C# 下的LLamaSharp: 高效的本地LLM推理库，自己写GPT-51CTO.COM

LLamaSharp 是一个跨平台库，用于在本地设备上运行 LLaMA/LLaVA 模型（以及其他模型）。基于 llama.cpp，LLamaSharp 在 CPU 和 GPU 上的推理都非常高效。通过高级 API 和 RAG 支持，您可以方便地在应用程序中部署大型语言模型（LLM）。

GitHub 地址

https://github.com/SciSharp/LLamaSharp

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下载代码：

git clone https://github.com/SciSharp/LLamaSharp.git

快速开始

安装

为了获得高性能，LLamaSharp 与从 C++ 编译的本地库交互，这些库称为 backends。我们为 Windows、Linux 和 Mac 提供了 CPU、CUDA、Metal 和 OpenCL 的后端包。您不需要编译任何 C++ 代码，只需安装后端包即可。

安装 LLamaSharp 包：

PM> Install-Package LLamaSharp

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安装一个或多个后端包，或使用自编译的后端：

LLamaSharp.Backend.Cpu: 适用于 Windows、Linux 和 Mac 的纯 CPU 后端。支持 Mac 的 Metal (GPU)。
LLamaSharp.Backend.Cuda11: 适用于 Windows 和 Linux 的 CUDA 11 后端。
LLamaSharp.Backend.Cuda12: 适用于 Windows 和 Linux 的 CUDA 12 后端。
LLamaSharp.Backend.OpenCL: 适用于 Windows 和 Linux 的 OpenCL 后端。

（可选）对于 Microsoft semantic-kernel 集成，安装 LLamaSharp.semantic-kernel 包。

（可选）要启用 RAG 支持，安装 LLamaSharp.kernel-memory 包（该包仅支持 net6.0 或更高版本），该包基于 Microsoft kernel-memory 集成。

模型准备

LLamaSharp 使用 GGUF 格式的模型文件，可以从 PyTorch 格式（.pth）和 Huggingface 格式（.bin）转换而来。获取 GGUF 文件有两种方式：

在 Huggingface 搜索模型名称 + 'gguf'，找到已经转换好的模型文件。
自行将 PyTorch 或 Huggingface 格式转换为 GGUF 格式。请按照 llama.cpp readme 中的说明使用 Python 脚本进行转换。

一般来说，我们推荐下载带有量化的模型，因为它显著减少了所需的内存大小，同时对生成质量的影响很小。

简单对话

LLamaSharp 提供了一个简单的控制台演示，展示了如何使用该库进行推理。以下是一个基本示例：

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using LLama.Common;
using LLama;


namespace appLLama
{
    internal class Program
    {
        static void Main(string[] args)
        {
            Chart();
        }


        static async Task Chart()
        {
            string modelPath = @"E:\Models\llama-2-7b-chat.Q4_K_M.gguf"; // change it to your own model path.


            var parameters = new ModelParams(modelPath)
            {
                ContextSize = 1024, // The longest length of chat as memory.
                GpuLayerCount = 5 // How many layers to offload to GPU. Please adjust it according to your GPU memory.
            };
            using var model = LLamaWeights.LoadFromFile(parameters);
            using var context = model.CreateContext(parameters);
            var executor = new InteractiveExecutor(context);


            // Add chat histories as prompt to tell AI how to act.
            var chatHistory = new ChatHistory();
            chatHistory.AddMessage(AuthorRole.System, "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.");
            chatHistory.AddMessage(AuthorRole.User, "Hello, Bob.");
            chatHistory.AddMessage(AuthorRole.Assistant, "Hello. How may I help you today?");


            ChatSession session = new(executor, chatHistory);


            InferenceParams inferenceParams = new InferenceParams()
            {
                MaxTokens = 256, // No more than 256 tokens should appear in answer. Remove it if antiprompt is enough for control.
                AntiPrompts = new List<string> { "User:" } // Stop generation once antiprompts appear.
            };


