#Long-context LLMs

Uncover how LongRAG enhances Retrieval-Augmented Generation with long-context LLMs, integrating 'long retriever' and 'long reader' for comprehensive 4K-token analysis. Access comprehensive guides and leverage cutting-edge LLMs like Gemini-1.5-Pro and GPT-4o for superior data handling.

LLMLingua provides efficient tools for compressing prompts in large language models, enabling up to 20x compression with minimal performance degradation. It integrates seamlessly with frameworks such as LangChain and LlamaIndex, optimizing costs and enhancing retrieval-augmented generation performance. LLMLingua-2 further improves task-agnostic compression, offering 3x-6x speed boosts. The latest release includes MInference, which cuts inference latency by as much as 10x in long-context applications, contributing to advancements in AI prompt compression.

Achieve significantly faster processing for long-context language models through dynamic sparse attention. This method increases efficiency for models like LLaMA-3 and GLM-4, preserving accuracy for intricate language tasks. MInference is compatible with a broad range of models, offering adaptability in computational processes. Recognized at NeurIPS'24, and compatible with platforms like Hugging Face, MInference 1.0 presents modern advancements in AI processing, enhancing long-context LLM capabilities.