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Phi-4-mini-reasoning is a compact, transformer-based language model developed by Microsoft, specifically optimized for mathematical reasoning tasks. With 3.8 billion parameters and support for a 128K token context length, it delivers high-quality, step-by-step problem-solving capabilities in environments where computational resources or latency are constrained. Fine-tuned using synthetic mathematical data generated by a more advanced model, Phi-4-mini-reasoning excels in multi-step, logic-intensive problem-solving scenarios, making it suitable for applications such as formal proof generation, symbolic computation, and advanced word problems. Key Features and Functionality: - Optimized for Mathematical Reasoning: Designed to handle complex, multi-step mathematical problems with structured logic and analytical thinking. - Compact Architecture: Balances reasoning ability with efficiency, enabling deployment in resource-constrained environments. - Extended Context Length: Supports up to 128K tokens, allowing for comprehensive context retention across problem-solving steps. - Fine-Tuned with Synthetic Data: Trained on a diverse set of over one million math problems, enhancing its reasoning performance. Primary Value and Problem Solving: Phi-4-mini-reasoning addresses the need for efficient, high-quality mathematical reasoning in scenarios where computational resources are limited. Its compact size and optimized performance make it ideal for educational applications, embedded tutoring systems, and deployments on edge or mobile devices. By maintaining context across multiple steps and applying structured logic, it provides accurate and reliable solutions for complex mathematical problems, thereby enhancing learning experiences and supporting advanced analytical tasks.
The Phi-3 Mini-4K-Instruct is a lightweight, state-of-the-art language model developed by Microsoft, featuring 3.8 billion parameters. It is part of the Phi-3 model family and is designed to support a context length of 4,000 tokens. Trained on a combination of synthetic data and filtered publicly available websites, the model emphasizes high-quality, reasoning-dense content. Post-training enhancements, including supervised fine-tuning and direct preference optimization, have been applied to improve instruction adherence and safety measures. The Phi-3 Mini-4K-Instruct demonstrates robust performance across benchmarks assessing common sense, language understanding, mathematics, coding, long-context comprehension, and logical reasoning, positioning it as a leading model among those with fewer than 13 billion parameters. Key Features and Functionality: - Compact Architecture: With 3.8 billion parameters, the model offers a balance between performance and resource efficiency. - Extended Context Length: Supports processing of up to 4,000 tokens, enabling handling of longer inputs effectively. - High-Quality Training Data: Utilizes a curated dataset combining synthetic data and filtered web content, focusing on high-quality and reasoning-intensive information. - Enhanced Instruction Following: Post-training processes, including supervised fine-tuning and direct preference optimization, improve the model's ability to follow instructions accurately. - Versatile Performance: Excels in various tasks such as common sense reasoning, language understanding, mathematical problem-solving, coding, and logical reasoning. Primary Value and User Solutions: The Phi-3 Mini-4K-Instruct addresses the need for a powerful yet efficient language model suitable for environments with limited memory and computational resources. Its compact size and extended context capabilities make it ideal for applications requiring low latency and strong reasoning abilities. By delivering state-of-the-art performance in a resource-efficient package, it enables developers and researchers to integrate advanced language understanding and generation features into their applications without the overhead associated with larger models.
The Phi-3-Small-128K-Instruct is a 7-billion-parameter, state-of-the-art language model developed by Microsoft. It is part of the Phi-3 family and is designed to handle a context length of up to 128,000 tokens. Trained on a combination of synthetic data and filtered publicly available web content, the model emphasizes high-quality, reasoning-dense properties. Post-training processes, including supervised fine-tuning and direct preference optimization, have been applied to enhance its instruction-following capabilities and safety measures. The Phi-3-Small-128K-Instruct demonstrates robust performance across benchmarks testing common sense, language understanding, mathematics, coding, long-context comprehension, and logical reasoning, positioning it competitively among models of similar and larger sizes. Key Features and Functionality: - Extensive Context Handling: Supports a context length of up to 128,000 tokens, enabling the processing of long and complex inputs. - High-Quality Training Data: Utilizes a blend of synthetic and curated web data, focusing on content rich in reasoning and quality. - Advanced Post-Training Techniques: Incorporates supervised fine-tuning and direct preference optimization to improve instruction adherence and safety. - Versatile Performance: Excels in tasks requiring common sense, language understanding, mathematical reasoning, coding proficiency, and logical analysis. Primary Value and User Solutions: The Phi-3-Small-128K-Instruct model offers developers and researchers a powerful tool for building AI systems that require deep reasoning and the ability to process extensive contextual information. Its efficient architecture makes it suitable for memory and compute-constrained environments, while its strong performance in various reasoning tasks addresses the needs of applications demanding high levels of understanding and analysis. By providing a robust foundation for generative AI features, the model accelerates the development of advanced language and multimodal applications.
Improve the accuracy of your machine learning models with publicly available datasets. Save time on data discovery and preparation by using curated datasets that are ready to use in machine learning workflows and easy to access from Azure services.
The Phi-3 Mini-4K-Instruct is a lightweight, state-of-the-art language model developed by Microsoft, featuring 3.8 billion parameters. It is part of the Phi-3 model family and is designed to support a context length of 4,000 tokens. Trained on a combination of synthetic data and filtered publicly available websites, the model emphasizes high-quality, reasoning-dense content. Post-training enhancements, including supervised fine-tuning and direct preference optimization, have been applied to improve instruction adherence and safety measures. The Phi-3 Mini-4K-Instruct demonstrates robust performance across benchmarks assessing common sense, language understanding, mathematics, coding, long-context comprehension, and logical reasoning, positioning it as a leading model among those with fewer than 13 billion parameters. Key Features and Functionality: - Compact Architecture: With 3.8 billion parameters, the model offers a balance between performance and resource efficiency. - Extended Context Length: Supports processing of up to 4,000 tokens, enabling handling of longer inputs effectively. - High-Quality Training Data: Utilizes a curated dataset combining synthetic data and filtered web content, focusing on high-quality and reasoning-intensive information. - Enhanced Instruction Following: Post-training processes, including supervised fine-tuning and direct preference optimization, improve the model's ability to follow instructions accurately. - Versatile Performance: Excels in various tasks such as common sense reasoning, language understanding, mathematical problem-solving, coding, and logical reasoning. Primary Value and User Solutions: The Phi-3 Mini-4K-Instruct addresses the need for a powerful yet efficient language model suitable for environments with limited memory and computational resources. Its compact size and extended context capabilities make it ideal for applications requiring low latency and strong reasoning abilities. By delivering state-of-the-art performance in a resource-efficient package, it enables developers and researchers to integrate advanced language understanding and generation features into their applications without the overhead associated with larger models.
Build differentiated, flexible mobility experiences, enabled by Microsoft Connected Vehicle Platform.
Azure Health Bot empowers developers in healthcare organizations to build and deploy AI-powered, compliant, conversational healthcare experiences at scale. It combines built-in medical database with natural language capabilities to understand clinical terminology and can be easily customized to support your organization's clinical use cases. The service ensures alignment with industry compliance requirements and is privacy protected to HIPAA standards.
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