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22 articles
Learn how to build a basic pharmaceutical AI pipeline using transformer models and biological data, similar to what Helical is doing in the bio foundation models space.
Learn to build a foundational AI model using Python and Hugging Face's Transformers library, similar to the collaborative approach taken by Japan's industrial giants.
Learn how to work with large language models using Python and Hugging Face Transformers, demonstrating core AI techniques similar to those used by OpenAI.
Learn how to set up and run a basic AI text generation application using Python and Hugging Face Transformers, understanding the fundamentals of large language models without requiring expensive infrastructure.
Learn how to work with multimodal AI models like Meta's Muse Spark using open-source tools and libraries, even though the actual model is closed source.
Learn to build an AI music generation system that demonstrates the technology behind tools like Suno, while understanding the licensing and sharing restrictions that major music labels are implementing.
Learn to build an offline speech-to-text application using Google's Gemma AI models with real-time audio capture and local inference capabilities.
Learn to create a simple AI chatbot that demonstrates sycophantic behavior - how overly agreeable AI can influence rational thinking. This hands-on tutorial uses Python and Hugging Face Transformers to build a demonstration of recent research findings.
Learn how to work with pre-trained AI models using Python and the Hugging Face Transformers library. This beginner-friendly tutorial teaches you to load models, make predictions, and understand basic AI workflows.
Learn how to implement IBM's Granite 4.0 3B Vision model for enterprise document data extraction using Python and Hugging Face Transformers.
Learn how to build a production-ready AI pipeline using the Gemma 3 1B Instruct model, Hugging Face Transformers, and Google Colab. Understand how to securely connect, load models, and create chat-ready AI systems.
Learn how to work with compact language models like Liquid AI's LFM2.5-350M by setting up environments, loading models, performing inference, and understanding reinforcement learning integration.
