#python #artificial_intelligence #attention_mechanism #deep_learning #multi_modal #text_to_image #transformers

https://github.com/lucidrains/DALLE-pytorch

#python #computer_vision #contrastive_loss #deep_learning #language_model #multi_modal_learning #pretrained_models #pytorch #zero_shot_classification

https://github.com/mlfoundations/open_clip

#python #cross_modal #data_structures #dataclass #deep_learning #docarray #elasticsearch #graphql #multi_modal #multimodal #nearest_neighbor_search #nested_data #neural_search #protobuf #qdrant #semantic_search #sqlite #unstructured_data #vector_search #weaviate

https://github.com/docarray/docarray

#python #cv #deep_learning #machine_learning #multi_modal #nlp #science #speech

https://github.com/modelscope/modelscope

#python #chatgpt #clip #deep_learning #gpt #hacktoberfest #hnsw #information_retrieval #knn #large_language_models #machine_learning #machinelearning #multi_modal #natural_language_processing #search_engine #semantic_search #tensor_search #transformers #vector_search #vision_language #visual_search

https://github.com/marqo-ai/marqo

#python #chinese #clip #computer_vision #contrastive_loss #coreml_models #deep_learning #image_text_retrieval #multi_modal #multi_modal_learning #nlp #pretrained_models #pytorch #transformers #vision_and_language_pre_training #vision_language

This project is about a Chinese version of the CLIP (Contrastive Language-Image Pretraining) model, trained on a large dataset of Chinese text and images. Here’s what you need to know This model helps you quickly perform tasks like calculating text and image features, cross-modal retrieval (finding images based on text or vice versa), and zero-shot image classification (classifying images without any labeled examples).
- **Ease of Use** The model has been tested on various datasets and shows strong performance in zero-shot image classification and cross-modal retrieval tasks.
- **Resources**: The project includes pre-trained models, training and testing codes, and detailed tutorials on how to use the model for different tasks.

Overall, this project makes it easy to work with Chinese text and images using advanced AI techniques, saving you time and effort.

https://github.com/OFA-Sys/Chinese-CLIP

#python #agents #ai #artificial_intelligence #attention_mechanism #chatgpt #gpt4 #gpt4all #huggingface #langchain #langchain_python #machine_learning #multi_modal_imaging #multi_modality #multimodal #prompt_engineering #prompt_toolkit #prompting #swarms #transformer_models #tree_of_thoughts

Swarms is an advanced multi-agent orchestration framework designed for enterprise-grade production use. Here are the key benefits and features Swarms offers production-ready infrastructure with high reliability, modular design, and comprehensive logging, reducing downtime and easing maintenance.
- **Agent Orchestration** Swarms allows multi-model support, custom agent creation, an extensive tool library, and multiple memory systems, providing flexibility and extended functionality.
- **Scalability** Swarms includes a simple API, extensive documentation, an active community, and CLI tools, making development faster and easier.
- **Security Features**//docs.swarms.world) for more detailed information.

https://github.com/kyegomez/swarms

#python #minicpm #minicpm_v #multi_modal

**MiniCPM-o 2.6** is a powerful multimodal model that can process images, videos, text, and audio, and provide high-quality outputs. Here are the key benefits It achieves comparable performance to GPT-4o-202405 in vision, speech, and multimodal live streaming, making it highly versatile.
- **Real-Time Speech Conversation** Outperforms proprietary models like GPT-4V and Claude 3.5 Sonnet in single image, multi-image, and video understanding.
- **Efficient Deployment** Can be used in various ways, including CPU inference with llama.cpp, quantized models, fine-tuning, and local WebUI demos.

This model enhances user experience by providing accurate and efficient multimodal interactions, making it a valuable tool for various applications.

https://github.com/OpenBMB/MiniCPM-o

#jupyter_notebook #ai #llm #llms #multi_modal #openai #python #rag

Retrieval-Augmented Generation (RAG) is a technique that helps improve the accuracy of large language models by fetching relevant information from databases or documents. This approach ensures that the model's responses are based on up-to-date and accurate data, reducing errors and "hallucinations" where the model might provide false information. For users, RAG offers more reliable and trustworthy responses, allowing them to verify the sources used to generate those responses. This method also saves resources by avoiding the need to retrain models with new data.

https://github.com/FareedKhan-dev/all-rag-techniques

#python #multi_modal_rag #retrieval_augmented_generation

RAG-Anything is a powerful AI system that helps you search and understand documents containing mixed content like text, images, tables, and math formulas all in one place. It uses smart parsing and analysis to break down complex documents and builds a knowledge graph to connect different types of information. This means you can ask detailed questions about any part of a document—whether text or images—and get clear, accurate answers quickly. It supports many file types like PDFs and Office files, making it ideal for research, technical work, or business reports where you need a unified, easy way to explore rich, multimodal content. This saves you time and effort by avoiding multiple tools and gives you deeper insights from your documents.

https://github.com/HKUDS/RAG-Anything