GitHub - alibaba/data-juicer: A one-stop data processing system to make data higher-quality, juicier, and more digestible for LLMs! 🍎 🍋 🌽 ➡️ ➡️🍸 🍹 🍷为大语言模型提供更高质量、更丰富、更易”消化“的数据！
slowllama
Fine-tune Llama2 and CodeLLama models, including 70B/35B on Apple M1/M2 devices (for example, Macbook Air or Mac Mini) or consumer nVidia GPUs.
slowllama is not using any quantization. Instead, it offloads parts of model to SSD or main memory on both forward/backward passes. In contrast with training large models from scratch (unattainable... See more
Fine-tune Llama2 and CodeLLama models, including 70B/35B on Apple M1/M2 devices (for example, Macbook Air or Mac Mini) or consumer nVidia GPUs.
slowllama is not using any quantization. Instead, it offloads parts of model to SSD or main memory on both forward/backward passes. In contrast with training large models from scratch (unattainable... See more
okuvshynov • GitHub - okuvshynov/slowllama: Finetune llama2-70b and codellama on MacBook Air without quantization
LLM-PowerHouse: A Curated Guide for Large Language Models with Custom Training and Inferencing
Welcome to LLM-PowerHouse, your ultimate resource for unleashing the full potential of Large Language Models (LLMs) with custom training and inferencing. This GitHub repository is a comprehensive and curated guide designed to empower developers, researche... See more
Welcome to LLM-PowerHouse, your ultimate resource for unleashing the full potential of Large Language Models (LLMs) with custom training and inferencing. This GitHub repository is a comprehensive and curated guide designed to empower developers, researche... See more
ghimiresunil • GitHub - ghimiresunil/LLM-PowerHouse-A-Curated-Guide-for-Large-Language-Models-with-Custom-Training-and-Inferencing: LLM-PowerHouse: Unleash LLMs' potential through curated tutorials, best practices, and ready-to-use code for custom training and inferencing.
Welcome to RAGatouille
Easily use and train state of the art retrieval methods in any RAG pipeline. Designed for modularity and ease-of-use, backed by research.
The main motivation of RAGatouille is simple: bridging the gap between state-of-the-art research and alchemical RAG pipeline practices. RAG is complex, and there are many moving parts. To g... See more
Easily use and train state of the art retrieval methods in any RAG pipeline. Designed for modularity and ease-of-use, backed by research.
The main motivation of RAGatouille is simple: bridging the gap between state-of-the-art research and alchemical RAG pipeline practices. RAG is complex, and there are many moving parts. To g... See more
GitHub - bclavie/RAGatouille: Easily use and train state of the art late-interaction retrieval methods (ColBERT) in any RAG pipeline. Designed for modularity and ease-of-use, backed by research.
(1) The separation between storage and compute , as encouraged by data lake architectures (e.g. the implementation of P would look different in a traditional database like PostgreSQL, or a cloud warehouse like Snowflake). This architecture is the focus of the current system, and it is prevalent in most mid-to-large enterprises (its benefits that be... See more