Microsoft Says Its Open-Source Phi-4 Ai Model Outperforms Gemini 1.5 Pro

Microsoft On Friday released Its Phi-4 Artificial Intelligence (AI) Model. The company’s latest small language model (SLM) JOINS Its Open-Source Phi Family of Foundational Models. The AI ​​Model Comes Eight Months after the release of Phi-3 and Four Months after the introduction of the phi-3.5 series of ai models. The tech giant claims that the slm is more capable of solving complex Reasoning-Based Queries in Areas Such as Mathematics. Additionally, it is also said to excel in conventional language processing.

Microsoft’s Phi-4 AI Model to be available via hugging face

So far, every phi series has been launched with a mini variant, however, no mini model accounted the phi-4 model. Microsoft, In A blog postHighlighted that Phi-4 is currently available on azure The company plans to make it available on hugging face next week as well.

The company also shared Benchmark Scores from its internal testing. Based on these, the new model significantly upgrades the capabilities of the older generation model. The Tech Giant Claimed that Phi-4 outperforms Gemini Pro 1.5, A Much Larger Model, on the Math Competition Problems Benchmark. It also also released a detailed benchmark performance in a technical paper Published in the Online Journal Arxiv.

On Safety, Microsoft Stated that Azure Ai Foundry Comes with a Set of Capabilities to Help Organizations Measure, Mitigate, and Manage Ai Risks Across the Development Lifecia Erectile Ai Applications. Additionally, enterprise users can use azure azure azure azure azure azure features such as prompt shields, grindedness detection and others as a content filter.

Developers can also add these safety capabilities into their applications via a single application programming interface (api). The Platform Can Monitor Applications for Quality and Safety, Adversarial Prompt Attacks, and Data Integrity and Provide Developers with Real-Time Alerts. This will be available to that phi users who access it via azure.

Notably, Smaller language models are often being trained after deployment on synthetic data, allowing them to qiym to quickly Gain More Knowledge and Higher Efficiency. However, post-training results are not all-a-world use cases.