IBM Developer

Introducing IBM Granite.Code A single, lightweight extension that is built to work with IBM's cutting-edge Granite Large Language Model (LLM) for code and is available to individual developers for free! IBM Granite.Code provides robust, context-aware AI support directly in the developer's environment to provide explanation, documentation, translation, and unit test generation Read our latest blog to learn about this powerful AI coding companion: https://ibm.co/3TizKlM

Start your Retrieval Augmented Generation (RAG) journey now with our newly launched RAG Cookbook: https://ibm.co/3Omktxv This Cookbook will help you harness latest tools, ranging from IBM’s watsonx.ai to open-source frameworks like LangChain and LlamaIndex. It contains an exhaustive list of best practices, considerations, and tips for building RAG solutions tailored to a variety of business applications. A comprehensive guide that has an end-to-end coverage of the entire RAG pipeline for architects, data engineers, developers, and 'hands on' technical folks with practical advice on implementing and optimizing RAG solutions for specific use cases. ----- #AI #RAG #developers

The world of AI is advancing at lightning speed, offering unprecedented opportunities for innovation in environmental applications. But let’s be real—working in this space isn’t without its challenges! From navigating geospatial data complexities to handling petabyte-scale datasets and identifying the most reliable environmental APIs, developers in this field tackle unique hurdles daily. In a recent blog post, we break down the Top 5 Challenges for Environmental Application Developers and explore how IBM Environmental Intelligence APIs can help you build climate-resilient solutions faster and more efficiently. Check out the full blog here: https://ibm.co/4eKBCMa Let’s build solutions that not only keeps pace with #AI but also protects our planet! #EnvironmentalTech #Sustainability

It is crucial yet tedious task to compare outputs from multiple LLMs for selecting a model that is tailored to your specific need. A model that maximize performance, minimize biases, and optimize costs. It can be tricky to choose between open-source models like Llama, Granite, and Mistral. To facilitate this process, Senior GenAI Engineer at IBM, Bhavishya Pandit, has authored this tutorial to provide solutions for a systematic evaluation and the code necessary to compare various LLMs, highlighting the benefits of efficient model selection and optimized workflows: https://ibm.co/4hs2seM #AI #LLM #OpenSource

Large Language Models (LLMs) have immense potential, but they come with challenges like the need for high-quality training data, specialized skills, and extensive computing resources. Forking and retraining these models can be time-consuming and costly. That’s where InstructLab steps in! In this tutorial, CSM Solutions Architect at IBM, Ahmed Azraq explains how to accomplish the same goal of fine-tuning open-source LLMs using InstructLab UI: https://ibm.co/4fmmR2V The InstructLab User Interface (UI) allows you to easily contribute knowledge or skills to the InstructLab taxonomy repository without worrying about YAML structure, the different validation rules, or the GitHub pull request (PR) process.

Explore this tutorial that explains the use of two approaches for migrating a NIM server to the latest AIX release using the virtual media library and the alt_disk_mksysb command. Based on the NIM resources and the infrastructure, you can choose any of the methods for migrating your NIM server. By following these procedures, you can smoothly migrate your NIM server while minimizing risks to your environment: https://ibm.co/4fmnVDP

Learn how to leverage environmental data in your applications through a structured learning path. Click on this link to start your learning journey now: https://ibm.co/48qiLVl

Discover IBM Environmental Intelligence that provides powerful APIs, geospatial data, and environmental insights to build innovative and sustainable applications. Learn how to optimize energy usage, assess carbon footprints, analyze wildfire risk, and predict climate impacts with cutting-edge tools for climate resilience and environmental risk management: https://ibm.co/4e5jnk8

Ever wondered what it’s like to have an AI assistant that writes code, debugs, and handles documentation—all locally on your machine? AI Engineer Kelly Abuelsaad shows you how to use a local open source AI code assistant to develop applications, powered by the Granite Code model. If you're looking to integrate AI into your development process while maintaining privacy, control, and open source compatibility, this guide shows you how to get started! https://lnkd.in/gRCSVvnx #AI #LLM #opensource

Granite models are open, state-of-the-art AI models that provide powerful performance, super safety, and top-notch security all in one family. IBM has announced the release of Granite 3.0, the 3rd-generation of Granite flagship models, designed as 'workhorse' models for generative AI, delivering strong performance for tasks such as RAG, classification, summarization, entity extraction, and tool use. The Granite models are released under the permissive Apache 2.0 license, making them unique in the combination of performance, flexibility, and autonomy they provide to enterprise clients and the community at large. Learn more about how you can get started right away with the IBM Granite models. #Granite #AI #LLM https://lnkd.in/gKBB43sg