# LLamaSharp - .NET Binding for llama.cpp

![logo](Assets/LLamaSharpLogo.png)

[![Discord](https://img.shields.io/discord/1106946823282761851?label=Discord)](https://discord.gg/M2fS4PNj)
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The C#/.NET binding of [llama.cpp](https://github.com/ggerganov/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

## Installation

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
```

The latest version of `LLamaSharp` and `LLamaSharp.Backend` may not always be the same. `LLamaSharp.Backend` follows up [llama.cpp](https://github.com/ggerganov/llama.cpp) because sometimes the 
break change of it makes some model weights invalid. If you are not sure which version of backend to install, just install the latest version.

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](https://github.com/ggerganov/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.

## FAQ

1. GPU out of memory: v0.2.3 put all layers into GPU by default. If the momory use is out of the capacity of your GPU, please set `n_gpu_layers` to a smaller number.
2. Unsupported model: `llama.cpp` is under quick development and often has break changes. Please check the release date of the model and find a suitable version of LLamaSharp to install.

## Simple Benchmark

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`. 

#### Windows

- llama.cpp: 2.98 words / second

- LLamaSharp: 2.94 words / second

## Usages

#### Model Inference and Chat Session

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](https://github.com/ggerganov/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](./LLama.Examples).

```cs

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);
    }
}
```

#### Quantization

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#.

```cs
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](./LLama.Examples).

#### Web API

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

## Demo

![demo-console](Assets/console_demo.gif)

## Roadmap

✅ LLaMa model inference.

✅ Embeddings generation.

✅ Chat session.

✅ Quantization

✅ ASP.NET core Integration

🔳 UI Integration

🔳 Follow up llama.cpp and improve performance

## Assets

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](https://huggingface.co/eachadea/ggml-vicuna-13b-1.1/tree/main)
- [TheBloke/wizardLM-7B-GGML](https://huggingface.co/TheBloke/wizardLM-7B-GGML)
- Magnet: [magnet:?xt=urn:btih:b8287ebfa04f879b048d4d4404108cf3e8014352&dn=LLaMA](magnet:?xt=urn:btih:b8287ebfa04f879b048d4d4404108cf3e8014352&dn=LLaMA)

The weights included in the magnet is exactly the weights from [Facebook LLaMa](https://github.com/facebookresearch/llama).

The prompts could be found below:

- [llama.cpp prompts](https://github.com/ggerganov/llama.cpp/tree/master/prompts) 
- [ChatGPT_DAN](https://github.com/0xk1h0/ChatGPT_DAN)
- [awesome-chatgpt-prompts](https://github.com/f/awesome-chatgpt-prompts)
- [awesome-chatgpt-prompts-zh](https://github.com/PlexPt/awesome-chatgpt-prompts-zh) (Chinese)

## Contact us

Join our chat on [Discord](https://discord.gg/quBc2jrz).

## License

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