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MiniCPM-o is a multimodal large language model designed to function as a real-time conversational assistant on edge devices. By mapping text, image, video, and audio inputs into a unified latent space, the system enables simultaneous cross-modal reasoning and full-duplex interaction. It is built as an edge-side inference engine, utilizing quantized model weights to maintain high-performance processing on consumer hardware. The system distinguishes itself through its integrated speech synthesis and voice cloning capabilities, which allow for the generation of expressive, personalized vocal out
This is an educational curriculum for building and training neural networks using PyTorch. It serves as a deep learning training guide and resource, providing a structured series of lessons on tensor computation and architecture development. The course uses an interactive learning model that synchronizes academic theory with practice. It pairs theoretical lecture slides with exercise-driven notebooks, requiring students to implement model logic within predefined templates to validate their conceptual understanding. The curriculum covers a broad range of deep learning capabilities, including
This project is an educational course and learning curriculum for implementing and fine-tuning transformer models using the Hugging Face ecosystem. It serves as a structured guide and technical walkthrough for processing multimodal data, adapting pre-trained neural networks, and deploying models. The material includes a guide for managing, versioning, and distributing model weights and datasets through a centralized asset hub. It also provides a practical tutorial on adapting models to specific datasets using parameter-efficient methods and an implementation guide for solving natural language
DeepSeek-VL2 is a multimodal large language model and vision-language system designed to analyze visual scenes and generate descriptive text. It functions as a visual question answering and visual grounding model, capable of extracting information from documents and locating specific objects or regions within images based on textual descriptions. The project utilizes a mixture-of-experts architecture to process combined image and text inputs. It is optimized for inference through incremental prefilling, which reduces the GPU memory requirements on hardware. The model covers multimodal data a
✨✨[NeurIPS 2025] VITA-1.5: Towards GPT-4o Level Real-Time Vision and Speech Interaction
The main features of vita-mllm/vita are: Multimodal Architectures, Multimodal Foundation Models.
Projects with overlapping indexed features include: openbmb/minicpm-o — MiniCPM-o is a multimodal large language model designed to function as a real-time conversational assistant on edge… atcold/pytorch-deep-learning-minicourse — This is an educational curriculum for building and training neural networks using PyTorch. It serves as a deep… huggingface/course — This project is an educational course and learning curriculum for implementing and fine-tuning transformer models… deepseek-ai/deepseek-vl2 — DeepSeek-VL2 is a multimodal large language model and vision-language system designed to analyze visual scenes and… lxtgh/omg-seg — Official Repo For OMG-LLaVA and OMG-Seg codebase [CVPR-24 and NeurIPS-24]. haotian-liu/llava — LLaVA is a multimodal large language model architecture designed to process and interpret both image and text inputs…