ChatGLM-6B is an open-source dialogue model for text generation, built upon the General Language Model (GLM) framework. It comprises 6.2 billion parameters. Utilizing model distillation techniques, practical tests show it consumes approximately 6GB of VRAM during training on an RTX 2080Ti graphics card.

ChatGLM-6B is an open-source dialogue model for text generation, built upon the General Language Model (GLM) framework. It comprises 6.2 billion parameters. Utilizing model distillation techniques, practical tests show it consumes approximately 6GB of VRAM during training on an RTX 2080Ti graphics card. Advantages: Lower Deployment Barrier: In FP16 half-precision, ChatGLM-6B requires a minimum of 13GB VRAM for inference. Combined with model quantization technology, this requirement can be reduced to 10GB (INT8) & 6GB (INT4). This enables deployment of ChatGLM-6B on consumer-grade graphics cards.

Converting Markdown files to HTML files enables them to be easily displayed & shared on the Internet.

Vector Search can use Machine Learning (ML) to capture the meaning & context of unstructured data (including text,images) & convert it into a digital representation.It is widely used in semantic search applications, where the Approximate Nearest Neighbor (ANN) algorithm is employed to identify similar data points. Compared to traditional keyword-based search, vector search delivers more relevant results & achieves faster performance.

Gradio excels in its user-friendliness. Its code structure is notably simpler than Streamlit’s. By merely defining input & output interfaces, developers can swiftly create basic interactive web pages, simplifying the deployment of models. This makes it ideal for developers working on relatively straightforward projects who need to deploy applications rapidly. It’s also easy to share: when launching an application, setting the share=True parameter generates a public link, enabling direct sharing with users, even via platforms like WeChat. Furthermore, debugging is convenient because Gradio can render the interface directly within a Jupyter notebook, streamlining the debugging process.

In Flowable, all database tables are prefixed with ACT_. ACT_RE_*: The “RE” stands for Repository, & these tables store static data, such as process definitions, process resources (e.g., images, rule files), & other persistent information. ACT_RU_*: The “RU” stands for Runtime, & these tables store dynamic data that is only relevant during the execution of a process instance, such as process instances, user tasks, variables, & jobs. Flowable retains runtime data only while a process instance is active & automatically purges the records once the instance is completed. This design ensures that the runtime tables remain small & efficient.