InternVL2-4B model enabling with OpenVINO

Authors: Tong Qiu, Xiake Sun

Starting with OpenVINO.GenAI 2025.1, the C API has been introduced, primarily to enhance interoperability with other programming languages, enabling developers to more effectively utilize OpenVINO-based generative AI across diverse coding environments.

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Compared to C++, C's ABI is more stable, often serving as an interface layer or bridge language for cross-language interoperability and integration. This allows developers to leverage the performance benefits of C++ in the backend while using other high-level languages for easier implementation and integration.

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As a milestone, we have currently delivered only the LLMPipeline and its associated C API interface. If you have other requirements or encounter any issues during usage, please submit an issue to OpenVINO.GenAI

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‍Currently, we have implemented a Go application Ollama using the C API (Please refer to https://blog.openvino.ai/blog-posts/ollama-integrated-with-openvino-accelerating-deepseek-inference), which includes more comprehensive features such as performance benchmarking for developers reference.

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Now, let's dive into the design logic of the C API, using a .NET C# example as a case study, based on the Windows platform with .NET 8.0.

Live Demo

Before we dive into the details, let's take a look at the final C# version of the ChatSample, which supports multi-turn conversations. Below is a live demo

How to Build a Chat Sample by C#

P/Invoke: Wrapping Unmanaged Code in .NET

First, the official GenAI C API can be found in this folder https://github.com/openvinotoolkit/openvino.genai/tree/master/src/c/include/openvino/genai/c . We also provide several pure C samples https://github.com/openvinotoolkit/openvino.genai/tree/master/samples/c/text_generation . Now, we will build our own C# Chat Sample based on the chat_sample_c. This sample can facilitate multi-turn conversations with the LLM.

C# can access structures, functions and callbacks in the unmanaged library openvino_genai_c.dll through P/Invoke. This example demonstrates how to invoke unmanaged functions from managed code.

public static class NativeMethods
{
 DllImport("openvino_genai_c.dll", CallingConvention = CallingConvention.Cdecl)]
public static extern ov_status_e ov_genai_llm_pipeline_create(
        [MarshalAs(UnmanagedType.LPStr)] string models_path,
        [MarshalAs(UnmanagedType.LPStr)] string device,
        out IntPtr pipe);

[DllImport("openvino_genai_c.dll", CallingConvention = CallingConvention.Cdecl)]
public static extern void ov_genai_llm_pipeline_free(IntPtr pipeline);

//Other methods

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The dynamic library openvino_genai_c.dll is imported, which relies on openvino_genai.dll. CallingConvention = CallingConvention.Cdecl here corresponds to the default calling convention _cdecl in C, which defines the argument-passing order, stack-maintenance responsibility, and name-decoration convention. For more details, refer to Argument Passing and Naming Conventions.

Additionally, the return value ov_status_e reuses an enum type from openvino_c.dll to indicate the execution status of the function. We need to implement a corresponding enum type in C#, such as

public enum ov_status_e
{
    OK = 0,
    GENERAL_ERROR = -1,
    NOT_IMPLEMENTED = -2,
    //...

}

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Next, we will implement our C# LLMPipeline, which inherits the IDisposable interface. This means that its instances require cleanup after use to release the unmanaged resources they occupy. In practice, object allocation and deallocation for native pointers are handled through the C interface provided by OpenVINO.GenAI. The OpenVINO.GenAI library takes full responsibility for memory management, which ensures memory safety and eliminates the risk of manual memory errors.

public class LlmPipeline : IDisposable
{
    private IntPtr _nativePtr;

    public LlmPipeline(string modelPath, string device)
    {
        var status = NativeMethods.ov_genai_llm_pipeline_create(modelPath, device, out _nativePtr);
        if (_nativePtr == IntPtr.Zero || status != ov_status_e.OK)
        {
            Console.WriteLine($"Error: {status} when creating LLM pipeline.");
            throw new Exception("Failed to create LLM pipeline.");
        }

        Console.WriteLine("LLM pipeline created successfully!");
    }

    public void Dispose()
    {
        if (_nativePtr != IntPtr.Zero)
        {
            NativeMethods.ov_genai_llm_pipeline_free(_nativePtr);
            _nativePtr = IntPtr.Zero;
        }