            Console.ForegroundColor = ConsoleColor.Yellow;
            Console.Write("The chat session has started.\nUser: ");
            Console.ForegroundColor = ConsoleColor.Green;
            string userInput = Console.ReadLine() ?? "";


            while (userInput != "exit")
            {
                await foreach ( // Generate the response streamingly.
                    var text
                    in session.ChatAsync(
                        new ChatHistory.Message(AuthorRole.User, userInput),
                        inferenceParams))
                {
                    Console.ForegroundColor = ConsoleColor.White;
                    Console.Write(text);
                }
                Console.ForegroundColor = ConsoleColor.Green;
                userInput = Console.ReadLine() ?? "";
            }
        }
    }
}

模型路径与参数设置：指定模型路径，以及上下文的大小和 GPU 层的数量。
加载模型并创建上下文：从文件中加载模型，并使用参数初始化上下文。
执行器与对话历史记录：定义一个 InteractiveExecutor，并设置初始的对话历史，包括系统消息和用户与助手的初始对话。
会话与推理参数：建立对话会话 ChatSession，设置推理参数，包括最大 token 数和反提示语。
用户输入与生成回复：开始聊天会话并处理用户输入，使用异步方法流式地生成助手的回复，并根据反提示语停止生成。

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你会发现中文支持不太好，即使用了千问的量化库。

中文处理官方例子

我这换成了千问的库：

using LLama.Common;
using LLama;
using System.Text;


namespace appLLama
{
    internal class Program
    {
        static void Main(string[] args)
        {
            // Register provider for GB2312 encoding
            Encoding.RegisterProvider(CodePagesEncodingProvider.Instance);
            Run();
        }


        private static string ConvertEncoding(string input, Encoding original, Encoding target)
        {
            byte[] bytes = original.GetBytes(input);
            var convertedBytes = Encoding.Convert(original, target, bytes);
            return target.GetString(convertedBytes);
        }


        public static async Task Run()
        {
            // Register provider for GB2312 encoding
            Encoding.RegisterProvider(CodePagesEncodingProvider.Instance);


            Console.ForegroundColor = ConsoleColor.Yellow;
            Console.WriteLine("This example shows how to use Chinese with gb2312 encoding, which is common in windows. It's recommended" +
                " to use https://huggingface.co/hfl/chinese-alpaca-2-7b-gguf/blob/main/ggml-model-q5_0.gguf, which has been verified by LLamaSharp developers.");
            Console.ForegroundColor = ConsoleColor.White;


            string modelPath = @"E:\LMModels\ay\Repository\qwen1_5-7b-chat-q8_0.gguf";// @"E:\Models\llama-2-7b-chat.Q4_K_M.gguf";


            var parameters = new ModelParams(modelPath)
            {
                ContextSize = 1024,
                Seed = 1337,
                GpuLayerCount = 5,
                Encoding = Encoding.UTF8
            };
            using var model = LLamaWeights.LoadFromFile(parameters);
            using var context = model.CreateContext(parameters);
            var executor = new InteractiveExecutor(context);


            ChatSession session;
            ChatHistory chatHistory = new ChatHistory();


            session = new ChatSession(executor, chatHistory);


            session
                .WithHistoryTransform(new LLamaTransforms.DefaultHistoryTransform());


            InferenceParams inferenceParams = new InferenceParams()
            {
                Temperature = 0.9f,
                AntiPrompts = new List<string> { "用户：" }
            };


            Console.ForegroundColor = ConsoleColor.Yellow;
            Console.WriteLine("The chat session has started.");


            // show the prompt
            Console.ForegroundColor = ConsoleColor.White;
            Console.Write("用户：");
            Console.ForegroundColor = ConsoleColor.Green;
            string userInput = Console.ReadLine() ?? "";


            while (userInput != "exit")
            {
                // Convert the encoding from gb2312 to utf8 for the language model
                // and later saving to the history json file.
                userInput = ConvertEncoding(userInput, Encoding.GetEncoding("gb2312"), Encoding.UTF8);


                if (userInput == "save")
                {
                    session.SaveSession("chat-with-kunkun-chinese");
                    Console.ForegroundColor = ConsoleColor.Yellow;
                    Console.WriteLine("Session saved.");
                }
                else if (userInput == "regenerate")
                {
                    Console.ForegroundColor = ConsoleColor.Yellow;
                    Console.WriteLine("Regenerating last response ...");


                    await foreach (
                        var text
                        in session.RegenerateAssistantMessageAsync(
                            inferenceParams))
                    {
                        Console.ForegroundColor = ConsoleColor.White;


