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Paper | Github | Dataset | Model
The main features of declare-lab/red-instruct are: Evaluation Benchmarks, Jailbreak Defenses.
Projects with overlapping indexed features include: zjunlp/easyedit — EasyEdit is a framework and toolkit designed for updating, inserting, or erasing specific factual information within… cyberalbsecop/awesome_gpt_super_prompting — This repository is a collection of specialized toolsets and libraries for large language model prompt engineering and… pyspur-dev/pyspur. datawhalechina/prompt-engineering-for-developers — This project is a technical curriculum and development guide focused on large language model prompt engineering,… albertwy/gpt-4v-evaluation — Data for evaluating GPT-4V. ai45lab/openrt — Open-source red teaming framework for MLLMs with 42+ attack methods.
EasyEdit is a framework and toolkit designed for updating, inserting, or erasing specific factual information within large language models without requiring full retraining. It functions as a parameter modifier and knowledge editing system capable of performing targeted weight updates across diverse model architectures. The project distinguishes itself by supporting both text-based and multimodal model editing, allowing for knowledge updates across image and text modalities. It provides utilities for model steering to adjust personality and reasoning patterns in real time via activation inter
This repository is a collection of specialized toolsets and libraries for large language model prompt engineering and security testing. It provides a library of advanced templates and frameworks designed to optimize the quality and specificity of model responses. The project includes resources for red teaming and security research, featuring a repository of prompts designed to bypass safety filters and operational constraints. It also provides techniques for system prompt extraction to reveal the internal instructions and configurations of AI personas. The collection covers a broader surface
This project is a technical curriculum and development guide focused on large language model prompt engineering, fine-tuning, and the creation of retrieval augmented generation applications. It serves as a comprehensive resource for developers to master crafting precise instructions and textual patterns to improve the quality and predictability of model outputs. The material covers the end-to-end workflow of adapting open-source models to specific datasets and integrating language models with vector databases to generate responses based on private information. It also provides a systematic ap
