nvidia / Qwen2.5-VL-7B-Instruct-NVFP4

Model Overview

Description:

The NVIDIA Qwen2.5-VL-7B-Instruct-FP4 model is the quantized version of Alibaba's Qwen2.5-VL-7B-Instruct model, which is an auto-regressive language model that uses an optimized transformer architecture. For more information, please check here . The NVIDIA Qwen2.5-VL-7B-Instruct-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. It was developed and built to a third party’s requirements for this application and use case. See the Non-NVIDIA (Qwen2.5-VL-7B-Instruct) Model Card .

License/Terms of Use:

Use of this model is governed by nvidia-open-model-license ADDITIONAL INFORMATION: Apache 2.0 .

Deployment Geography:

Global, except in European Union

Use Case:

Developers looking to take off the shelf pre-quantized models for deployment in AI Agent systems, chatbots, RAG systems, and other AI-powered applications.

Release Date:

Huggingface 08/22/2025 via https://huggingface.co/nvidia/Qwen2.5-VL-7B-Instruct-FP4

Model Architecture:

Architecture Type: Transformers
Network Architecture: Qwen2.5-VL-7B

* This model was developed based on Qwen2.5-VL-7B ** Number of model parameters 7 10^9

Input:

Input Type(s): Multilingual text, and images
Input Format(s): String, Images
Input Parameters: One-Dimensional (1D), Two-Dimensional (2D)
Other Properties Related to Input: Context length up to 32K

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), they achieve faster training and inference times compared to CPU-only solutions.

Software Integration:

Supported Runtime Engine(s):

• TensorRT-LLM
  

Supported Hardware Microarchitecture Compatibility:

• NVIDIA Blackwell
  

Preferred Operating System(s):

• Linux
  

Model Version(s):

The model is quantized with nvidia-modelopt v0.35.0

Post Training Quantization

This model was obtained by quantizing the weights and activations of Qwen2.5-VL-7B-Instruct to FP4 data type, ready for inference with TensorRT-LLM. Only the weights and activations of the linear operators within transformer blocks of the language model are quantized.

Training, Testing, and Evaluation Datasets:

** Data Modality [Image] [Text]

Calibration Dataset:

** Link: cnn_dailymail
** Data collection method: Automated.
** Labeling method: Automated.

Training Datasets:

** Data Collection Method by Dataset: Undisclosed
** Labeling Method by Dataset: Undisclosed
** Properties: Undisclosed

Testing Dataset:

** Data Collection Method by Dataset: Undisclosed
** Labeling Method by Dataset: Undisclosed
** Properties: Undisclosed

Inference:

Engine: TensorRT-LLM
Test Hardware: B200 coming soon
** Currently supported on DGX Spark

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/Qwen2.5-VL-7B-Instruct-FP4", tensor_parallel_size=4)

    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 needs to be protected for spawning processes.
if __name__ == '__main__':
    main()

Ethical Considerations

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns here.