The NVIDIA Phi-4-reasoning-plus FP4 model is the quantized version of Microsoft’s Phi-4-reasoning-plus model, which is a multimodal foundation model that uses an optimized transformer architecture. For more information, please check here. The NVIDIA Phi-4-reasoning-plus FP4 model is quantized with TensorRT Model Optimizer.
This model is ready for commercial/non-commercial use.
Third-Party Community Consideration
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 (Phi-4-reasoning-plus) Model Card.
Developers looking to take off the shelf pre-quantized models for deployment in AI Agent systems, chatbots, RAG systems, and other AI-powered applications.
**This model was developed based on Phi-4-reasoning-plus
** Number of model parameters 14.7*10^9
Input:
Input Type(s): Text Input Format(s): String Input Parameters: 1D (One-Dimensional): Sequences Other Properties Related to Input: Context length up to 64k
Output:
Output Type(s): Text Output Format: String Output Parameters: 1D (One-Dimensional): Sequences Other Properties Related to Output: N/A
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.
The model is quantized with nvidia-modelopt v0.35.0
Post Training Quantization
This model was obtained by quantizing the weights and activations of Phi-4-reasoning-plus to FP4 data type, ready for inference with TensorRT-LLM. Only the weights and activations of the linear operators within transformer blocks are quantized.
** Data Collection Method by Dataset: Automated, Synthetic, Human
** Labeling Method by Dataset: Undisclosed
** Properties: Training data is a mixture of Q&A, chat format data in math, science, and coding. The chat prompts are sourced from filtered high-quality web data and optionally rewritten and processed through a synthetic data generation pipeline. We further include data to improve truthfulness and safety.
Testing Dataset:
** Data Collection Method by Dataset: Undisclosed
** Labeling Method by Dataset: Undisclosed
** Properties: Undisclosed
Inference:
Engine: TensorRT-LLM Test Hardware: B200
Usage
Deploy with TensorRT-LLM
To deploy the quantized checkpoint with TensorRT-LLM LLM API, follow the sample codes below:
LLM API sample usage:
from tensorrt_llm import LLM, SamplingParams
def main():
prompts = [
"Hello, my name is",
"The president of the United States is",
"The capital of France is",
"The future of AI is",
]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(model="nvidia/Phi-4-reasoning-plus-FP4")
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
# The entry point of the program need to be protected for spawning processes.
if __name__ == '__main__':
main()
Ethical Considerations
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