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Description

Phi-4-mini-reasoning is a transformer-based language model with 3.8 billion parameters, specifically designed to excel in mathematical reasoning and methodical problem-solving within environments that have limited computational capacity or latency constraints. Its optimization stems from fine-tuning with synthetic data produced by the DeepSeek-R1 model, striking a balance between efficiency and sophisticated reasoning capabilities. With training that encompasses over one million varied math problems, ranging in complexity from middle school to Ph.D. level, Phi-4-mini-reasoning demonstrates superior performance to its base model in generating lengthy sentences across multiple assessments and outshines larger counterparts such as OpenThinker-7B, Llama-3.2-3B-instruct, and DeepSeek-R1. Equipped with a 128K-token context window, it also facilitates function calling, which allows for seamless integration with various external tools and APIs. Moreover, Phi-4-mini-reasoning can be quantized through the Microsoft Olive or Apple MLX Framework, enabling its deployment on a variety of edge devices, including IoT gadgets, laptops, and smartphones. Its design not only enhances user accessibility but also expands the potential for innovative applications in mathematical fields.

Description

Xgen-small is a compact language model crafted by Salesforce AI Research that is tailored for enterprise use, offering efficient long-context capabilities at a manageable cost. It employs a combination of focused data curation, scalable pre-training, length extension, instruction fine-tuning, and reinforcement learning to address the intricate and high-volume inference needs of contemporary businesses. In contrast to conventional large models, Xgen-small excels in processing extensive contexts, allowing it to effectively synthesize insights from various sources such as internal documents, code bases, research articles, and real-time data feeds. With parameter sizes of 4B and 9B, it strikes a careful balance between cost efficiency, privacy protections, and comprehensive long-context comprehension, positioning itself as a reliable and sustainable option for large-scale Enterprise AI implementation. This innovative approach not only enhances operational efficiency but also empowers organizations to leverage AI effectively in their strategic initiatives.

API Access

Has API Yes 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

Agentforce Vibes No 
ExecuTorch Yes 
Hugging Face Yes 
Microsoft Azure Yes 
Microsoft Foundry Yes 
Microsoft Foundry Models Yes 

Integrations

Agentforce Vibes Yes 
ExecuTorch No 
Hugging Face No 
Microsoft Azure No 
Microsoft Foundry No 
Microsoft Foundry Models No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours Yes 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours Yes 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) Yes 
In Person Yes 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) Yes 
In Person Yes 

Vendor Details

Company Name

Microsoft

Founded

1975

Country

United States

Website

azure.microsoft.com/en-us/blog/one-year-of-phi-small-language-models-making-big-leaps-in-ai/

Vendor Details

Company Name

Salesforce

Founded

1999

Country

United States

Website

www.salesforce.com/blog/xgen-small-enterprise-ready-small-language-models/

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