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
Open-source red teaming framework for MLLMs with 42+ attack methods
ECCV 2024 BenchLMM: Benchmarking Cross-style Visual Capability of Large Multimodal Models
[ICML 2024] Fool Your (Vision and) Language Model With Embarrassingly Simple Permutations
The main features of ys-zong/foolyourvllms are: Evaluation Benchmarks.
Open-source alternatives to ys-zong/foolyourvllms include: pyspur-dev/pyspur. datawhalechina/prompt-engineering-for-developers — This project is a technical curriculum and development guide focused on large language model prompt engineering,… ai45lab/openrt — Open-source red teaming framework for MLLMs with 42+ attack methods. ailab-cvc/seed-bench — (CVPR2024)A benchmark for evaluating Multimodal LLMs using multiple-choice questions. albertwy/gpt-4v-evaluation — Data for evaluating GPT-4V. aifeg/benchlmm — [ECCV 2024] BenchLMM: Benchmarking Cross-style Visual Capability of Large Multimodal Models.
