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
The TinyLlama initiative seeks to pretrain a Llama model with 1.1 billion parameters using a dataset of 3 trillion tokens. With the right optimizations, this ambitious task can be completed in a mere 90 days, utilizing 16 A100-40G GPUs. We have maintained the same architecture and tokenizer as Llama 2, ensuring that TinyLlama is compatible with various open-source projects that are based on Llama. Additionally, the model's compact design, consisting of just 1.1 billion parameters, makes it suitable for numerous applications that require limited computational resources and memory. This versatility enables developers to integrate TinyLlama seamlessly into their existing frameworks and workflows.
API Access
Has API
Yes
API Access
Has API
No
Screenshots View All
No images available
Integrations
ExecuTorch
Yes
Hugging Face
Yes
Microsoft Azure
Yes
Microsoft Foundry
Yes
Microsoft Foundry Models
Yes
Runpod
No
Integrations
ExecuTorch
No
Hugging Face
No
Microsoft Azure
No
Microsoft Foundry
No
Microsoft Foundry Models
No
Runpod
Yes
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Pricing Details
Free
Open source
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
No
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
Yes
Mac
Yes
Linux
Yes
Chromebook
No
Customer Support
Business Hours
Yes
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
No
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)
No
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
TinyLlama
Website
github.com/jzhang38/TinyLlama