        GC.SuppressFinalize(this);
    }
    // Other Methods
}

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Callback Implementation

Next, let's implement the most complex method of the LLMPipeline, the GenerateStream method. This method encapsulates the LLM inference process. Let's take a look at the original C code. The result can be retrieved either via ov_genai_decoded_results or streamer_callback. ov_genai_decoded_results provides the inference result all at once, while streamer_callback allows for streaming inference results. ov_genai_decoded_results or streamer_callback must be non-NULL; neither can be NULL at the same time. For more information please refer to the comments https://github.com/openvinotoolkit/openvino.genai/blob/master/src/c/include/openvino/genai/c/llm_pipeline.h

// code snippets from //https://github.com/openvinotoolkit/openvino.genai/blob/master/src/c/include/openvino/genai/c/llm_// pipeline.h 
typedef enum {
    OV_GENAI_STREAMMING_STATUS_RUNNING = 0,  // Continue to run inference
    OV_GENAI_STREAMMING_STATUS_STOP =
        1,  // Stop generation, keep history as is, KV cache includes last request and generated tokens
    OV_GENAI_STREAMMING_STATUS_CANCEL = 2  // Stop generate, drop last prompt and all generated tokens from history, KV
                                           // cache includes history but last step
} ov_genai_streamming_status_e;

// ...
typedef struct {
    ov_genai_streamming_status_e(
        OPENVINO_C_API_CALLBACK* callback_func)(const char* str, void* args);  //!< Pointer to the callback function
    void* args;  //!< Pointer to the arguments passed to the callback function
} streamer_callback;

// ...
OPENVINO_GENAI_C_EXPORTS ov_status_e ov_genai_llm_pipeline_generate(ov_genai_llm_pipeline* pipe,
                                                                    const char* inputs,
                                                                    const ov_genai_generation_config* config,
                                                                    const streamer_callback* streamer,
                                                                    ov_genai_decoded_results** results);

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The streamer_callback structure includes not only the callback function itself, but also an additional void* args for enhanced flexibility. This design allows developers to pass custom context or state information to the callback.

For example, in C++ it's common to pass a this pointer through args, enabling the callback function to access class members or methods when invoked.

// args is a this pointer
void callback_func(const char* str, void* args) {
    MyClass* self = static_cast<MyClass*>(args);
    self->DoSomething();
}

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This C# code defines a class StreamerCallback that helps connect a C callback function with a C# method. It wraps a C function pointer MyCallbackDelegate and a void* args into a struct.

- ToNativePTR method constructs the streamer_callback structure, allocates a block of memory, and copies the structure's data into it, allowing it to be passed to a native C function.

- GCHandle is used to safely pin the C# object so that it can be passed as a native pointer to unmanaged C code.

- CallbackWrapper method is the actual function that C code will call.

[UnmanagedFunctionPointer(CallingConvention.Cdecl)]
public delegate ov_genai_streamming_status_e MyCallbackDelegate(IntPtr str, IntPtr args);

[StructLayout(LayoutKind.Sequential)]
public struct streamer_callback
{
    public MyCallbackDelegate callback_func;
    public IntPtr args;
}
public class StreamerCallback : IDisposable
{
    public Action<string> OnStream;
    public MyCallbackDelegate Delegate;
    private GCHandle _selfHandle;

    public StreamerCallback(Action<string> onStream)
    {
        OnStream = onStream;
        Delegate = new MyCallbackDelegate(CallbackWrapper);
        _selfHandle = GCHandle.Alloc(this); 
    }

    public IntPtr ToNativePtr()
    {
        var native = new streamer_callback
        {
            callback_func = Delegate,
            args = GCHandle.ToIntPtr(_selfHandle)
        };

