🔥🔥🔥VITA: Towards Open-Source Interactive Omni Multimodal LLM [📽 VITA-1.5 Demo Show! Here We Go! 🔥] [📖 VITA-1.5 Paper (Comming Soon)] [🌟 GitHub] [🤗 Hugging Face] [🍎 VITA-1.0] [💬 WeChat (微信)] We are excited to introduce theVITA-1.5, a more powerful and...
Large language models (LLMs) are artificial intelligence (AI) tools specifically trained to process and generate text. LLMs attracted substantial public attention after OpenAI’s ChatGPT was made publicly available in November 2022. LLMs can often answer
Agent的“运动”可以被大致分类为感知,内部处理,行动三个部分。分别对应图中Perception,Brain,Action三个模块。Brain模块承担记忆、思考和决策等内在的任务;Perception模块负责感知和处理来自外部环境的多模态信息;Action模块负责使用工具执行任务并影响周围环境。三个模块也描述了一个agent自身状态的全部组成。 其机理分别于...
16. Besides the InstructGPT dataset, a dialog dataset, not clearly reported by OpenAI, was generated from conversations between human AI trainers and the chatbot, which was further fine-tuned to the state-of-the-art model, ChatGPT or formally GPT...
Full training updates all the parameters to finetune a language model. Here is an example to finetune a GPT-2 base model. cddata&&./download.sh alpaca&&cd- bash ./scripts/run_finetune.sh \ --model_name_or_path gpt2 \ --dataset_path data/alpaca/train_conversation \ --output_model_...
2.5 Action 在人类做出决策并采取行动后,他们控制自己的身体,比如手部,执行这些行动,将认知决策转化为与周围世界互动的物理动作。行动模块充当了LLMGAs的手,将语言描述的决策转化为游戏环境中可执行的动作,使Agent能够与其周围环境或游戏状态互动并改变之。不同的游戏需要Agent输出不同粒度的行动。对于需要操纵控制的游戏...
IUIPickerViewModel IUIPopoverBackgroundViewMethods IUIPopoverControllerDelegate IUIPopoverPresentationControllerDelegate IUIPreviewActionItem IUIPreviewInteractionDelegate IUIPrinterPickerControllerDelegate IUIPrintInteractionControllerDelegate IUIScrollViewAccessibilityDelegate IUIScrollViewDelegate IUISearchBarDelegate ...
Planning and architecture Planning and architecture Technical diagrams Site and solution planning Server farm and environment planning Server farm and environment planning System requirements Server farms and topologies Logical architecture planning Hosted environments Virtualization planning Authentication Security Per...
parser.add_argument("--langchain", action="store_true") args = parser.parse_args() prompt = args.prompt top_k = args.top_k top_p = args.top_p use_stream_chat = False temperature = args.temperature langchain = args.langchain
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