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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
This is the official repository of our paper ScanQA: 3D Question Answering for Spatial Scene Understanding (CVPR 2022) by Daichi Azuma, Taiki Miyanishi, Shuhei Kurita, and Motoki Kawanabe. We propose a new 3D spatial understanding task for 3D question answering (3D-QA). In the 3D-QA task, models…
The main features of atr-dbi/scanqa are: Evaluation Benchmarks.
Projects with overlapping indexed features 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.