        IntPtr ptr = Marshal.AllocHGlobal(Marshal.SizeOf<streamer_callback>());
        Marshal.StructureToPtr(native, ptr, false);
        return ptr;
    }

    public void Dispose()
    {
        if (_selfHandle.IsAllocated)
            _selfHandle.Free();
    }

    private ov_genai_streamming_status_e CallbackWrapper(IntPtr str, IntPtr args)
    {
        string content = Marshal.PtrToStringAnsi(str) ?? string.Empty;

        if (args != IntPtr.Zero)
        {
            var handle = GCHandle.FromIntPtr(args);
            if (handle.Target is StreamerCallback self)
            {
                self.OnStream?.Invoke(content);
            }
        }

        return ov_genai_streamming_status_e.OV_GENAI_STREAMMING_STATUS_RUNNING;
    }
}

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Then We implemented the GenerateStream method in class LLMPipeline.

 public void GenerateStream(string input, GenerationConfig config, StreamerCallback? callback = null)
 {
     IntPtr configPtr = config.GetNativePointer();
     IntPtr decodedPtr;// placeholder

     IntPtr streamerPtr = IntPtr.Zero;

     if (callback != null)
     {
         streamerPtr = callback.ToNativePtr();
     }

     var status = NativeMethods.ov_genai_llm_pipeline_generate(
         _nativePtr,
         input,
         configPtr,
         streamerPtr,  
         out decodedPtr
     );

     if (streamerPtr != IntPtr.Zero)
         Marshal.FreeHGlobal(streamerPtr);

     callback?.Dispose();

     if (status != ov_status_e.OK)
     {
         Console.WriteLine($"Error: {status} during generation.");
         throw new Exception("Failed to generate results.");
     }
     return;
 }

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We use the following code to invoke our callback and GenerateStream.

pipeline.StartChat(); // Start chat with keeping history in kv cache.

Console.WriteLine("question:");
while (true)
{
    string? input = Console.ReadLine();
    if (string.IsNullOrWhiteSpace(input)) break; 

    using var streamerCallback = new StreamerCallback((string chunk) =>
    {
        Console.Write(chunk); 
    });

    pipeline.GenerateStream(input, generationConfig, streamerCallback);
    
    input = null;
    Console.WriteLine("\n----------\nquestion:");
}

pipeline.FinishChat(); // Finish chat and clear history in kv cache.

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About Deployment

We can directly download the OpenVINO official release of the LLM's IR from Hugging Face using this link.

git clone https://huggingface.co/OpenVINO/Phi-3.5-mini-instruct-int8-ov

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The OpenVINO.GenAI 2025.1 package can be downloaded via this link.

The C# project directly depends on openvino_genai_c.dll, which in turn has transitive dependencies on other toolkit-related DLLs, including Intel TBB libraries.

To ensure proper runtime behavior, all the DLLs delivered with OpenVINO.GenAI — including openvino_genai_c.dll and its dependencies — are bundled and treated as part of the C# project’s runtime dependencies.

We use the following cmd commands to download the genai package and copy all the required dependent DLLs to the directory containing the *.csproj file.

curl -O https://storage.openvinotoolkit.org/repositories/openvino_genai/packages/2025.1/windows/openvino_genai_windows_2025.1.0.0_x86_64.zip
tar -xzvf openvino_genai_windows_2025.1.0.0_x86_64.zip
xcopy /y openvino_genai_windows_2025.1.0.0_x86_64\runtime\bin\intel64\Release\*.dll "C:\path\to\ChatSample\"
xcopy /y openvino_genai_windows_2025.1.0.0_x86_64\runtime\3rdparty\tbb\bin\*.dll "C:\path\to\ChatSample\"

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Full Implementation

Please refer to https://github.com/apinge/openvino_ai_practice/tree/main/ov_genai_interop/ov_genai_interop_net, to access the full implementation.

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Authors: Hongbo Zhao, Fiona Zhao, Tong Qiu

Why Choose the Ollama + OpenVINO Combination?

Dual-Engine Driven Technical Advantages

The integration of Ollama and OpenVINO delivers a powerful dual-engine solution for the management and inference of large language models (LLMs). Ollama offers a streamlined model management toolchain, while OpenVINO provides efficient acceleration capabilities for model inference across Intel hardware (CPU/GPU/NPU). This combination not only simplifies the deployment and invocation of models but also significantly enhances inference performance, making it particularly suitable for scenarios demanding high performance and ease of use.

