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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
Smaug-72B is a formidable open-source large language model (LLM) distinguished by several prominent features:
Exceptional Performance: It currently ranks first on the Hugging Face Open LLM leaderboard, outperforming models such as GPT-3.5 in multiple evaluations, demonstrating its ability to comprehend, react to, and generate text that closely resembles human writing.
Open Source Availability: In contrast to many high-end LLMs, Smaug-72B is accessible to everyone for use and modification, which encourages cooperation and innovation within the AI ecosystem.
Emphasis on Reasoning and Mathematics: This model excels particularly in reasoning and mathematical challenges, a capability attributed to specialized fine-tuning methods developed by its creators, Abacus AI.
Derived from Qwen-72B: It is essentially a refined version of another robust LLM, Qwen-72B, which was launched by Alibaba, thereby enhancing its overall performance.
In summary, Smaug-72B marks a notable advancement in the realm of open-source artificial intelligence, making it a valuable resource for developers and researchers alike. Its unique strengths not only elevate its status but also contribute to the ongoing evolution of AI technology.
API Access
Has API
API Access
Has API
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Integrations
ChatLLM
Hugging Face
Microsoft Azure
Microsoft Foundry
Microsoft Foundry Models
Integrations
ChatLLM
Hugging Face
Microsoft Azure
Microsoft Foundry
Microsoft Foundry Models
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
Free
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
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
Abacus
Founded
2019
Country
United States
Website
huggingface.co/abacusai/Smaug-72B-v0.1