models, have sentience. They are not creative. They are machines created by us that help us with the things that we want to do," he said in an interaction with AIM. Moreover, he believes that GitHub will enable 100 million developers to become AI engineers and AI won't take away the jobs of developers. "In many ways this new age of AI has actually created more demand for developers because now somebody also has to build all the AI systems. "When you embark on a new job, whether fresh out of college or transitioning from another company, the primary challenge is understanding the company's operations and code bases, which could be thousands of files," he said. A tool like Copilot could be very useful for entry-level coders. Moreover, Dohmke believes that going forward, applicants will be expected to be adept at using AI tools like Copilot and ChatGPT. "Some software companies have even begun incorporating Copilot into their interview processes, replacing traditional coding exercises with tasks that assess applicants' ability to utilise these tools effectively," Dohmke said.
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Microsoft's GitHub launches playground for AI models
Microsoft's developer platform GitHub has announced GitHub Models, an interactive sandbox environment for developers and engineers to explore different AI models for free. Leading models like Meta AI's Llama 3.1, OpenAI's GPT-4o and GPT-4o mini, Mistral AI's Mistral Large 2 and Cohere's Command will be among the options available. The playground's beta version has been made available to a limited group of people. Developers can use GitHub Models to deploy AI models via the built-in playground, test different prompts and model parameters and then launch them into environments like GitHub Codespaces and Visual Studio Code. GitHub Copilot launches premium enterprise AI coding assistant GitHub CEO Thomas Dohmke believes that the democratic tool will have a huge impact on its userbase in India in a way that "every developer in India on GitHub can become an AI engineer, generating a new wave of AI applications that stand to accelerate India's competitive advantage in the age of AI." Much like platforms like Hugging Face, GitHub Models also helps developers export and integrate their code into existing workflows making it even easier for them. (For top technology news of the day, subscribe to our tech newsletter Today's Cache) Model playgrounds have also been on the offering from cloud providers including Microsoft Azure and even OpenAI. However, Azure's playground can only be used by their customers who will have to complete a pre-defined workflow specific to the cloud computing platform before signing up. On the other hand, GitHub's models can be used immediately. Users can then switch to Azure. Developers who are interested in using GitHub Models can sign up to a waitlist. Read Comments
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GitHub introduces a new AI Model Playground, allowing developers to experiment with and compare various large language models. This move positions GitHub as a potential competitor to Hugging Face in the AI development space.
GitHub, the Microsoft-owned platform for software development, has unveiled its latest innovation: an AI Model Playground. This new feature allows developers to test, compare, and experiment with various large language models (LLMs) directly within the GitHub ecosystem 1. The playground is designed to streamline the process of integrating AI capabilities into software projects, marking a significant step in GitHub's evolution as a comprehensive platform for AI-driven development.
The AI Model Playground offers a range of functionalities tailored to meet the needs of modern developers:
GitHub's introduction of the AI Model Playground positions the platform as a potential competitor to established AI development platforms like Hugging Face 4. This move reflects the growing importance of AI in software development and GitHub's ambition to become a one-stop-shop for developers working with AI technologies.
The launch of the AI Model Playground has generated significant interest within the developer community. Experts believe this feature could democratize access to advanced AI tools, enabling a wider range of developers to incorporate AI into their projects 5.
As AI continues to reshape the software development landscape, GitHub's latest offering is expected to play a crucial role in fostering innovation and collaboration in the field of AI-driven software engineering.
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