Linux / amd64
Linux / arm64
This container houses the Phi-4-Mini-Instruct, which is a lightweight open model built upon synthetic data and filtered publicly available websites - with a focus on high-quality, reasoning dense data. The model belongs to the Phi-4 model family and supports 128K token context length. The model underwent an enhancement process, incorporating both supervised fine-tuning and direct preference optimization to support precise instruction adherence and robust safety measures.
The container components are ready for commercial/non-commercial use.
This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party's requirements for this application and use case; see link to Non-NVIDIA[microsoft/Phi-4-mini-instruct]
(microsoft/Phi-4-mini-instruct · Hugging Face).
GOVERNING TERMS: The NIM container is governed by the NVIDIA Software License Agreement and the Product-Specific Terms for NVIDIA AI Products; and the use of the model is governed by the NVIDIA Community Model License Agreement.
ADDITIONAL INFORMATION: MIT License.
You are responsible for ensuring that your use of NVIDIA provided models complies with all applicable laws.
Global
Build.Nvidia.com 02/26/2025 via
phi-4-mini-instruct Model by Microsoft | NVIDIA NIM
Github 02/26/2026 via
Phi-4-mini-instruct and Phi-4-multimodal-instruct are now available in GitHub Models (GA) - GitHub Changelog
Huggingface 02/27/2025 via
microsoft/Phi-4-mini-instruct · Hugging Face
Phi-4-Mini-Instruct Container includes the following model:
| Model Name & Link | Use Case | How to Pull the Model |
|---|---|---|
| Phi-4-Mini-Instruct | Instruction-tuned small language model for reasoning, math, code generation, and general-purpose dialogue, suitable for deployment in constrained compute environments or as a backbone for fine-tuned task-specific systems. | Automatic |
Visit the NIM Container LLM page for release documentation, deployment guides, and more.
Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.
Phi-4-Mini-Instruct-1.12.0
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