#大语言模型#Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. 🐳Docker-friendly.⚡Always in sync with Sharepoint, Google Drive, S3, Kafka, PostgreSQL, real-time data APIs, a...
#计算机科学#UpTrain is an open-source unified platform to evaluate and improve Generative AI applications. We provide grades for 20+ preconfigured checks (covering language, code, embedding use-cases), perform ro...
Context management for long-context LLMs, agents, and vibe coding. Instantly build context for an entire repo, selected files, folders, and GitHub issues to generate structured AI-XML context with rea...
LLM prompts, llama3 prompts, llama2 prompts
#大语言模型#It is a comprehensive resource hub compiling all LLM papers accepted at the International Conference on Learning Representations (ICLR) in 2024.
#大语言模型#Open Source LLM proxy that transparently captures and logs all interactions with LLM API
This project investigates the security of large language models by performing binary classification of a set of input prompts to discover malicious prompts. Several approaches have been analyzed using...
#大语言模型#Franklin is a LLM powered AI IRC chat bot
#大语言模型# 🔭 Threat report analysis via LLM and Vector DB
Prompt generator for LLM agents with interceptors
#大语言模型#Proofreading and editing prompts for ChatGPT, Copilot, Gemini, etc.
An application that transforms prompt engineering for LLMs by automating prompt generation, creating diverse test cases, and evaluating and ranking prompts.
#大语言模型#Evaluating LLMs with Multiple Problems at once: A New Paradigm for Probing LLM Capabilities
#大语言模型#Just released Visual-Prompt-Craft – A simple toolkit for visual prompting with CLIP & ViT
Just released Visual-Prompt-Craft – A simple toolkit for visual prompting with CLIP & ViT
I used prompt engineering with Claude.ai to create a QR Generator
Creating an AI chatbot that can take on different personas, keep track of conversation history, and provide coherent responses.
#自然语言处理#Various projects in Data Science
#大语言模型#A framework to move beyond simple prompting towards defining *how* the LLM should structure its internal processing, access its latent knowledge, and apply specific heuristics or constraints when deal...