Showing 9 open source projects for "algorithms framework"

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  • 1
    H2O LLM Studio

    H2O LLM Studio

    Framework and no-code GUI for fine-tuning LLMs

    Welcome to H2O LLM Studio, a framework and no-code GUI designed for fine-tuning state-of-the-art large language models (LLMs). You can also use H2O LLM Studio with the command line interface (CLI) and specify the configuration file that contains all the experiment parameters. To finetune using H2O LLM Studio with CLI, activate the pipenv environment by running make shell. With H2O LLM Studio, training your large language model is easy and intuitive. First, upload your dataset and then start...
    Downloads: 4 This Week
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  • 2
    Youtu-GraphRAG

    Youtu-GraphRAG

    Vertically Unified Agents for Graph Retrieval-Augmented Reasoning

    ...These structures allow the system to perform multi-hop reasoning by decomposing complex questions into smaller queries that can be executed across different parts of the graph. The framework also incorporates hierarchical community detection algorithms that organize knowledge into clusters, improving both retrieval efficiency and reasoning performance. In addition to graph construction and retrieval, the system integrates iterative reasoning techniques that refine answers through multiple retrieval and reasoning cycles.
    Downloads: 1 This Week
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  • 3
    slime LLM

    slime LLM

    slime is an LLM post-training framework for RL Scaling

    ...The framework is designed to support a wide range of training modes, allowing both synchronous and asynchronous RL workflows and programmable rollout interfaces that simplify experimentation with custom environments and reward signals. Because it integrates tightly with SGLang and other training engines, slime can improve scalability and efficiency while providing maintainability and adaptability for developing new models and training algorithms.
    Downloads: 1 This Week
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  • 4
    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...
    Downloads: 3 This Week
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  • 5
    KVCache-Factory

    KVCache-Factory

    Unified KV Cache Compression Methods for Auto-Regressive Models

    KVCache-Factory is an open-source research framework designed to explore and implement unified key-value cache compression techniques for autoregressive transformer models. In large language models, the key-value cache stores intermediate attention states that enable efficient token generation during inference, but these caches can consume large amounts of GPU memory when handling long contexts. KVCache-Factory provides a platform for implementing and evaluating multiple compression...
    Downloads: 0 This Week
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  • 6
    OmAgent

    OmAgent

    Build multimodal language agents for fast prototype and production

    OmAgent is an open-source Python framework designed to simplify the development of multimodal language agents that can reason, plan, and interact with different types of data sources. The framework provides abstractions and infrastructure for building AI agents that operate on text, images, video, and audio while maintaining a relatively simple interface for developers. Instead of forcing developers to implement complex orchestration logic manually, the system manages task scheduling, worker...
    Downloads: 0 This Week
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  • 7
    mergekit

    mergekit

    Tools for merging pretrained large language models

    mergekit is an open-source toolkit designed to combine multiple pretrained language models into a single unified model through parameter merging techniques. The framework enables developers to merge model checkpoints so that the resulting model inherits capabilities from several source models without requiring additional training. This approach allows researchers to combine specialized models into a more versatile system capable of performing multiple tasks. mergekit implements a variety of merging algorithms and strategies that control how model parameters are blended together during the merging process. ...
    Downloads: 0 This Week
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  • 8
    PKU Beaver

    PKU Beaver

    Constrained Value Alignment via Safe Reinforcement Learning

    PKU Beaver is an open-source research project focused on improving the safety alignment of large language models through reinforcement learning from human feedback under explicit safety constraints. The framework introduces techniques that separate helpfulness and harmlessness signals during training, allowing models to optimize for useful responses while minimizing harmful behavior. To support this process, the project provides datasets containing human-labeled examples that encode both...
    Downloads: 0 This Week
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  • 9
    MemoryOS

    MemoryOS

    MemoryOS is designed to provide a memory operating system

    ...These layers typically include short-term memory for immediate conversation context, mid-term memory for topic-level grouping, and long-term personal memory for persistent knowledge about users or tasks. The system dynamically updates and promotes information between these layers using structured algorithms that prioritize relevance and recency.
    Downloads: 2 This Week
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