ElBruno.LocalLLMs

ElBruno.LocalLLMs

Run local LLMs in .NET through IChatClient 🧠

Run local LLMs in .NET through IChatClient — the same interface you'd use for Azure OpenAI, Ollama, or any other provider. Powered by ONNX Runtime GenAI.

Features

• 🔌 IChatClient implementation — seamless integration with Microsoft.Extensions.AI
• 📦 Automatic model download — models are fetched from HuggingFace on first use
• 🚀 Zero friction — works out of the box with sensible defaults (Phi-3.5 mini)
• 🖥️ Multi-hardware — CPU, CUDA, and DirectML execution providers
• 💉 DI-friendly — register with AddLocalLLMs() in ASP.NET Core
• 🔄 Streaming — token-by-token streaming via GetStreamingResponseAsync
• 📊 Multi-model — switch between Phi-3.5, Phi-4, Qwen2.5, Llama 3.2, and more
• 🎯 Fine-tuned models — pre-trained Qwen2.5 variants for tool calling and RAG (guide)

Installation

dotnet add package ElBruno.LocalLLMs

Then add one runtime package depending on your target hardware:

# 🖥️ CPU (works everywhere — required for CPU-only apps):
dotnet add package Microsoft.ML.OnnxRuntimeGenAI

# 🟢 NVIDIA GPU (CUDA):
dotnet add package Microsoft.ML.OnnxRuntimeGenAI.Cuda

# 🔵 Any Windows GPU — AMD, Intel, NVIDIA (DirectML):
dotnet add package Microsoft.ML.OnnxRuntimeGenAI.DirectML

⚠️ Add exactly one runtime package. Do not reference both Microsoft.ML.OnnxRuntimeGenAI and Microsoft.ML.OnnxRuntimeGenAI.Cuda simultaneously — the native binaries conflict and GPU support will silently fail.

🚀 The library defaults to ExecutionProvider.Auto — it tries GPU first and falls back to CPU automatically. No code changes needed.

Quick Start

using ElBruno.LocalLLMs;
using Microsoft.Extensions.AI;

// Create a local chat client (downloads Phi-3.5 mini on first run)
using var client = await LocalChatClient.CreateAsync();

var response = await client.GetResponseAsync([
    new(ChatRole.User, "What is the capital of France?")
]);

Console.WriteLine(response.Text);

Streaming

using ElBruno.LocalLLMs;
using Microsoft.Extensions.AI;

using var client = await LocalChatClient.CreateAsync(new LocalLLMsOptions
{
    Model = KnownModels.Phi35MiniInstruct
});

await foreach (var update in client.GetStreamingResponseAsync([
    new(ChatRole.System, "You are a helpful assistant."),
    new(ChatRole.User, "Explain quantum computing in simple terms.")
]))
{
    Console.Write(update.Text);
}

Model Metadata

Inspect model capabilities at runtime — context window size, model name, and vocabulary:

using var client = await LocalChatClient.CreateAsync();

var metadata = client.ModelInfo;
Console.WriteLine($"Model:          {metadata?.ModelName}");
Console.WriteLine($"Context window: {metadata?.MaxSequenceLength}");
Console.WriteLine($"Vocab size:     {metadata?.VocabSize}");

This is useful for prompt-length validation, adaptive chunking, and model selection logic.

Dependency Injection

builder.Services.AddLocalLLMs(options =>
{
    options.Model = KnownModels.Phi35MiniInstruct;
    options.ExecutionProvider = ExecutionProvider.DirectML;
});

// Inject IChatClient anywhere
public class MyService(IChatClient chatClient) { ... }

Supported Models

Fine-Tuned Models

Pre-trained variants optimized for specific tasks. A fine-tuned 0.5B model often matches or exceeds a base 1.5B on its specialized task.

See the Supported Models Guide for detailed model cards, performance benchmarks, and selection guidance.

Samples

Requirements

• .NET 8.0 or .NET 10.0
• CPU (default), NVIDIA GPU (CUDA), or Windows GPU (DirectML)
• ~2-8 GB disk space per model (depending on size and quantization)

Building from Source

git clone https://github.com/elbruno/ElBruno.LocalLLMs.git
cd ElBruno.LocalLLMs
dotnet restore ElBruno.LocalLLMs.slnx
dotnet build ElBruno.LocalLLMs.slnx
dotnet test ElBruno.LocalLLMs.slnx --framework net8.0

Documentation

• Getting Started — installation, first steps, configuration
• Supported Models — full model reference with tiers, specs, decision tree
• Architecture — design decisions and internal structure
• Samples Guide — walkthrough of each sample application
• Benchmarks — how to run and interpret performance benchmarks
• Fine-Tuning Guide — using and training fine-tuned models
• ONNX Conversion — converting HuggingFace models to ONNX format
• Publishing — NuGet package publishing with OIDC
• Contributing — how to contribute
• Changelog — version history

🤝 Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License — see the LICENSE file for details.

👋 About the Author

Made with ❤️ by Bruno Capuano (ElBruno)

• 📝 Blog: elbruno.com
• 📺 YouTube: youtube.com/@inthelabs
• 🔗 LinkedIn: linkedin.com/in/inthelabs
• 𝕏 Twitter: twitter.com/inthelabs
• 🎙️ Podcast: inthelabs.dev

🙏 Acknowledgments

• ONNX Runtime GenAI — inference engine
• Microsoft.Extensions.AI — IChatClient interface
• Hugging Face — model hosting and community