Prompt patterns is a framework for organizing AI-driven system design through structured prompt engineering and domain-driven development methodologies. It provides a library of standardized interaction strategies designed to improve the consistency, accuracy, and logical reasoning of large language model outputs. By applying these patterns, users can translate complex business scenarios into structured domain models and technical specifications. The project distinguishes itself by integrating domain-driven design principles directly into the prompting workflow. It utilizes techniques such as
VLM-R1 is a reasoning vision-language model and embodied AI framework designed to map visual inputs and language instructions into physical navigation waypoints and robotic actions. It functions as a multimodal policy optimizer and an open vocabulary detector capable of locating objects based on arbitrary natural language descriptions. The system distinguishes itself through the use of chain-of-thought reasoning and reinforcement learning to solve complex visual and spatial tasks. It utilizes a video semantic memory system, which employs a visual cache to maintain a history of live video for
This project serves as an educational resource and guide for prompt engineering, providing a structured methodology for interacting with large language models. It focuses on teaching core strategies to improve the reliability, accuracy, and consistency of model outputs across a variety of natural language processing tasks. The framework emphasizes the use of standardized templates and logical decomposition to manage complex instructions. By implementing techniques such as few-shot context injection, iterative refinement, and delimiter-based segmentation, the project demonstrates how to guide
This project is a private document analysis tool that enables conversational interaction with PDF files by executing all language model inference and processing entirely on the local machine. By running models directly within the browser or local environment, it ensures that sensitive user data remains offline and inaccessible to external servers or third-party cloud providers. The system utilizes retrieval augmented generation to provide context-aware answers, supported by local document text extraction and vector embedding indexing. This architecture allows for semantic search and informati
[Neurips'24 Spotlight] Visual CoT: Advancing Multi-Modal Language Models with a Comprehensive Dataset and Benchmark for Chain-of-Thought Reasoning
deepcs233/visual-cot 的主要功能包括：Chain Of Thought。
deepcs233/visual-cot 的开源替代品包括： phodal/prompt-patterns — Prompt patterns is a framework for organizing AI-driven system design through structured prompt engineering and… om-ai-lab/vlm-r1 — VLM-R1 is a reasoning vision-language model and embodied AI framework designed to map visual inputs and language… qwenlm/qwen2-vl — Qwen2-VL is a multimodal large language model and vision language model designed to process and reason across text,… pandabearlab/prompt-tutorial — This project serves as an educational resource and guide for prompt engineering, providing a structured methodology… jacoblee93/fully-local-pdf-chatbot — This project is a private document analysis tool that enables conversational interaction with PDF files by executing… dannyrose30/vcot.