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14 articles
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.
Learn how to use Microsoft's new Harrier-OSS-v1 multilingual embedding models to generate semantic embeddings and calculate similarity scores across multiple languages.
Learn to build an AI model evaluation framework that can compare different AI systems using standardized benchmarks, similar to how Anthropic tests Claude Mythos.
Learn how Meta's new AI model TRIBE v2 helps decode brain activity in response to videos, audio, and text, and why this breakthrough matters for neuroscience and AI.
Learn how to use Microsoft's MAI-Image-2 AI model to generate images programmatically using Python and Hugging Face Transformers.
Learn how to work with AI models using Python and the Hugging Face Transformers library. This beginner-friendly tutorial teaches you to load, use, and interact with AI models while understanding ethical considerations.
This article explains the trade-offs in AI language model performance, focusing on how models like Grok 4.20 reduce hallucinations but lag behind top-tier models in benchmarks.
Learn how to create your own image generation pipeline using Python and Hugging Face's Transformers library. This beginner-friendly tutorial teaches you to generate images from text prompts using pre-trained models similar to Luma AI's Uni-1.
Learn how to implement Chain-of-Thought prompting techniques using Hugging Face Transformers to guide language models toward more structured reasoning patterns, similar to OpenAI's CoT-Control research.