What are the principles we can use to build LLM-powered software that is actually good enough to put in the hands of production customers?
#大语言模型#[ICML'24 Spotlight] LLM Maybe LongLM: Self-Extend LLM Context Window Without Tuning
#大语言模型#A discovery and compression tool for your Python codebase. Creates a knowledge graph for a LLM context window, efficiently outlining your project | Code structure visualization | LLM Context Window Ef...
#大语言模型#A lightweight tool to optimize your C# project for LLM context windows by using a knowledge graph | Code structure visualization | Static analysis for AI | Large Language Model tooling | .NET ecosyste...
[ICLR 2025] Official code repository for "TULIP: Token-length Upgraded CLIP"
#大语言模型#A discovery and compression tool for your Java codebase. Creates a knowledge graph for a LLM context window, efficiently outlining your project #LLM #AI #Java #CodeAnalysis #ContextWindow #DeveloperTo...
Building Agents with LLM structured generation (BAML), MCP Tools, and 12-Factor Agents principles
#大语言模型#Information on LLM models, context window token limit, output token limit, pricing and more.
#大语言模型#A tool that analyzes your content to determine if you need a RAG pipeline or if modern language models can handle your text directly. It compares your content's token requirements against model contex...
#大语言模型#Tezeta is a Python package designed to optimize memory in chatbots and Language Model (LLM) requests using relevance-based vector embeddings. This, in essence, provides support for using much longer c...
#大语言模型#A visualization website for comparing LLMs' long context comprehension based on the FictionLiveBench benchmark.
#大语言模型#Contains a bunch of LLM functions that are useful in LLM application development.
A language model that generates text based on a given prompt.
an efficient, extensible LLM with integrated active self-summarization to simulate extended context windows