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<p class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
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<li class="toctree-l2"><a class="reference internal" href="llm_quantization.html">Generation with Quantization</a></li>
<li class="toctree-l2"><a class="reference internal" href="llm_auto_parallel.html">Automatic Parallelism with LLM</a></li>
<li class="toctree-l2"><a class="reference internal" href="llm_multilora.html">Generate text with multiple LoRA adapters</a></li>
<li class="toctree-l2"><a class="reference internal" href="llm_logits_processor.html">Control generated text using logits post processor</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../blogs/H100vsA100.html">H100 has 4.6x A100 Performance in TensorRT-LLM, achieving 10,000 tok/s at 100ms to first token</a></li>
<li class="toctree-l1"><a class="reference internal" href="../blogs/H200launch.html">H200 achieves nearly 12,000 tokens/sec on Llama2-13B with TensorRT-LLM</a></li>
<li class="toctree-l1"><a class="reference internal" href="../blogs/Falcon180B-H200.html">Falcon-180B on a single H200 GPU with INT4 AWQ, and 6.7x faster Llama-70B over A100</a></li>
<li class="toctree-l1"><a class="reference internal" href="../blogs/quantization-in-TRT-LLM.html">Speed up inference with SOTA quantization techniques in TRT-LLM</a></li>
<li class="toctree-l1"><a class="reference internal" href="../blogs/XQA-kernel.html">New XQA-kernel provides 2.4x more Llama-70B throughput within the same latency budget</a></li>
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<section id="llm-examples-introduction">
<h1>LLM Examples Introduction<a class="headerlink" href="#llm-examples-introduction" title="Link to this heading"></a></h1>
<p>Here is a simple example to show how to use the LLM with TinyLlama.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="linenos"> 1</span><span class="kn">from</span> <span class="nn">tensorrt_llm</span> <span class="kn">import</span> <span class="n">LLM</span><span class="p">,</span> <span class="n">SamplingParams</span>
<span class="linenos"> 2</span>
<span class="linenos"> 3</span>
<span class="linenos"> 4</span><span class="k">def</span> <span class="nf">main</span><span class="p">():</span>
<span class="linenos"> 5</span>
<span class="linenos"> 6</span> <span class="n">prompts</span> <span class="o">=</span> <span class="p">[</span>
<span class="linenos"> 7</span> <span class="s2">&quot;Hello, my name is&quot;</span><span class="p">,</span>
<span class="linenos"> 8</span> <span class="s2">&quot;The president of the United States is&quot;</span><span class="p">,</span>
<span class="linenos"> 9</span> <span class="s2">&quot;The capital of France is&quot;</span><span class="p">,</span>
<span class="linenos">10</span> <span class="s2">&quot;The future of AI is&quot;</span><span class="p">,</span>
<span class="linenos">11</span> <span class="p">]</span>
<span class="linenos">12</span> <span class="n">sampling_params</span> <span class="o">=</span> <span class="n">SamplingParams</span><span class="p">(</span><span class="n">temperature</span><span class="o">=</span><span class="mf">0.8</span><span class="p">,</span> <span class="n">top_p</span><span class="o">=</span><span class="mf">0.95</span><span class="p">)</span>
<span class="linenos">13</span>
<span class="linenos">14</span> <span class="n">llm</span> <span class="o">=</span> <span class="n">LLM</span><span class="p">(</span><span class="n">model</span><span class="o">=</span><span class="s2">&quot;TinyLlama/TinyLlama-1.1B-Chat-v1.0&quot;</span><span class="p">)</span>
<span class="linenos">15</span>
<span class="linenos">16</span> <span class="n">outputs</span> <span class="o">=</span> <span class="n">llm</span><span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">prompts</span><span class="p">,</span> <span class="n">sampling_params</span><span class="p">)</span>
<span class="linenos">17</span>
<span class="linenos">18</span> <span class="c1"># Print the outputs.</span>
<span class="linenos">19</span> <span class="k">for</span> <span class="n">output</span> <span class="ow">in</span> <span class="n">outputs</span><span class="p">:</span>
<span class="linenos">20</span> <span class="n">prompt</span> <span class="o">=</span> <span class="n">output</span><span class="o">.</span><span class="n">prompt</span>
<span class="linenos">21</span> <span class="n">generated_text</span> <span class="o">=</span> <span class="n">output</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">text</span>
<span class="linenos">22</span> <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Prompt: </span><span class="si">{</span><span class="n">prompt</span><span class="si">!r}</span><span class="s2">, Generated text: </span><span class="si">{</span><span class="n">generated_text</span><span class="si">!r}</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="linenos">23</span>
