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Phi 4 Mini Instruct Abliterated

Developed by lunahr
Phi-4-mini-instruct is a lightweight open-source model built on synthetic data and curated public websites, focusing on high-quality data with strong reasoning capabilities. It supports a 128K token context length and is enhanced through supervised fine-tuning and direct preference optimization to ensure precise instruction following and safety.
Downloads 250
Release Time : 3/1/2025

Model Overview

Phi-4-mini-instruct is a lightweight open-source model specializing in high-quality data and strong reasoning capabilities, suitable for multilingual scenarios and particularly ideal for resource-constrained environments and low-latency requirements.

Model Features

Lightweight Design
A 3.8B-parameter lightweight model, suitable for resource-constrained environments and low-latency requirements.
Multilingual Support
Supports 23 languages, including Chinese, English, French, German, and more.
Strong Reasoning Capabilities
Focuses on high-quality data and strong reasoning, excelling particularly in mathematics and logic.
128K Token Context Length
Supports long-context processing, ideal for complex tasks and multi-turn conversations.
Instruction-Following Optimization
Ensures precise instruction adherence and safety through supervised fine-tuning and direct preference optimization.

Model Capabilities

Text Generation
Multilingual Understanding
Mathematical Reasoning
Logical Reasoning
Code Generation

Use Cases

Business Applications
Customer Support
Used for multilingual customer support, providing fast and accurate responses.
Improves customer satisfaction and reduces response time.
Content Generation
Generates multilingual marketing content or product descriptions.
Increases content production efficiency and reduces labor costs.
Research
Language Model Research
Serves as a building block for generative AI capabilities, accelerating research in language and multimodal models.
Advances AI technology and promotes the development of multilingual models.
Mathematical and Logical Reasoning
Used for research and testing in mathematical and logical reasoning tasks.
Enhances model performance in complex tasks.
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