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
We introduce the task of dense captioning in 3D scans from commodity RGB-D sensors. As input, we assume a point cloud of a 3D scene; the expected output is the bounding boxes along with the descriptions for the underlying objects. To address the 3D object detection and description problems, we…
daveredrum/scan2cap की मुख्य विशेषताएं हैं: Evaluation Benchmarks।
daveredrum/scan2cap के ओपन-सोर्स विकल्पों में शामिल हैं: 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.
