#benchmark performance

LLaVA-NeXT introduces significant advancements in multimodal models, particularly in video processing with the LLaVA-Video-178K dataset. This synthetic dataset significantly enhances video instruction tuning, comprising 178,510 captions and extensive Q&A pairs, alongside the LLaVA-Video 7B/72B models to improve video benchmarks. The project focuses on multi-image, video, and 3D task innovations and promotes thorough model evaluations and efficient task transfer techniques.

DeepSeek Coder, trained on 2 trillion tokens, excels in multilingual code modeling. Supporting over 80 programming languages, it performs well on benchmarks such as HumanEval, MBPP, and DS-1000. Available in models from 1B to 33B, it meets different computational requirements. It is designed for effective code completion and infilling, making it suitable for both project-level applications and educational purposes. This open-source language model's efficiency and scalability offer distinct advantages over CodeLlama and GPT-35-turbo.