Tiny LVLM-eHub: Early Multimodal Experiments with Bard paper page: huggingface.co/papers/2308.03… Recent advancements in Large Vision-Language Models (LVLMs) have demonstrated significant progress in tackling complex multimodal tasks. Among these cutting-edge developments, Google's

Thank AK AK for the post. 🔥 Excited to introduce OmniQuant - An advanced open-source algorithm for compressing large language models! 📜 Paper: arxiv.org/abs/2308.13137 🔗 Code: github.com/OpenGVLab/Omni… 💡 Key Features: 🚀Omnidirectional Calibration: Enables easier weight

ImageBind-LLM: Multi-modality Instruction Tuning paper page: huggingface.co/papers/2309.03… present ImageBind-LLM, a multi-modality instruction tuning method of large language models (LLMs) via ImageBind. Existing works mainly focus on language and image instruction tuning, different

🔥 Introducing #SPHINX 🦁: an all-in-one multimodal LLM with a unified interface that seamlessly integrates domains, tasks, & embeddings. 🧵N 👋 Explore the Gradio demo AK: imagebind-llm.opengvlab.com Dive into the open resources! 🤗 Model Hugging Face:

Generating ~200 million parameters in just minutes! 🥳 Excited to share our work with Doven Tang , ZHAO WANGBO , and Yang You: 'Recurrent Diffusion for Large-Scale Parameter Generation' (RPG for short). Example: Obtain customized models using prompts (see below). (🧵1/8)