Average Ratings 0 Ratings
Average Ratings 0 Ratings
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
Distil Labs enhances AI performance by substituting costly calls to advanced models with tailored small language models designed for specific tasks while ensuring the quality standards are upheld. By monitoring real production traffic and gathering traces from current LLM requests, it constructs an evaluation set to gain insights into actual workload behavior. Following this, the company creates and verifies synthetic training data, aligns the data distribution with the intended workload, and engages in supervised fine-tuning alongside reinforcement learning. The model is then quantized, and an optimized endpoint is established. The outcomes are systematically assessed against the existing model concerning accuracy, latency, and efficiency, providing teams with data to determine when to increase traffic. Ultimately, the OpenAI-compatible endpoint features a specialized small language model, prompt optimization, effective caching, and refined serving tailored for the specific application, ensuring maximum performance. This comprehensive approach allows organizations to maximize the potential of their AI implementations.
API Access
Has API
Yes
API Access
Has API
Yes
Integrations
ExecuTorch
Yes
GPT-5.4 nano
No
Gemini 2.0 Flash-Lite
No
Hugging Face
Yes
Microsoft Azure
Yes
Microsoft Foundry
Yes
Microsoft Foundry Models
Yes
Integrations
ExecuTorch
No
GPT-5.4 nano
Yes
Gemini 2.0 Flash-Lite
Yes
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
$0.04 per 1M tokens
Free Trial
No
Free Version
Yes
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
No
Live Rep (24/7)
Yes
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
No
Live Training (Online)
Yes
In Person
No
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
distil labs
Founded
2024
Country
Germany
Website
www.distillabs.ai/