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SkalskiP avatar

SkalskiP/SoM

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87 Stars·6 Forks·Jupyter Notebook·7 Aufrufe

SoM

Unofficial implementation and experiments related to Set-of-Mark (SoM) 👁️

Features

  • Foundational AI References - Techniques for Set of Mark prompting in vision models.

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Häufig gestellte Fragen

Was macht skalskip/som?

Unofficial implementation and experiments related to Set-of-Mark (SoM) 👁️

Was sind die Hauptfunktionen von skalskip/som?

Die Hauptfunktionen von skalskip/som sind: Foundational AI References.

Welche Open-Source-Alternativen gibt es zu skalskip/som?

Open-Source-Alternativen zu skalskip/som sind unter anderem: datawhalechina/dopmc — Datawhale 开源项目管理委员会(Datawhale Open-source Project Management Committee,简称DOPMC). en/get-started. jushbjj/mr.-ranedeer-ai-tutor — Mr. Ranedeer AI Tutor is an AI education framework and system prompt designed to transform a large language model into… linshenkx/prompt-optimizer — Prompt Optimizer is a framework designed for the iterative refinement and testing of text-based instructions for large… rryuliu/securitygpt. significant-gravitas/auto-gpt — Auto-GPT is an autonomous agent framework that uses large language models to decompose complex goals and execute…

Open-Source-Alternativen zu SoM

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  • datawhalechina/dopmcAvatar von datawhalechina

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  • jushbjj/mr.-ranedeer-ai-tutorAvatar von JushBJJ

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    29,599Auf GitHub ansehen↗

    Mr. Ranedeer AI Tutor is an AI education framework and system prompt designed to transform a large language model into a personalized tutor. It uses a structured set of instructions to organize educational content into sequential modules and knowledge assessments for adaptive learning. The system features a persona template that allows for the adjustment of academic depth and communication tone to match a student's specific needs. It also provides multilingual support, enabling the tutor to switch instruction and output languages based on user preferences. The framework covers custom lesson

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  • linshenkx/prompt-optimizerAvatar von linshenkx

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    Prompt Optimizer is a framework designed for the iterative refinement and testing of text-based instructions for large language models. It functions as an automated evaluation pipeline that systematically adjusts prompt structure, constraints, and clarity to improve the accuracy and consistency of model outputs. The system distinguishes itself through a model-agnostic interface that standardizes communication across different artificial intelligence providers. It incorporates a versioned asset management system to track prompt history, enabling developers to maintain consistency and perform r

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Alle 8 Alternativen zu SoM anzeigen→