You can find more information on github repository:

https://github.com/openvinotoolkit/openvino_contrib/tree/master/modules/ollama_openvino

Core Value of Ollama

    1. Streamlined LLM Management Toolchain: Ollama provides a user-friendly command-line interface, enabling users to effortlessly download, manage, and run various LLM models.

    2. One-Click Model Deployment: With simple commands, users can quickly deploy and invoke models without complex configurations.

    3. Unified API Interface: Ollama offers a unified API interface, making it easy for developersto integrate into various applications.

    4. Active Open-Source Community: Ollama boasts a vibrant open-source community, providing users with abundant resources and support.

Limitations of Ollama

Currently, Ollama only supports llama.cpp as itsbackend, which presents some inconveniences:

    1. Limited Hardware Compatibility: llama.cpp is primarily optimized for CPUs and NVIDIA GPUs, and cannot fully leverage the acceleration capabilities of Intel GPUs or NPUs, resulting in suboptimal performance in high-performance computing scenarios.

    2. Performance Bottlenecks: For large-scale models or high-concurrency scenarios, the performance of llama.cpp may fall short, especially when handling complex tasks, leading to slower inference speeds.

Breakthrough Capabilities of OpenVINO

    1. Deep Optimization for Intel Hardware (CPU/iGPU/Arc dGPU/NPU): OpenVINO is deeply optimized for Intel hardware, fully leveraging the performance potential of CPUs, iGPUs, dGPUs, and NPUs.

    2. Cross-Platform Heterogeneous Computing Support: OpenVINO supports cross-platform heterogeneous computing, enabling efficient model inference across different hardware platforms.

    3. Model Quantization and Compression Toolchain: OpenVINO provides a comprehensive toolchain for model quantization and compression, significantly reducing model size and improving inference speed.

    4. Significant Inference Performance Improvement: Through OpenVINO's optimizations, model inference performance can be significantly enhanced, especially for large-scale models and high-concurrency scenarios.

    5. Extensibility and Flexibility Support: OpenVINO GenAI offers robust extensibility and flexibility for Ollama-OV, supporting pipeline optimization techniques such as speculative decoding, prompt-lookup decoding, pipeline parallelization, and continuous batching, laying a solid foundation for future pipeline serving optimizations.

Developer Benefits of Integration

    1. Simplified Development Experience: Retains Ollama's CLI interaction features, allowing developers to continue using familiar command-line tools for model management and invocation.

    2. Performance Leap: Achieves hardware-level acceleration through OpenVINO, significantly boosting model inference performance, especially for large-scale models and high-concurrency scenarios.

    3. Multi-Hardware Adaptation and Ecosystem Expansion: OpenVINO's support enables Ollama to adapt to multiple hardware platforms, expanding its application ecosystem and providing developers with more choices and flexibility.

Three Steps to Enable Acceleration

    1. Download Precompiled Executables

       please refer to : https://github.com/zhaohb/ollama_ov/tree/main?tab=readme-ov-file#google-driver

    2.Configure OpenVINO GenAI Environment

        For Windows systems, first extract the downloaded OpenVINO GenAI package to the directory openvino_genai_windows_2025.2.0.0.dev20250320_x86_64, then execute the following commands:

cd openvino_genai_windows_2025.2.0.0.dev20250320_x86_64
setupvars.bat

    3. Set Up cgocheck

      Windows:

           set GODEBUG=cgocheck=0

      Linux:

          export GODEBUG=cgocheck=0

At this point, the executable files have been downloaded, and the OpenVINO GenAI, OpenVINO, and CGO environments have been successfully configured.

Custom Model Deployment Guide

Since the Ollama Model Library does not support uploading non-GGUF format IR models, we will create an OCI image locally using OpenVINO IR that is compatible with Ollama. Here, we use the DeepSeek-R1-Distill-Qwen-7B model as an example:

    1. Download the OpenVINO IR Model

        Download the model from ModelScope:

pip install modelscope
modelscope download --model zhaohb/DeepSeek-R1-Distill-Qwen-7B-int4-ov --local_dir ./DeepSeek-R1-Distill-Qwen-7B-int4-ov

    2. Package the Downloaded OpenVINO IR Directory

        Compress the directory into a *.tar.gz file:

tar -zcvf DeepSeek-R1-Distill-Qwen-7B-int4-ov.tar.gz DeepSeek-R1-Distill-Qwen-7B-int4-ov

