The C#/.NET binding of llama.cpp. It provides APIs to inference the LLaMa Models and deploy it on native environment or Web. It works on both Windows and Linux and does NOT require compiling llama.cpp yourself.

• Load and inference LLaMa models
• Simple APIs for chat session
• Quantize the model in C#/.NET
• ASP.NET core integration
• Native UI integration

Firstly, search LLamaSharp in nuget package manager and install it.

PM> Install-Package LLamaSharp

Then, search and install one of the following backends:

LLamaSharp.Backend.Cpu
LLamaSharp.Backend.Cuda11
LLamaSharp.Backend.Cuda12

Note that version v0.2.1 has a package named LLamaSharp.Cpu. After v0.2.2 it will be dropped.

We publish the backend with cpu, cuda11 and cuda12 because they are the most popular ones. If none of them matches, please compile the llama.cpp from source and put the libllama under your project's output path. When building from source, please add -DBUILD_SHARED_LIBS=ON to enable the library generation.

Currently it's only a simple benchmark to indicate that the performance of LLamaSharp is close to llama.cpp. Experiments run on a computer with Intel i7-12700, 3060Ti with 7B model. Note that the benchmark uses LLamaModel instead of LLamaModelV1.

• llama.cpp: 2.98 words / second

• LLamaSharp: 2.94 words / second

Currently, LLamaSharp provides two kinds of model, LLamaModelV1 and LLamaModel. Both of them works but LLamaModel is more recommended because it provides better alignment with the master branch of llama.cpp.

Besides, ChatSession makes it easier to wrap your own chat bot. The code below is a simple example. For all examples, please refer to Examples.

var model = new LLamaModel(new LLamaParams(model: "<Your path>", n_ctx: 512, repeat_penalty: 1.0f));
var session = new ChatSession<LLamaModel>(model).WithPromptFile("<Your prompt file path>")
                .WithAntiprompt(new string[] { "User:" });
Console.Write("\nUser:");
while (true)
{
    Console.ForegroundColor = ConsoleColor.Green;
    var question = Console.ReadLine();
    Console.ForegroundColor = ConsoleColor.White;
    var outputs = session.Chat(question); // It's simple to use the chat API.
    foreach (var output in outputs)
    {
        Console.Write(output);
    }
}

The following example shows how to quantize the model. With LLamaSharp you needn't to compile c++ project and run scripts to quantize the model, instead, just run it in C#.

string srcFilename = "<Your source path>";
string dstFilename = "<Your destination path>";
string ftype = "q4_0";
if(Quantizer.Quantize(srcFileName, dstFilename, ftype))
{
    Console.WriteLine("Quantization succeed!");
}
else
{
    Console.WriteLine("Quantization failed!");
}

For more usages, please refer to Examples.

We provide the integration of ASP.NET core here. Since currently the API is not stable, please clone the repo and use it. In the future we'll publish it on NuGet.

✅ LLaMa model inference.

✅ Embeddings generation.

✅ Chat session.

✅ Quantization

✅ ASP.NET core Integration

🔳 UI Integration

🔳 Follow up llama.cpp and improve performance

The model weights are too large to be included in the repository. However some resources could be found below:

• eachadea/ggml-vicuna-13b-1.1
• TheBloke/wizardLM-7B-GGML
• Magnet: magnet:?xt=urn:btih:b8287ebfa04f879b048d4d4404108cf3e8014352&dn=LLaMA

The weights included in the magnet is exactly the weights from Facebook LLaMa.

The prompts could be found below:

• llama.cpp prompts
• ChatGPT_DAN
• awesome-chatgpt-prompts
• awesome-chatgpt-prompts-zh (Chinese)

Join our chat on Discord.

This project is licensed under the terms of the MIT license.