
A Deep Dive into Federated Learning of LLMs
Federated Learning (FL) enables privacy-preserving training of Large Language Models (LLMs) across decentralized data sources,
Federated Learning (FL) enables privacy-preserving training of Large Language Models (LLMs) across decentralized data sources,
DeepSeek-Prover-V2 combines informal reasoning and formal proof steps to solve complex theorems , achieving top
Browser-Use is an open-source Python library that lets LLM-powered agents interact with websites via natural
The highest distinction in the data science profession. Not just earn a charter, but use it as a designation.
LightRAG simplifies and streamlines the development of retriever-agent-generator pipelines for LLM applications.
Discover the power of llama-agents: a comprehensive framework for creating, iterating, and deploying efficient multi-agent
RAVEN enhances vision-language models using multitask retrieval-augmented learning for efficient, sustainable AI.
NuMind’s NuExtract model for zero-shot or fine-tuned structured data extraction.
Deep Lake: an advanced lakehouse for efficient AI data storage and retrieval, perfect for RAG
Explore Microsoft’s Florence-2: Unifying vision and language tasks with prompt-based AI integration.
Compare and contrast between different vector databases and understand their utilities.
Discover Microsoft’s AutoGen Studio for easy multi-agent system development and deployment.
Discover Nvidia’s Nemotron-4 340B models, revolutionising synthetic data generation and LLM training challenges.
Using LlamaIndex and LlamaParse for RAG implementation by preparing Excel data for LLM applications.
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