    3. Create a Modelfile

        Define the model configuration in a Modelfile:

FROM DeepSeek-R1-Distill-Qwen-7B-int4-ov.tar.gz
ModelType "OpenVINO"
InferDevice "GPU"
PARAMETER stop ""
PARAMETER stop "```"
PARAMETER stop "</User|>"
PARAMETER stop "<|end_of_sentence|>"
PARAMETER stop "</｜"
PARAMETER max_new_token 4096
PARAMETER stop_id 151643
PARAMETER stop_id 151647
PARAMETER repeat_penalty 1.5
PARAMETER top_p 0.95
PARAMETER top_k 50
PARAMETER temperature 0.8

    4. Create an Ollama-Compatible Model

        Use the Modelfile to create a model supported by Ollama:

ollama create DeepSeek-R1-Distill-Qwen-7B-int4-ov:v1 -f Modelfile

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With these steps, we have successfully created the DeepSeek-R1-Distill-Qwen-7B-int4-ov:v1 model, which is now ready for use with the Ollama OpenVINO backend.

1. Introduction

Janus is a unified multimodal understanding and generation model developed by DeepSeek. Janus proposed decoupling visual encoding to alleviate the conflict between multimodal understanding and generation tasks. Janus-Pro further scales up the Janus model to larger model size (deepseek-ai/Janus-Pro-1B &  deepseek-ai/Janus-Pro-7B) with optimized training strategy and training data, achieving significant advancements in both multimodal understanding and text-to-image tasks.

Figure 1 shows the architecture of Janus-Pro, which decouples visual encoding for multimodal understanding and visual generation. “Und. Encoder” and “Gen. Encoder” are abbreviations for “Understanding Encoder” and “Generation Encoder”.  For the multimodal understanding task, SigLIP vision encoder used to extract high-dimensional semantic features from the image, while for the vision generation task, VQ tokenizer used to map images to discrete IDs. Both the understanding adaptor and the generation adaptor are two-layer MLPs to map the embeddings to the input space of LLM.

In this blog, we will introduce how to deploy Janus-Pro model with OpenVINO^TM runtime on the intel platform.

2. Janus-Pro Pytorch Model to OpenVINO^TM Model Conversion

2.1. Setup Python Environment

$ git clone https://github.com/sammysun0711/openvino_aigc_samples.git
$ cd openvino_aigc_samples/Janus
$ conda create -n janus-ov python=3.10
$ conda activate janus-ov
$ pip install -r requirements.txt

2.2 Download Janus Pytorch model (Optional)

$ modelscope download --model deepseek-ai/Janus-Pro-1B --local_dir Janus-Pro-1B

2.3. Convert Pytorch Model to OpenVINO^TM INT4 Model

$ python convert_janus.py -m Janus-Pro-1B -o Janus-Pro-1B-OV

The converted OpenVINO will be saved in Janus-Pro-1B-OV directory for deployment.

3.  Janus-Pro Inference with OpenVINO^TM Demo

In this section, we provide several examples to show Janus-Pro for multimodal understanding and vision generation tasks.

3.1. Multimodal Understanding Task – Image Caption with OpenVINO^TM

$ python run_janus_vlm_ov.py -m Janus-Pro-1B-OV -p "Describe image in det
ails" -i images/cat_in_box.png -d GPU

Prompt: Describe image in details

Input image:

Generated Output:

The image shows a gray tabby cat lying inside an open cardboard box on a carpeted floor. The cat is lying on its back with its belly exposed, looking relaxed and comfortable. The background includes a beige-colored sofa and a light-colored curtain,
 suggesting a cozy indoor setting. The cat's tail is curled around its body, and it appears to be enjoying itself.