                        // Convert the encoding from utf8 to gb2312 for the console output.
                        Console.Write(ConvertEncoding(text, Encoding.UTF8, Encoding.GetEncoding("gb2312")));
                    }
                }
                else
                {
                    await foreach (
                        var text
                        in session.ChatAsync(
                            new ChatHistory.Message(AuthorRole.User, userInput),
                            inferenceParams))
                    {
                        Console.ForegroundColor = ConsoleColor.White;
                        Console.Write(text);
                    }
                }


                Console.ForegroundColor = ConsoleColor.Green;
                userInput = Console.ReadLine() ?? "";


                Console.ForegroundColor = ConsoleColor.White;
            }
        }
    }
}

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Winform写一个简单例子

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Chat类：

public class Chat
{
    ChatSession session;




    InferenceParams inferenceParams = new InferenceParams()
        {
            Temperature = 0.9f,
            AntiPrompts = new List<string> { "用户：" }
        };


    private  string ConvertEncoding(string input, Encoding original, Encoding target)
        {
            byte[] bytes = original.GetBytes(input);
            var convertedBytes = Encoding.Convert(original, target, bytes);
            return target.GetString(convertedBytes);
        }


    public void Init()
        {
            // Register provider for GB2312 encoding
            Encoding.RegisterProvider(CodePagesEncodingProvider.Instance);


            Console.ForegroundColor = ConsoleColor.Yellow;
            Console.WriteLine("This example shows how to use Chinese with gb2312 encoding, which is common in windows. It's recommended" +
                " to use https://huggingface.co/hfl/chinese-alpaca-2-7b-gguf/blob/main/ggml-model-q5_0.gguf, which has been verified by LLamaSharp developers.");
            Console.ForegroundColor = ConsoleColor.White;


            string modelPath = @"E:\LMModels\ay\Repository\qwen1_5-7b-chat-q8_0.gguf";// @"E:\Models\llama-2-7b-chat.Q4_K_M.gguf";


            var parameters = new ModelParams(modelPath)
            {
                ContextSize = 1024,
                Seed = 1337,
                GpuLayerCount = 5,
                Encoding = Encoding.UTF8
            };


            var model = LLamaWeights.LoadFromFile(parameters);
            var context = model.CreateContext(parameters);
            var executor = new InteractiveExecutor(context);
            var chatHistory = new ChatHistory();
            session = new ChatSession(executor, chatHistory);
            session
                .WithHistoryTransform(new LLamaTransforms.DefaultHistoryTransform());
        }


    public async Task Run(string userInput,Action<string> callback)
        {
            while (userInput != "exit")
            {
                userInput = ConvertEncoding(userInput, Encoding.GetEncoding("gb2312"), Encoding.UTF8);


                if (userInput == "save")
                {
                    session.SaveSession("chat-with-kunkun-chinese");


                }
                else if (userInput == "regenerate")
                {
                    await foreach (
                        var text
                        in session.RegenerateAssistantMessageAsync(
                            inferenceParams))
                    {
                        callback(ConvertEncoding(text, Encoding.UTF8, Encoding.GetEncoding("gb2312")));
                    }
                }
                else
                {
                    await foreach (
                        var text
                        in session.ChatAsync(
                            new ChatHistory.Message(AuthorRole.User, userInput),
                            inferenceParams))
                    {
                        callback(text);
                    }
                }


                userInput = "";
            }
        }
}

Form1界面事件：

public partial class Form1 : Form
{
    Chat chat = new Chat();
    public Form1()
    {
        InitializeComponent();
        Encoding.RegisterProvider(CodePagesEncodingProvider.Instance);
        chat.Init();
    }


    private  void btnSend_Click(object sender, EventArgs e)
    {
        var call = new Action<string>(x =>
        {
            this.Invoke(() =>
            {
                txtLog.AppendText(x);
            });
        });
        //chat.Run(txtMsg.Text, call);


        Task.Run(() =>
        {
            chat.Run(txtMsg.Text, call);
        });


    }
}

更新例子可以去官网上看，写的比较专业。

https://scisharp.github.io/LLamaSharp/0.13.0/

C# 下的LLamaSharp: 高效的本地LLM推理库，自己写GPT