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2 Repos

Awesome GitHub RepositoriesTask Execution Pipelines

Sequences of model calls and output processors designed for evaluation or iterative training.

Distinct from Model Training Pipelines: Focuses on the operational sequence of model calls for a task, not the ML training of the model itself.

Explore 2 awesome GitHub repositories matching artificial intelligence & ml · Task Execution Pipelines. Refine with filters or upvote what's useful.

Awesome Task Execution Pipelines GitHub Repositories

Finde die besten Repos mit KI.Wir suchen mit KI nach den am besten passenden Repositories.
  • sylphai-inc/adalflowAvatar von SylphAI-Inc

    SylphAI-Inc/AdalFlow

    4,167Auf GitHub ansehen↗

    AdalFlow is an autonomous AI agent framework and LLM application library designed for building modular workflows. It serves as a model-agnostic interface and RAG pipeline orchestrator, allowing users to develop ReAct agents that utilize iterative reasoning and external tool execution to solve complex tasks. The project distinguishes itself through a prompt optimization system that uses textual gradient descent to automatically refine prompt templates and few-shot examples. It treats model feedback as a differentiable signal, enabling a form of LLM backpropagation to iteratively improve output

    Defines sequences of model calls and processors to build structured execution pipelines.

    Python
    Auf GitHub ansehen↗4,167
  • idea-ccnl/fengshenbang-lmAvatar von IDEA-CCNL

    IDEA-CCNL/Fengshenbang-LM

    4,128Auf GitHub ansehen↗

    Fengshenbang-LM is a Chinese language model ecosystem and pre-training framework designed for the development and fine-tuning of billion-parameter large language models. It serves as a natural language processing pipeline and cross-modal AI platform capable of generating content across different modalities, including text-to-image generation and protein structure prediction. The project provides a domain-specific model adapter for applying pretrained models to specialized industries such as healthcare, finance, and law. It utilizes a distributed configuration system and data sharding to manag

    Orchestrates sequential prediction and fine-tuning workflows for consistent downstream task deployment.

    Pythonaigcchinese-nlpdistributed-training
    Auf GitHub ansehen↗4,128
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