How to Use ML.NET and DeepSeek
for Fast AI Development

Integrating DeepSeek models directly with ML.NET is not straightforward, as there is no readily available sample code demonstrating this integration. However, you can achieve this by leveraging ML.NET's support for TensorFlow models. Here's a step-by-step guide to get you started:

1. Export the DeepSeek Model

Ensure that your DeepSeek model is saved in a format compatible with TensorFlow, such as the .pb (Protocol Buffers) format. You can use the DeepSeek GitHub repository to explore sample models and save them appropriately.

2. Set Up ML.NET to Use TensorFlow

ML.NET provides built-in support for TensorFlow models. First, ensure you have the necessary NuGet packages installed:

    Install-Package Microsoft.ML
    Install-Package Microsoft.ML.TensorFlow

3. Code Example

Here is an example of how to load and use a DeepSeek model with ML.NET:

    using Microsoft.ML;
    using Microsoft.ML.Data;

    // Define input and output schemas
    public class ModelInput
    {
        [VectorType(784)]
        public float[] Input { get; set; }
    }

    public class ModelOutput
    {
        [VectorType(10)]
        public float[] Output { get; set; }
    }

    class Program
    {
        static void Main(string[] args)
        {
            MLContext mlContext = new MLContext();

            // Load TensorFlow model
            var pipeline = mlContext.Model.LoadTensorFlowModel("path/to/deepseek-model.pb")
                .ScoreTensorName("output_tensor")
                .AddInput("input_tensor", input => input.Input);

            // Prepare input data
            IDataView inputData = mlContext.Data.LoadFromEnumerable(new List
            {
                new ModelInput { Input = new float[784] }
            });

            // Transform data
            var transformedData = pipeline.Fit(inputData).Transform(inputData);

            // Get predictions
            var predictions = mlContext.Data.CreateEnumerable(transformedData, reuseRowObject: false);

            foreach (var prediction in predictions)
            {
                Console.WriteLine(string.Join(", ", prediction.Output));
            }
        }
    }

4. Fine-Tuning and Testing

After loading the model, you can fine-tune it further or use it as-is for inference. The DeepSeek GitHub repository also includes examples of training models that can later be exported for use with ML.NET.

5. Additional Resources

DeepSeek GitHub Repository: Explore pre-trained models and training examples.
ML.NET Documentation: Learn more about ML.NET capabilities and TensorFlow integration.