2 Repos
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.
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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.
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.