3.2. Multimodal Understanding Task – Equation Description with OpenVINO^TM

$ python run_janus_vlm_ov.py -m Janus-Pro-1B-OV -p "Generate the latex code of this formula" -i images/equation.png -d GPU

Prompt: Generate the latex code of this formula

Input Image:

Generated Output:

\[ A_n = a_0 \begin{bmatrix} 1 & + \frac{3}{4} \sum_{k=1}^{n} \begin{bmatrix} 4 \\ 9 \end{bmatrix}^k \end{bmatrix} \]

This formula represents the cumulative sum of the first \( n \) terms of the sequence \( a_n \), where \( a_n \) is the sequence of \( a_0 \), \( \frac{3}{4} \), and \( \begin{bmatrix} 4 \\ 9 \end{bmatrix}^k \). The first term \( a_0 \) is the first term of the sequence, the second term is the sum of the first \( n-1 \) terms, and the third term is the sum of the first \( n \) terms. The sequence \( \begin{bmatrix} 4 \\ 9 \end{bmatrix}^k \) represents the \( k \)-th term of the sequence. The
 formula calculates the cumulative sum of the sequence \( a_n \).

This formula can be used to calculate the cumulative sum of the sequence \( a_n \) for any given \( n \).

3.3. Multimodal Understanding Task – Code Generation with OpenVINO^TM

$ python run_janus_vlm_ov.py -m Janus-Pro-1B-OV -p "Generate the matplotlib pyplot code for this plot" -i images\pie_chart.png -d GPU

Prompt: Generate the matplotlib pyplot code for this plot

Input Image:

Generated Output:

The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
Sure, here is the code to generate the matplotlib plot based on the information in the image:

```python
import matplotlib.pyplot as plt

# Data
data = {
    'Hogs': 30,
    'Frogs': 15,
    'Logs': 10,
    'Dogs': 45
}

# Create the plot
plt.figure(figsize=(10, 6))

# Pie chart
for key, value in data.items():
    # Calculate the percentage of the pie chart
    percentage = value / 100

    # Add the pie slice
    plt.pie(percentage, labels=data.keys(), autopct='%1.1f%%')

# Title and labels
plt.title('Pie chart')
plt.xlabel('Categories')
plt.ylabel('Percentage')

# Show the plot
plt.show()
```

This code will create a pie chart based on the data provided in the image.

3.4. Vision Generation Task with OpenVINO^TM

$ python run_janus_t2i_ov.py -m Janus-Pro-1B-OV -d GPU

Input Prompt: A cute and adorable baby fox with big brown eyes, autumn leaves in the background enchanting, immortal, fluffy, shiny mane, Petals, fairyism, unreal engine 5 and Octane Render, highly detailed, photorealistic, cinematic, natural colors.

Generated image:

4. Performance Evaluation & Memory Usage Analysis

We also provide benchmark scripts to evaluate Janus-Pro model performance and memory usage with OpenVINO^TM inference, you may specify model name and device for your target platform.

4.1. Benchmark Janus-Pro for Multimodal Understanding Task with OpenVINO^TM

$ python benchmark_janus_vlm_ov.py -m Janus-Pro-1B-OV -d GPU

Here are some arguments for benchmark script for Multimodal Understanding Task:

--model_id: specify the Janus OpenVINO^TM model directory

--prompt: specify input prompt for multimodal understanding task

--image_path: specify input image for multimodal understanding task

--niter:  specify number of test iteration, default is 5

--device: specify which device to run inference

--max_new_tokens: specify max number of generated tokens

By default, the benchmark script will run 5 round multimodal understanding tasks on target device, then report pipeline initialization time, average first token latency (including preprocessing), 2^nd+ token throughput and max RSS memory usage.

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4.2. Benchmark Janus-Pro for Text-to-Image Task with OpenVINO^TM

$ python benchmark_janus_t2i_ov.py -m Janus-Pro-1B-OV -d GPU

Here are some arguments for benchmark scripts for Text-to-Image Task

--model_id: specify the Janus OpenVINO ^TM model directory

--prompt: specify input prompt for text-to-image generation task

--niter:  specify number of test iteration

--device: specify which device to run inference

By default, the benchmark script will run 5 round image generation tasks on target device, then report the pipeline initialization time, average image generation latency and max RSS memory usage.

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5. Conclusion

In this blog, we introduced how to enable Janus-Pro model with OpenVINO^TM runtime, then we demonstrated the Janus-Pro capability for various multimodal understanding and image generation tasks. In the end, we provide python script for performance & memory usage evaluation for both multimodal understanding and image generation task on target platform.