# Contextual Learning

VILA is a pre-trained visual language model (VLM) that achieves video and multi-image understanding capabilities through pre-training with large-scale interleaved image-text data. VILA can be deployed on edge devices using the AWQ 4bit quantization and TinyChat framework. Key advantages include: 1) Interleaved image-text data is crucial for performance enhancement; 2) Not freezing the large language model (LLM) during interleaved image-text pre-training promotes context learning; 3) Re-mixing text instruction data is critical for boosting VLM and plain text performance; 4) Token compression can expand the number of video frames. VILA demonstrates captivating capabilities including video inference, context learning, visual reasoning chains, and better world knowledge.

LMOps is a foundation research and technology for AI products based on LLMs and generative AI models. It provides functionalities such as automatic prompt optimization, Promptist, extensible prompts, general prompt retrieval, LLM retrieval, as well as fundamental features including structured prompting, extensible prompting, LLM accelerator, LLM customization, and contextual understanding learning. LMOps links include microsoft/unilm and microsoft/torchscale. It is applicable to various scenarios, such as text-to-image generation, long sequence prompt consumption, and prompt extension. LMOps is an open-source project under the MIT License.