This project implements TnT-LLM, a framework designed for large-scale text mining using Large Language Models (LLMs). TnT-LLM automates two core tasks: label taxonomy generation and text classification, both of which traditionally require significant manual effort and domain expertise.
Transform your ideas with structured AI-powered brainstorming methods Built with Next.js, TypeScript, and OpenAI's GPT models
RAGLight is a lightweight and modular Python library for implementing Retrieval-Augmented Generation (RAG). It enhances the capabilities of Large Language Models (LLMs) by combining document retrieval with natural language inference.
Feed it CVE-2025-29824. Get back a markdown report that names the buggy function, shows the pre/post-patch decompiled C, walks the trigger flow, and attaches a confidence score and per-call cost trace.
LLMs struggle when asked to generate or modify large JSON blobs. trustcall solves this by asking the LLM to generate JSON patch operations. This is a simpler task that can be done iteratively. This enables:
Principalele funcționalități ale hinthornw/trustcall sunt: Developer Toolkits.
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