<span class="linenos">24</span>
<span class="linenos">25</span><span class="c1"># The entry point of the program need to be protected for spawning processes.</span>
<span class="linenos">26</span><span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
<span class="linenos">27</span> <span class="n">main</span><span class="p">()</span>
</pre></div>
</div>
<p>The LLM API can be used for both offline or online usage. See more examples of the LLM API here:</p>
<div class="toctree-wrapper compound">
<p class="caption" role="heading"><span class="caption-text">LLM API Examples</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="llm_inference.html">Generate text</a></li>
<li class="toctree-l1"><a class="reference internal" href="llm_inference_distributed.html">Distributed LLM Generation</a></li>
<li class="toctree-l1"><a class="reference internal" href="llm_inference_async.html">Generate Text Asynchronously</a></li>
<li class="toctree-l1"><a class="reference internal" href="llm_inference_async_streaming.html">Generate Text in Streaming</a></li>
<li class="toctree-l1"><a class="reference internal" href="llm_quantization.html">Generation with Quantization</a></li>
<li class="toctree-l1"><a class="reference internal" href="llm_auto_parallel.html">Automatic Parallelism with LLM</a></li>
<li class="toctree-l1"><a class="reference internal" href="llm_multilora.html">Generate text with multiple LoRA adapters</a></li>
<li class="toctree-l1"><a class="reference internal" href="llm_logits_processor.html">Control generated text using logits post processor</a></li>
<li class="toctree-l1"><a class="reference internal" href="llm_guided_decoding.html">Generate text with guided decoding</a></li>
<li class="toctree-l1"><a class="reference internal" href="llm_lookahead_decoding.html">Generate Text Using Lookahead Decoding</a></li>
</ul>
</div>
<p>For more details on how to fully utilize this API, check out:</p>
<ul class="simple">
<li><p><a class="reference external" href="customization.html">Common customizations</a></p></li>
<li><p><a class="reference external" href="../llm-api/index.html">LLM API Reference</a></p></li>
</ul>
<section id="supported-models">
<span id="id1"></span><h2>Supported Models<a class="headerlink" href="#supported-models" title="Link to this heading"></a></h2>
<ul class="simple">
<li><p>Llama (including variants Mistral, Mixtral, InternLM)</p></li>
<li><p>GPT (including variants Starcoder-1/2, Santacoder)</p></li>
<li><p>Gemma-1/2</p></li>
<li><p>Phi-1/2/3</p></li>
<li><p>ChatGLM (including variants glm-10b, chatglm, chatglm2, chatglm3, glm4)</p></li>
<li><p>QWen-1/1.5/2</p></li>
<li><p>Falcon</p></li>
<li><p>Baichuan-1/2</p></li>
<li><p>GPT-J</p></li>
<li><p>Mamba-1/2</p></li>
</ul>
</section>
<section id="model-preparation">
<span id="id2"></span><h2>Model Preparation<a class="headerlink" href="#model-preparation" title="Link to this heading"></a></h2>
<p>The <code class="docutils literal notranslate"><span class="pre">LLM</span></code> class supports input from any of the following:</p>
<ol class="arabic simple">
<li><p><strong>Hugging Face Hub</strong>: Triggers a download from the Hugging Face model hub, such as <code class="docutils literal notranslate"><span class="pre">TinyLlama/TinyLlama-1.1B-Chat-v1.0</span></code>.</p></li>
<li><p><strong>Local Hugging Face models</strong>: Uses a locally stored Hugging Face model.</p></li>
<li><p><strong>Local TensorRT-LLM engine</strong>: Built by <code class="docutils literal notranslate"><span class="pre">trtllm-build</span></code> tool or saved by the Python LLM API.</p></li>
</ol>
<p>Any of these formats can be used interchangeably with the <code class="docutils literal notranslate"><span class="pre">LLM(model=&lt;any-model-path&gt;)</span></code> constructor.</p>
<p>The following sections show how to use these different formats for the LLM API.</p>
<section id="hugging-face-hub">
<span id="id3"></span><h3>Hugging Face Hub<a class="headerlink" href="#hugging-face-hub" title="Link to this heading"></a></h3>
<p>Using the Hugging Face hub is as simple as specifying the repo name in the LLM constructor:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">llm</span> <span class="o">=</span> <span class="n">LLM</span><span class="p">(</span><span class="n">model</span><span class="o">=</span><span class="s2">&quot;TinyLlama/TinyLlama-1.1B-Chat-v1.0&quot;</span><span class="p">)</span>
</pre></div>
</div>
</section>
<section id="local-hugging-face-models">
<h3>Local Hugging Face Models<a class="headerlink" href="#local-hugging-face-models" title="Link to this heading"></a></h3>
<p>Given the popularity of the Hugging Face model hub, the API supports the Hugging Face format as one of the starting points.
