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2 Repos

Awesome GitHub RepositoriesPrompt Processing

Tools for dynamic prompt manipulation and injection.

Distinguishing note: Focuses on inline prompt modification.

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

Awesome Prompt Processing GitHub Repositories

Finde die besten Repos mit KI.Wir suchen mit KI nach den am besten passenden Repositories.
  • lllyasviel/fooocusAvatar von lllyasviel

    lllyasviel/Fooocus

    50,260Auf GitHub ansehen↗

    Fooocus is a generative image interface designed to simplify the creation of high-quality visual content from text descriptions. It functions as a latent diffusion pipeline and model orchestrator, managing the complex interactions between neural network layers, mathematical samplers, and hardware resource allocation to produce professional-grade imagery. The project distinguishes itself through a sophisticated prompt engineering engine and modular style management. Users can dynamically modify output characteristics by injecting style adapters directly into prompts or by utilizing wildcards a

    Injects dynamic content into text prompts using wildcards and weight adjustments.

    Python
    Auf GitHub ansehen↗50,260
  • nvidia/garakAvatar von NVIDIA

    NVIDIA/garak

    8,114Auf GitHub ansehen↗

    Garak is an AI model evaluation tool and vulnerability scanner designed for red teaming large language models and auditing the security of retrieval-augmented generation pipelines. It identifies behavioral weaknesses, such as jailbreaks, hallucinations, and data leakage, by simulating adversarial attacks and executing automated testing vectors. The framework utilizes an adaptive probing loop where prompts can react to previous model behavior and be modified in flight via middleware. To ensure consistent analysis, it employs a provider-agnostic interface to interact with various model APIs and

    Provides middleware to transform and paraphrase prompts in flight before they reach the model.

    Pythonaillm-evaluationllm-security
    Auf GitHub ansehen↗8,114
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