AI Toolkit for VS Code January Update
AI Toolkit is a VS Code extension aiming to empower AI engineers in transforming their curiosity into advanced generative AI applications. This toolkit, featuring both local-enabled and cloud-accelerated inner loop capabilities, is set to ease model exploration, prompt engineering, and the creation and evaluation of generative applications. We are pleased to announce the January Update to the toolkit with support for OpenAI's o1 model and enhancements in the Model Playground and Bulk Run features. What's New? January’s update brings several exciting new features to boost your productivity in AI development. Here's a closer look at what's included: Support for OpenAI’s new o1 Model: We've added access to GitHub hosted OpenAI’s latest o1 model. This new model replaces the o1-preview and offers even better performance in handling complex tasks. You can start interacting with the o1 model within VS Code for free by using the latest AI Toolkit update. Chat History Support in Model Playground: We have heard your feedback that tracking past model interactions is crucial. The Model Playground has been updated to include support for chat history. This feature saves chat history as individual files stored entirely on your local machine, ensuring privacy and security. Bulk Run with Prompt Templating: The Bulk Run feature, introduced in the AI Toolkit December release, now supports prompt templating with variables. This allows users to create templates for prompts, insert variables, and run them in bulk. This enhancement simplifies the process of testing multiple scenarios and models. Stay tuned for more updates and enhancements as we continue to innovate and support your journey in AI development. Try out the AI Toolkit for Visual Studio Code, share your thoughts, and file issues and suggest features in our GitHub repo. Thank you for being a part of this journey with us!Bring your own models on AI Toolkit - using Ollama and API keys
As we have seen in past blog posts, AI toolkit supports a range of models using Github Marketplace of models. However, you might require support for external models hosted by Google, Anthropic and Open AI which are either not available in the Github catalog of models or might want to use the models served by Ollama. We will cover both of these scenarios in this blog post. OpenAI, Anthropic and Google Hosted models Once you click on the Model catalog window and selected the models hosted by Google, Anthropic and OpenAI, you should see the following models selected. Also, you can add your APIs keys to the model in the following way On the above models, you should click on the "Try in playground", just below the model name model card and you should be able to see the following dialog box on the top search bar of the VS Code window. Here I have clicked on the "Try it in playground" link for Anthropic Claude 3.5 Sonnet model Enter your API Key and you are good to go. Also, as you can see in the dropdown text, you can also edit or change the value later. Similarly, you can perform the same action for the Google and OpenAI hosted models. Once you have done this you are free to use these models in the playground and for using the other features of AI toolkit extension. Using models served by Ollama Several developers are also using Ollama to experiment and play with models using the command line. Ollama is an open-source AI tool that allows users to run large language models (LLMs) on their local systems. It's a valuable tool for industries that require data privacy, such as healthcare, finance, and government which might need locally hosted models. So, AI toolkit already supports some locally downloadable models such as those in the Phi-series by Microsoft or those by Mistral. Ollama supports a wider variety of models especially those from Meta's Llama series of LLMs and SLMs. The complete list of models currently supported by Ollama can be found at Ollama library. We will run ollama on windows and when you run ollama and see help command you get the following output. Once you have selected the model from the library, you can use the ollama pull or ollama run to download the model. The run command will download the model and then run it if it's not already downloaded. The pull command will just download the model from the repository. Since I want you show you a multimodal model that can be run locally, I will be go to the command line and download and run llama3.2-vision model. See the commands for the same below We can also list the models already downloaded. As you can see, I have downloaded and tried a bunch of models. It might take a bit of time to download the models based on the speed of your internet connection. As you can see some of the models are quite large downloads. Also, you might need to make sure you have enough RAM on your laptop or desktop to run the models. So now we have downloaded the models, and we have run the models. Now, let's see how we can access them in AI Toolkit for VSCode. Go to the My models window as seen below. Click on the '+' symbol as seen below in the screenshot. Once you click on this you will see a dropdown in search. Click on add an Ollama model You will now see the choice to either select a model from ollama library or a custom ollama endpoint. For the purposes of this tutorial, we will select models. Let's select Multimodal modal we downloaded earlier. We should see the models we had seen in ollama list command earlier. See below. Now we can select the checkbox alongside llama3.2-vision:latest and select okay. You should now see the model appear in the My Models window like below. You can now right click on it and start using the model by loading in the playground. Since this is a multimodal model, you can use it to generate text as below. The screenshot below shows the model loaded via ollama and at the same time you can see that it is has the clip symbol activated in the window. Since this is a multi-modal model, we can give it an image and ask it questions. Which is what we will do next. Next, now let's attach an image and ask it questions about this image. This might take a bit more time as it will need to analyze the image and answer any questions. Since it is a generative AI model it can give slightly different inputs when given the same image. I can also ask the models questions about the image, and it will use the information from the image - the objects shown, the relationship between the objects and its world knowledge from its training to answer the questions. For example, see the session below. So, as you can the AI toolkit can be a fantastic place to try out different models from various sources. Ollama is also a great tool to try pre-built models locally securely without sending your data to the cloud, which can make it suitable for air-gapped environments and data privacy sensitive industries like fintech, healthcare and government. You have greater control over the models and the environment and the data they run on. It is also possible to customize the models and then serve them via ollama. This helps you choose the best model for your AI application. Resources AI toolkit for VSCode - https://marketplace.visualstudio.com/items?itemName=ms-windows-ai-studio.windows-ai-studio AI toolkit for VSCode on Github - https://github.com/microsoft/vscode-ai-toolkit Ollama - https://github.com/ollama/ollama Ollama library - https://ollama.com/library Azure AI Discord - https://aka.ms/AzureAI/DiscordAI Toolkit for Visual Studio Code: October 2024 Update Highlights
The AI Toolkit’s October 2024 update revolutionizes Visual Studio Code with game-changing features for developers, researchers, and enthusiasts. Explore multi-model integration, including GitHub Models, ONNX, and Google Gemini, alongside custom model support. Dive into multi-modal capabilities for richer AI testing and seamless multi-platform compatibility across Windows, macOS, and Linux. Tailored for productivity, the enhanced Model Catalog simplifies choosing the best tools for your projects. Try it now and share feedback to shape the future of AI in VS Code!Getting Started - Generative AI with Phi-3-mini: A Guide to Inference and Deployment
Getting started with Microsoft Phi-3-mini - Inference Phi-3-mini models, Discover how Phi-3-mini, a new series of models from Microsoft, enables deployment of Large Language Models (LLMs) on edge devices and IoT devices. Learn how to use Semantic Kernel, Ollama/LlamaEdge, and ONNX Runtime to access and infer phi3-mini models, and explore the possibilities of generative AI in various application scenarios
