Showing 3 open source projects for "runtime full"

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  • 1
    SWIFT LLM

    SWIFT LLM

    Use PEFT or Full-parameter to CPT/SFT/DPO/GRPO 600+ LLMs

    SWIFT LLM is a comprehensive framework developed within the ModelScope ecosystem for training, fine-tuning, evaluating, and deploying large language models and multimodal models. The platform provides a full machine learning pipeline that supports tasks ranging from model pre-training to reinforcement learning alignment techniques. It integrates with popular inference engines such as vLLM and LMDeploy to accelerate deployment and runtime performance. The framework also includes support for many modern training strategies, including preference learning methods and parameter-efficient fine-tuning techniques. ms-swift is designed to work with hundreds of language and multimodal models, providing a unified environment for experimentation and production deployment.
    Downloads: 5 This Week
    Last Update:
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  • 2
    SAG

    SAG

    SQL-Driven RAG Engine

    ...Documents are first decomposed into atomic semantic events, which are then represented using multidimensional natural language vectors. These vectors allow the system to identify relationships between concepts and construct a graph representation of knowledge at runtime. The architecture also includes a three-stage retrieval pipeline consisting of recall, expansion, and reranking steps to improve search accuracy. The engine integrates semantic vector similarity with traditional full-text search to improve both recall and precision. Because the knowledge graph is generated dynamically, the system can adapt to new information without requiring manual graph maintenance.
    Downloads: 4 This Week
    Last Update:
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  • 3
    Ludwig AI

    Ludwig AI

    Low-code framework for building custom LLMs, neural networks

    ...Declarative YAML configuration file is all you need to train a state-of-the-art LLM on your data. Support for multi-task and multi-modality learning. Comprehensive config validation detects invalid parameter combinations and prevents runtime failures. Automatic batch size selection, distributed training (DDP, DeepSpeed), parameter efficient fine-tuning (PEFT), 4-bit quantization (QLoRA), and larger-than-memory datasets. Retain full control of your models down to the activation functions. Support for hyperparameter optimization, explainability, and rich metric visualizations. ...
    Downloads: 0 This Week
    Last Update:
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