#fine-tune

H2O LLM Studio is a no-code platform for fine-tuning large language models using an intuitive GUI. It features cutting-edge techniques like Low-Rank Adaptation, supports multiple hyperparameters, and offers model performance tracking through Neptune and W&B integration. Recent enhancements provide robust training and optimization methods.

TencentPretrain is a comprehensive AI toolkit for pre-training and fine-tuning in text, vision, and audio modalities. Its modular architecture allows for flexible model configuration and scalability. With a diverse model zoo, it offers a range of pre-trained models suited for various tasks. Supporting CPU, single GPU, and distributed training, including DeepSpeed, it ensures superior performance in AI research and applications such as classification and reading comprehension.

Neural-Cherche is a versatile library designed to fine-tune and utilize neural search models like Splade, ColBERT, and SparseEmbed in both offline and online contexts. Compatible with CPU, GPU, and MPS devices, it ensures seamless performance while storing computed embeddings to minimize redundancy. Its straightforward integration with existing data workflows offers a customized search experience. Comprehensive documentation facilitates deployment and training, making it an efficient choice for neural search projects.

DeepSeekMoE 16B, utilizing a Mixture-of-Experts architecture, enhances computational efficiency with a reduction to 40% of operations. Matching the performance of models like LLaMA2 7B, its Base and Chat versions support English and Chinese, enabling deployment on a single GPU without quantization. Available under specific licensing for research and commercial applications.