Generative AI

How to Build a Matryoshka-Optimized Sentence Embedding Model for Ultra-Fast Retrieval with 64-Dimension Truncation

How to Build a Matryoshka-Optimized Sentence Embedding Model for Ultra-Fast Retrieval with 64-Dimension Truncation

In this tutorial, we fine-tune the embedding model of Sentence-Transformers using Matryoshka Representation Learning so that the initial vector measurements…
How to Build a RAG Pipeline for Atomic-Agents with Scripted Schemas, Dynamic Content Injection, and Agent Integration

How to Build a RAG Pipeline for Atomic-Agents with Scripted Schemas, Dynamic Content Injection, and Agent Integration

In this tutorial, we build an advanced, end-to-end learning pipeline Atomic-Agents by combining typed agent interfaces, structured information, and a…
How to Design Tensor Pipelines for Deep Learning Using Einops with Perception, Attention, and Multimodal Models

How to Design Tensor Pipelines for Deep Learning Using Einops with Perception, Attention, and Multimodal Models

section("6) pack unpack") B, Cemb = 2, 128 class_token = torch.randn(B, 1, Cemb, device=device) image_tokens = torch.randn(B, 196, Cemb, device=device)…
How to Build a Privacy-Preserving Integrated Pipeline for Optimizing Large-Language Models with LoRA Using Flower and PEFT

How to Build a Privacy-Preserving Integrated Pipeline for Optimizing Large-Language Models with LoRA Using Flower and PEFT

!pip -q install -U "protobuf<5" "flwr[simulation]" transformers peft accelerate datasets sentencepiece import torch if torch.cuda.is_available(): !pip -q install -U bitsandbytes…
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