
Mastering Data Compression with LLMs via LMCompress
LMCompress uses large language models to achieve state of the art, lossless compression across text,
LMCompress uses large language models to achieve state of the art, lossless compression across text,
AlphaEvolve by DeepMind evolves and optimizes code using LLMs and evolutionary algorithms, enabling breakthroughs in
J1 by Meta AI is a reasoning-focused LLM judge trained with synthetic data and verifiable
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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