To use the API with Llama 3.1 models, download the model from the <a class="reference external" href="https://huggingface.co/meta-llama/Meta-Llama-3.1-8B">Meta Llama 3.1 8B model page</a> by using the following command:</p>
<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">git lfs install</span>
<span class="go">git clone https://huggingface.co/meta-llama/Meta-Llama-3.1-8B</span>
</pre></div>
</div>
<p>After the model downloading finished, we can load the model as below:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">llm</span> <span class="o">=</span> <span class="n">LLM</span><span class="p">(</span><span class="n">model</span><span class="o">=&lt;</span><span class="n">path_to_meta_llama_from_hf</span><span class="o">&gt;</span><span class="p">)</span>
</pre></div>
</div>
<dl class="simple">
<dt>Note:</dt><dd><p>Using this model is subject to a <a class="reference external" href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/">particular license</a>. Agree to the terms and <a class="reference external" href="https://huggingface.co/meta-llama/Meta-Llama-3-8B?clone=true">authenticate with HuggingFace</a> to begin the download.</p>
</dd>
</dl>
</section>
<section id="from-tensorrt-llm-engine">
<span id="id4"></span><h3>From TensorRT-LLM Engine<a class="headerlink" href="#from-tensorrt-llm-engine" title="Link to this heading"></a></h3>
<p>There are two ways to build the TensorRT-LLM engine:</p>
<ol class="arabic">
<li><p><strong>Using the ``trtllm-build`` Tool</strong>: You can build the TensorRT-LLM engine from the Hugging Face model directly with the <code class="docutils literal notranslate"><span class="pre">trtllm-build</span></code> tool and then save the engine to disk for later use.
Refer to the <a class="reference external" href="https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples/llama">README</a> in the <code class="docutils literal notranslate"><span class="pre">examples/llama</span></code> repository on GitHub.</p>
<p>After the engine building is finished, we can load the model as below:</p>
</li>
</ol>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">llm</span> <span class="o">=</span> <span class="n">LLM</span><span class="p">(</span><span class="n">model</span><span class="o">=&lt;</span><span class="n">path_to_trt_engine</span><span class="o">&gt;</span><span class="p">)</span>
</pre></div>
</div>
<ol class="arabic simple" start="2">
<li><p><strong>Using an ``LLM`` Instance</strong>: Use an <code class="docutils literal notranslate"><span class="pre">LLM</span></code> instance to create the engine and persist to local disk:</p></li>
</ol>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">llm</span> <span class="o">=</span> <span class="n">LLM</span><span class="p">(</span><span class="o">&lt;</span><span class="n">model</span><span class="o">-</span><span class="n">path</span><span class="o">&gt;</span><span class="p">)</span>
<span class="c1"># Save engine to local disk</span>
<span class="n">llm</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="o">&lt;</span><span class="n">engine</span><span class="o">-</span><span class="nb">dir</span><span class="o">&gt;</span><span class="p">)</span>
</pre></div>
</div>
<p>The engine can be reloaded as above.</p>
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