thelamapi/next2-fast

VISIONConcurrency Cost:1Model Size:4.3BQuant:BF16Ctx Length:32kPublished:Feb 15, 2026License:mitArchitecture:Transformer0.0K Open Weights Cold

thelamapi/next2-fast is a 4-billion parameter Multimodal Vision-Language Model (VLM) developed by Lamapi, built upon the Gemma 3 architecture. It is optimized for high-performance reasoning across multiple languages, including English, Turkish, German, French, and Spanish, and seamlessly processes both images and text. This model is designed for efficient deployment on consumer hardware, offering fast inference speeds and strong capabilities in coding, mathematics, and visual scene analysis.

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Next 2 Fast: Global Multimodal Intelligence

Next 2 Fast is a 4-billion parameter Multimodal Vision-Language Model (VLM) developed by Lamapi, an AI research lab in Tรผrkiye. Built on the Gemma 3 architecture, it is engineered for high-performance reasoning across languages and modalities, aiming to bridge the gap between large commercial models and accessible open-source intelligence.

Key Capabilities

  • Multilingual Proficiency: Fluent in English, Turkish, German, French, Spanish, and over 25 other languages, offering true multilingual understanding without "translation-ese."
  • Multimodal Vision-Language: Processes both images and text to generate code, descriptions, and analysis, capable of reading charts and identifying objects.
  • High Efficiency & Speed: Optimized for low-latency inference, running approximately 2x faster than previous generations and deployable on consumer hardware (8GB VRAM) using 4-bit/8-bit quantization.
  • Strong Reasoning: Delivers flagship-level performance in a compact size, outperforming Gemma 3 4B, Llama 3.2 3B, and Phi-3.5 Mini on benchmarks like MMLU (85.1%), MMLU-Pro (67.4%), GSM8K (83.5%), and MATH (71.2%).
  • Code & Math: Exhibits strong capabilities in Python coding, debugging, and solving mathematical problems.

Good For

  • Applications requiring fast, efficient multimodal reasoning on consumer hardware.
  • Multilingual AI assistants and content generation in supported languages.
  • Tasks involving visual intelligence, such as image analysis, chart interpretation, and object identification.
  • Developers seeking a powerful yet accessible VLM for global deployment and real-time applications.