# Inference Model

UIGEN-T1-Qwen-7b is a large language model specializing in UI inference and generation. Utilizing a sophisticated reasoning chain approach, it generates HTML and CSS-based UI components, offering front-end development teams a fast layout generation solution. Fine-tuned from Qwen2.5-Coder-7B-Instruct, the model excels at generating basic front-end applications like dashboards, login pages, and registration forms. Its key advantage lies in its ability to rapidly produce structured HTML/CSS code and, through reasoning, create UI layouts that adhere to design principles. The primary use cases include streamlining front-end development workflows, boosting development efficiency, and providing support for low-code/no-code tools.

s1 is an inference model that focuses on achieving efficient text generation capabilities with a limited set of samples. It scales during testing using budget enforcement techniques, capable of matching the performance of o1-preview. Developed by Niklas Muennighoff et al., the related research is published on arXiv. The model employs Safetensors technology, boasts 32.8 billion parameters, and supports text generation tasks. Its main advantage lies in achieving high-quality reasoning through a limited number of samples, making it suitable for scenarios requiring efficient text generation.

OpenAI o3-mini is the latest inference model introduced by OpenAI, optimized for science, technology, engineering, and mathematics (STEM) applications. It provides powerful inference capabilities while maintaining low costs and low latency, excelling particularly in mathematics, science, and programming. This model supports various developer features, such as function calls and structured outputs, and allows users to select different inference strengths based on their needs. The launch of o3-mini further reduces the cost of using inference models, making it more suitable for a wide range of application scenarios.

DeepSeek-R1-Zero is an inference model developed by the DeepSeek team, focusing on enhancing inference capabilities through reinforcement learning. This model exhibits powerful reasoning behaviors such as self-validation, reflection, and generating long chains of reasoning without requiring supervised fine-tuning. Its main advantages include efficient inference capabilities, immediate usability without pre-training, and outstanding performance in mathematical, coding, and reasoning tasks. The model is built on the DeepSeek-V3 architecture and is suitable for large-scale inference tasks in both research and commercial applications.

DeepSeek-R1, launched by the DeepSeek team, is the first generation inference model that exhibits exceptional inference capabilities through extensive reinforcement learning training, eliminating the need for supervised fine-tuning. The model excels in mathematical, coding, and reasoning tasks, comparable to the OpenAI-o1 model. Additionally, DeepSeek-R1 offers various distilled models catering to different scalability and performance requirements. Its open-source nature provides robust tools for the research community, supporting commercial use and further development.

The OpenAI o3 model is a next-generation inference model following the o1, featuring both o3 and o3-mini versions. Under certain conditions, o3 approaches capabilities of Artificial General Intelligence (AGI), scoring as high as 87.5% on the ARC-AGI benchmark test, significantly surpassing the human average. It excels in mathematics and programming tasks, achieving 96.7% in the 2024 American Mathematics Invitational Exam (AIME) and reaching a rating of 2727 on Codeforces. The o3 model can self-verify facts and use the 'private reasoning chain' for inference, enhancing answer accuracy. It is the first model trained with 'deliberative alignment' techniques to adhere to safety principles. Currently, the o3 model is not widely available, but security researchers can register for preview access to the o3-mini model. The o3 mini version will launch at the end of January, followed closely by the full version.