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What is Tev1?

Tev1 is an advanced model designed for quick decision-making and classification, created by Together AI and fine-tuned from Qwen3.5 4B. It features an intuitive interface that allows users to enter a state, pose a question, and select from 2 to 24 labeled options, leading to an output of a single letter that corresponds to a semantic key via software. This innovative structure is particularly advantageous for diverse production tasks, including the routing of support tickets, evaluating automated returns, implementing policy guidelines, conducting sentiment analysis, categorizing documents, and embedding precise decision-making within extensive agent workflows. With its impressive 4.7B parameter count and a context window of 32.8K tokens, Tev1 leverages non-thinking generation to deliver swift and dependable classifications. The model has been honed using a broad collection of 37,840 examples that encompass intent detection, boolean comprehension, sentiment evaluation, policy enforcement, and the categorization of news articles and academic research. For optimal performance, it is recommended to set the temperature to 0 and maintain a low output budget, which ensures that the results remain both consistent and easy to understand. Furthermore, Tev1 is recognized as a highly adaptable tool that meets a variety of classification requirements effectively and efficiently. Overall, its capabilities make it an essential asset in any decision-making framework.

What is Phi-4-mini-reasoning?

Phi-4-mini-reasoning is an advanced transformer-based language model that boasts 3.8 billion parameters, tailored specifically for superior performance in mathematical reasoning and systematic problem-solving, especially in scenarios with limited computational resources and low latency. The model's optimization is achieved through fine-tuning with synthetic data generated by the DeepSeek-R1 model, which effectively balances performance and intricate reasoning skills. Having been trained on a diverse set of over one million math problems that vary from middle school level to Ph.D. complexity, Phi-4-mini-reasoning outperforms its foundational model by generating extensive sentences across numerous evaluations and surpasses larger models like OpenThinker-7B, Llama-3.2-3B-instruct, and DeepSeek-R1 in various tasks. Additionally, it features a 128K-token context window and supports function calling, which ensures smooth integration with different external tools and APIs. This model can also be quantized using the Microsoft Olive or Apple MLX Framework, making it deployable on a wide range of edge devices such as IoT devices, laptops, and smartphones. Furthermore, its design not only enhances accessibility for users but also opens up new avenues for innovative applications in the realm of mathematics, potentially revolutionizing how such problems are approached and solved.

Media

Media

Integrations Supported

Python
Qwen3.5
TypeScript

Integrations Supported

ExecuTorch
Hugging Face
Microsoft Azure
Microsoft Foundry
Microsoft Foundry Models

API Availability

Has API

API Availability

Has API

Pricing Information

$0.04 per 1M tokens
Free Version

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Standard Support
Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Together AI

Date Founded

2022

Company Location

United States

Company Website

www.together.ai/models/tev1-4b-experimental

Company Facts

Organization Name

Microsoft

Date Founded

1975

Company Location

United States

Company Website

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

Categories and Features

AI Decision Models

Not specified

Categories and Features

AI Models

Not specified

AI Reasoning Models

Not specified

Small Language Models

Not specified

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