danshuitaihejie / llamasharp Goto Github PK

The C#/.NET binding of llama.cpp. It provides higher-level APIs to inference the LLaMA Models and deploy it on local device with C#/.NET. It works on Windows, Linux and Mac without need to compile llama.cpp yourself. Even without a GPU or not enough GPU memory, you can still use LLaMA models! 🤗

Furthermore, it provides integrations with other projects such as semantic-kernel, kernel-memory and BotSharp to provide higher-level applications.

Discussions about the roadmap to v1.0.0: #287

• Quick start
• Tricks for FAQ
• Full documentation
• API reference

• Official Console Examples
• Unity Demo
• LLamaStack (with WPF and Web support)

1. Install LLamaSharp package in NuGet:

PM> Install-Package LLamaSharp

2. Install one of these backends:

   • LLamaSharp.Backend.Cpu: Pure CPU for Windows & Linux. Metal for Mac.
   • LLamaSharp.Backend.Cuda11: CUDA11 for Windows and Linux
   • LLamaSharp.Backend.Cuda12@ CUDA 12 for Windows and Linux
   • If none of these backends is suitable you can compile llama.cpp yourself. In this case, please DO NOT install the backend packages! Instead, add your DLL to your project and ensure it will be copied to the output directory when compiling your project. If you do this you must use exactly the correct llama.cpp commit, refer to the version table further down.

3. (optional) For Microsoft semantic-kernel integration, install the LLamaSharp.semantic-kernel package.

4. (optional) For Microsoft kernel-memory integration, install the LLamaSharp.kernel-memory package (this package currently only supports net6.0).

Llama.cpp is a fast moving project with frequent breaking changes, therefore breaking changes are expected frequently in LLamaSharp. LLamaSharp follows semantic versioning and will not introduce breaking API changes on patch versions.

It is suggested to update to the latest patch version as soon as it is released, and to update to new major versions as soon as possible.

LLamaSharp provides two ways to run inference: LLamaExecutor and ChatSession. The chat session is a higher-level wrapping of the executor and the model. Here's a simple example to use chat session.

using LLama.Common;
using LLama;

string modelPath = "<Your model path>"; // change it to your own model path
var prompt = "Transcript of a dialog, where the User interacts with an Assistant named Bob. Bob is helpful, kind, honest, good at writing, and never fails to answer the User's requests immediately and with precision.\r\n\r\nUser: Hello, Bob.\r\nBob: Hello. How may I help you today?\r\nUser: Please tell me the largest city in Europe.\r\nBob: Sure. The largest city in Europe is Moscow, the capital of Russia.\r\nUser:"; // use the "chat-with-bob" prompt here.

// Load a model
var parameters = new ModelParams(modelPath)
{
    ContextSize = 1024,
    Seed = 1337,
    GpuLayerCount = 5
};
using var model = LLamaWeights.LoadFromFile(parameters);

// Initialize a chat session
using var context = model.CreateContext(parameters);
var ex = new InteractiveExecutor(context);
ChatSession session = new ChatSession(ex);

// show the prompt
Console.WriteLine();
Console.Write(prompt);

// run the inference in a loop to chat with LLM
while (prompt != "stop")
{
    await foreach (var text in session.ChatAsync(new ChatHistory.Message(AuthorRole.User, prompt), new InferenceParams { Temperature = 0.6f, AntiPrompts = [ "User:" ] }))
    {
        Console.Write(text);
    }
    prompt = Console.ReadLine() ?? "";
}

// save the session
session.SaveSession("SavedSessionPath");

The following example shows how to quantize the model:

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 usage, please refer to Examples.

We provide an integration with ASP.NET core and a web app demo. Since we are in short of hands, if you're familiar with ASP.NET core, we'll appreciate it if you would like to help upgrading the Web API integration.

✅: completed. ⚠️: outdated for latest release but will be updated. 🔳: not completed

✅ LLaMa model inference
✅ Embeddings generation, tokenization and detokenization
✅ Chat session
✅ Quantization
✅ Grammar
✅ State saving and loading
✅ BotSharp Integration Online Demo
✅ ASP.NET core Integration
✅ Semantic-kernel Integration
🔳 Fine-tune
✅ Local document search (enabled by kernel-memory)
🔳 MAUI Integration

1. GPU out of memory: Please try setting n_gpu_layers to a smaller number.
2. Unsupported model: llama.cpp is under quick development and often has breaking changes. Please check the release date of the model and find a suitable version of LLamaSharp to install, or generate gguf format weights from original weights yourself.
3. Cannot load native library:
   • Ensure you have installed one of the backend packages.
   • Run NativeLibraryConfig.WithLogs() at the very beginning of your code to print more information.
4. Models in GGUF format are compatible with LLamaSharp. It's a good idea to search for gguf on huggingface to find a model. Another choice is generate a GGUF format file yourself, please refer to convert.py for more information.

Any contribution is welcomed! There's a TODO list in LLamaSharp Dev Project and you could pick an interesting one to start. Please read the contributing guide for more information.

You can also do one of the followings to help us make LLamaSharp better:

• Submit a feature request.
• Star and share LLamaSharp to let others know it.
• Write a blog or demo about LLamaSharp.
• Help to develop Web API and UI integration.
• Just open an issue about the problem you met!

Join our chat on Discord (please contact Rinne to join the dev channel if you want to be a contributor).

Join QQ group

If you want to compile llama.cpp yourself you must use the exact commit ID listed for each version.

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