
Hands-On RAG with AWS Bedrock and S3 Vector Store
Turn HR documents into a smart chatbot using Amazon S3 Vectors and Bedrock. Upload to
Turn HR documents into a smart chatbot using Amazon S3 Vectors and Bedrock. Upload to
Google’s LangExtract, a Gemini-powered Python library for extracting structured, grounded information.
Explore how Reinforcement Learning fine-tunes LLMs. This guide demystifies PPO, RLHF, RLAIF, DPO, and GRPO,
Discover the most influential AI research papers of 2024, featuring advancements like Mixtral, Byte Latent
The Byte Latent Transformer (BLT) eliminates tokenization, learning directly from raw bytes. Explore its dynamic
Attention-Based Distillation efficiently compresses large language models by aligning attention patterns between teacher and student.
Choosing between full fine-tuning and parameter-efficient tuning depends on your task’s complexity and available resources.
Master LLM fine-tuning with tools, techniques, and practical insights for domain-specific AI applications.
ModernBERT enhances BERT’s capabilities with longer context handling, optimized training techniques, and efficient inference.
LLaMA-Mesh bridges language and 3D design, enabling AI to generate 3D meshes from textual prompts.
Multi-Agent Reinforcement Learning (MARL) enables multiple agents to interact and optimize outcomes in dynamic environments.
Falcon 3 redefines AI with its optimized architecture, extended context handling, and quantized models for
Master the art of integrating and managing multiple AI models with Portkey. Explore its Universal
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