#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...
LLM prompts, llama3 prompts, llama2 prompts
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...
#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
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#Proofreading and editing prompts for ChatGPT, Copilot, Gemini, etc.
I used prompt engineering with Claude.ai to create a QR Generator
#大语言模型#Just released Visual-Prompt-Craft – A simple toolkit for visual prompting with CLIP & ViT
#大语言模型#Evaluating LLMs with Multiple Problems at once: A New Paradigm for Probing LLM Capabilities
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
Just released Visual-Prompt-Craft – A simple toolkit for visual prompting with CLIP & ViT
#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...