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<h1 id="get-started">Get Started</h1>
<h2 id="install-packages">Install packages</h2>
<p>Firstly, search <code>LLamaSharp</code> in nuget package manager and install it.</p>
<pre><code>PM&gt; Install-Package LLamaSharp
</code></pre>
<p>Then, search and install one of the following backends:</p>
<pre><code>LLamaSharp.Backend.Cpu
LLamaSharp.Backend.Cuda11
LLamaSharp.Backend.Cuda12
</code></pre>
<p>Here's the mapping of them and corresponding model samples provided by <code>LLamaSharp</code>. If you're not sure which model is available for a version, please try our sample model.</p>
<table>
<thead>
<tr>
<th>LLamaSharp.Backend</th>
<th>LLamaSharp</th>
<th>Verified Model Resources</th>
<th>llama.cpp commit id</th>
</tr>
</thead>
<tbody>
<tr>
<td>-</td>
<td>v0.2.0</td>
<td>This version is not recommended to use.</td>
<td>-</td>
</tr>
<tr>
<td>-</td>
<td>v0.2.1</td>
<td><a href="https://huggingface.co/TheBloke/wizardLM-7B-GGML/tree/previous_llama">WizardLM</a>, <a href="https://huggingface.co/eachadea/ggml-vicuna-13b-1.1/tree/main">Vicuna (filenames with "old")</a></td>
<td>-</td>
</tr>
<tr>
<td>v0.2.2</td>
<td>v0.2.2, v0.2.3</td>
<td><a href="https://huggingface.co/TheBloke/wizardLM-7B-GGML/tree/previous_llama_ggmlv2">WizardLM</a>, <a href="https://huggingface.co/eachadea/ggml-vicuna-13b-1.1/tree/main">Vicuna (filenames without "old")</a></td>
<td>63d2046</td>
</tr>
<tr>
<td>v0.3.0</td>
<td>v0.3.0</td>
<td><a href="https://huggingface.co/AsakusaRinne/LLamaSharpSamples/tree/v0.3.0">LLamaSharpSamples v0.3.0</a>, <a href="https://huggingface.co/TheBloke/wizardLM-7B-GGML/tree/main">WizardLM</a></td>
<td>7e4ea5b</td>
</tr>
</tbody>
</table>
<h2 id="download-a-model">Download a model</h2>
<p>One of the following models could be okay:</p>
<ul>
<li>LLaMA 🦙</li>
<li><a href="https://github.com/ggerganov/llama.cpp#instruction-mode-with-alpaca">Alpaca</a></li>
<li><a href="https://github.com/ggerganov/llama.cpp#using-gpt4all">GPT4All</a></li>
<li><a href="https://github.com/ymcui/Chinese-LLaMA-Alpaca">Chinese LLaMA / Alpaca</a></li>
<li><a href="https://github.com/bofenghuang/vigogne">Vigogne (French)</a></li>
<li><a href="https://github.com/ggerganov/llama.cpp/discussions/643#discussioncomment-5533894">Vicuna</a></li>
<li><a href="https://bair.berkeley.edu/blog/2023/04/03/koala/">Koala</a></li>
<li><a href="https://github.com/OpenBuddy/OpenBuddy">OpenBuddy 🐶 (Multilingual)</a></li>
<li><a href="#using-pygmalion-7b--metharme-7b">Pygmalion 7B / Metharme 7B</a></li>
<li><a href="https://github.com/nlpxucan/WizardLM">WizardLM</a></li>
</ul>
<p><strong>Note that because <code>llama.cpp</code> is under fast development now and often introduce break changes, some model weights on huggingface which works under a version may be invalid with another version. If it's your first time to configure LLamaSharp, we'd like to suggest for using verified model weights in the table above.</strong></p>
<h2 id="run-the-program">Run the program</h2>
<p>Please create a console program with dotnet runtime &gt;= netstandard 2.0 (&gt;= net6.0 is more recommended). Then, paste the following code to <code>program.cs</code>;</p>
<pre><code class="language-cs">using LLama.Common;
using LLama;

string modelPath = &quot;&lt;Your model path&gt;&quot; // change it to your own model path
var prompt = &quot;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:&quot;; // use the &quot;chat-with-bob&quot; prompt here.

// Load model
var parameters = new ModelParams(modelPath)
{
    ContextSize = 1024
};
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 (true)
{
    foreach (var text in session.Chat(prompt, new InferenceParams() { Temperature = 0.6f, AntiPrompts = new List&lt;string&gt; { &quot;User:&quot; } }))
    {
        Console.Write(text);
    }

    Console.ForegroundColor = ConsoleColor.Green;
    prompt = Console.ReadLine();
    Console.ForegroundColor = ConsoleColor.White;
}
</code></pre>
<p>After starting it, you'll see the following outputs.</p>
<pre><code>Please input your model path: D:\development\llama\weights\wizard-vicuna-13B.ggmlv3.q4_1.bin
llama.cpp: loading model from D:\development\llama\weights\wizard-vicuna-13B.ggmlv3.q4_1.bin
llama_model_load_internal: format     = ggjt v3 (latest)
llama_model_load_internal: n_vocab    = 32000
llama_model_load_internal: n_ctx      = 1024
llama_model_load_internal: n_embd     = 5120
llama_model_load_internal: n_mult     = 256
llama_model_load_internal: n_head     = 40
llama_model_load_internal: n_layer    = 40
llama_model_load_internal: n_rot      = 128
llama_model_load_internal: ftype      = 3 (mostly Q4_1)
llama_model_load_internal: n_ff       = 13824
llama_model_load_internal: n_parts    = 1
llama_model_load_internal: model size = 13B
llama_model_load_internal: ggml ctx size = 7759.48 MB
llama_model_load_internal: mem required  = 9807.48 MB (+ 1608.00 MB per state)
....................................................................................................
llama_init_from_file: kv self size  =  800.00 MB

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.

User: Hello, Bob.
Bob: Hello. How may I help you today?
User: Please tell me the largest city in Europe.
Bob: Sure. The largest city in Europe is Moscow, the capital of Russia.
User:
</code></pre>
<p>Now, enjoy chatting with LLM!</p>


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