#pre-training

Firefly is a versatile tool for training large models, offering pre-training, instruction fine-tuning, and DPO functionality for a broad range of popular models, including Llama3 and Vicuna. It employs methodologies such as full parameter tuning, LoRA, and QLoRA for efficient resource usage, catering to users with limited computing power. Its user-friendly approach allows for straightforward model training with optimized configurations to minimize memory and time consumption. Discover open-source model weights and benefit from proven methods, achieving notable improvements in the Open LLM Leaderboard.

The repository showcases recent advancements in multi-modality learning by Microsoft's MSM group, emphasizing pre-training techniques. It features comprehensive video-language datasets and models, including HD-VILA, LF-VILA, and CLIP-ViP, as well as image-language models like Pixel-BERT and VisualParsing. In 2023, the CLIP-ViP model was accepted at ICLR, and LF-VILA was featured at NeurIPS 2022. Community contributions are welcomed under Microsoft's Open Source Code of Conduct.

UER-py is a toolkit for pre-training and fine-tuning NLP models on general-domain corpora and downstream tasks. It features a modular architecture supporting models such as BERT and GPT-2, facilitating the extension and utilization of pre-trained models from its model zoo. Achieving high performance in tasks like classification and reading comprehension, UER-py is compatible with CPU and multi-GPU systems, offering comprehensive functions for researchers to explore and optimize advanced models.

LucaOne is an advanced model harmonizing nucleic acid and protein languages, streamlining bioinformatics tasks. It supports genomic and proteomic research through efficient embedding solutions and data integration, with capabilities for continuous training and refined inference methods. The platform accommodates extensive downstream tasks and offers comprehensive solutions available through GitHub and FTP.

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.

This collection offers detailed interview notes for Large Language Models (LLMs) derived from expert experiences. It includes foundational to advanced preparation, addressing frequent interview questions, model structures, and training goals. The guide provides strategies for managing issues such as repetitive outputs and model choice in different fields, as well as insights on distributed training, efficient tuning, and inference. It serves as a practical resource for understanding LLMs in professional interviews without excessive embellishment.

The LaVIT repository enhances language models by merging visual comprehension and generation into a cohesive framework. Highlighted at ICLR 2024, it utilizes visual tokenization to transform imagery into digestible data tokens, thus optimizing multimodal interaction. Video-LaVIT extends this capability to handle video content, providing reliable text-to-visual and video translation for diverse AI applications. The release of pre-trained weights on HuggingFace broadens its utility in tasks such as captioning and Q&A, supporting comprehensive multimodal operations within an integrated platform.