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Meet the moeinGTS Family: From Edge-Ready LLMs to Fine-Tuned Vision Adapters

The moeinGTS team, led by Ali and Arshiya Sohrevardi, has released an open-source family of AI models designed for local deployment, including lightweight LLMs (1.5b and 3b), a flagship model (GTS-1), a vision adapter (MoeinGTS-kamal-1), and a security adapter (moeinGTS-paspan). The models support GGUF quantization and integrate with Ollama, Hugging Face Diffusers, and PyTorch/TensorFlow, enabling collaborative multi-model agentic workflows.

read2 min views2 publishedAug 27, 2026

#

🚀 Introducing the moeinGTS AI Family: Lightweight, Modular & Open-Source

Building AI solutions often requires striking a balance between local efficiency, specialized performance, and seamless system integration. The moeinGTS ecosystem was designed to tackle these challenges by offering a suite of tailored open-source models—ranging from high-speed local LLMs to domain-specific vision and security adapters.

Here is a full breakdown of the moeinGTS model lineup:

🤖 The Model Lineup

moeinGTS 1.5b

— The Ultra-Lightweight Core

Role: Fast, low-latency reasoning & local orchestration. #

Best For: Edge devices, quick zero-shot responses, microcontrollers, and resource-constrained environments.

moeinGTS 3b

— The Balanced Workhorse

Role: High-efficiency general reasoning and structured data generation. #

Best For: Interactive local applications, lightweight agentic pipelines, and local API backends.

GTS-1

— The Flagship Intelligence

Role: Advanced logic, complex prompt adherence, and multi-step execution. #

Best For: Core system backends, deep code synthesis, and multi-agent coordination.

MoeinGTS-kamal-1 — The Visual & Artistic Adapter

Role: Specialized Diffusers/LoRA fine-tune for high-detail atmospheric and monochrome image generation. #

Best For: Style-consistent visual generation, portraiture, and aesthetic UI graphics.

moeinGTS-paspan

— The Security & Guardrail Sentinel

Role: Input validation, prompt injection defense, and output safety filtering. #

Best For: Securing local agent workflows, auditing inputs, and ethical hacking guardrails.

🛠️ Architecture & Deployment

The entire moeinGTS suite is built with local accessibility in mind:

Quantization Support: Optimized for GGUF

formats (Q4_K_M

, Q8_0

) for low-VRAM environments. #

Framework Integration: Plug-and-play compatible with Ollama, Hugging Face Diffusers, and PyTorch/TensorFlow pipelines. #

Agentic Multi-Model Setup: Designed to run collaboratively—where paspan

guards, GTS-1

plans, and kamal-1

generates visual assets.

🔗 Explore & Connect

🔗 Explore & Connect

Hugging Face Models: #

Ollama Hub: arshiyasohrevardimoein/moeinGTS #

Developed by: Ali & Arshiya Sohrevardi (moeinGTS Team) #AI #OpenSource #MachineLearning #HuggingFace #Python #LocalAI

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