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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>
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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="n">prompts</span> <span class="o">=</span> <span class="p">[</span>
<span class="linenos"> 4</span> <span class="s2">&quot;Hello, my name is&quot;</span><span class="p">,</span>
<span class="linenos"> 5</span> <span class="s2">&quot;The president of the United States is&quot;</span><span class="p">,</span>
<span class="linenos"> 6</span> <span class="s2">&quot;The capital of France is&quot;</span><span class="p">,</span>
<span class="linenos"> 7</span> <span class="s2">&quot;The future of AI is&quot;</span><span class="p">,</span>
<span class="linenos"> 8</span><span class="p">]</span>
<span class="linenos"> 9</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">10</span>
<span class="linenos">11</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">12</span>
<span class="linenos">13</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">14</span>
<span class="linenos">15</span><span class="c1"># Print the outputs.</span>
<span class="linenos">16</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">17</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">18</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">19</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>
</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>
<ul class="simple">
<li><p><a class="reference external" href="https://nvidia.github.io/TensorRT-LLM/llm-api-examples/llm_inference.html">LLM Inference</a></p></li>
<li><p><a class="reference external" href="https://nvidia.github.io/TensorRT-LLM/llm-api-examples/llm_inference_distributed.html">LLM Inference Distributed</a></p></li>
<li><p><a class="reference external" href="https://nvidia.github.io/TensorRT-LLM/llm-api-examples/llm_inference_async.html">LLM Inference Async</a></p></li>
<li><p><a class="reference external" href="https://nvidia.github.io/TensorRT-LLM/llm-api-examples/llm_inference_async_streaming.html">LLM Inference Async Streaming</a></p></li>
<li><p><a class="reference external" href="https://nvidia.github.io/TensorRT-LLM/llm-api-examples/llm_quantization.html">LLM Quantization</a></p></li>
<li><p><a class="reference external" href="https://nvidia.github.io/TensorRT-LLM/llm-api-examples/llm_auto_parallel.html">LLM Auto Parallel</a></p></li>
<li><p><a class="reference external" href="https://nvidia.github.io/TensorRT-LLM/llm-api-examples/llm_logits_processor.html">LLM Logits Processor</a></p></li>
<li><p><a class="reference external" href="https://nvidia.github.io/TensorRT-LLM/llm-api-examples/llm_auto_parallel.html">Automatic Parallelism</a> (in preview)</p></li>
<li><p><a class="reference external" href="https://nvidia.github.io/TensorRT-LLM/llm-api-examples/llm_inference_async.html">Generation Async</a></p></li>
<li><p><a class="reference external" href="https://nvidia.github.io/TensorRT-LLM/llm-api-examples/llm_inference_async_streaming.html">Generation Async Streamling</a>
For more details on how to fully utilize this API, check out:</p></li>
<li><p><a class="reference external" href="https://nvidia.github.io/TensorRT-LLM/llm-api-examples/customization.html">Common customizations</a></p></li>
<li><p><a class="reference external" href="https://nvidia.github.io/TensorRT-LLM/llm-api/index.html">LLM API Reference</a></p></li>
</ul>
<section id="supported-models">
<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">
<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 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 LLM(model=<any-model-path>) constructor.
The following sections how to use get these different formats for the LLM API.</p>
<section id="hugging-face-hub">
<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 id="local-hugging-face-models">
<h4>Local Hugging Face Models<a class="headerlink" href="#local-hugging-face-models" title="Link to this heading"></a></h4>
<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>
<p>Note that using this model is subject to a <a class="reference external" href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/">particular</a> license. 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>
</section>
</section>
<section id="from-tensorrt-llm-engine">
<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>You can build the TensorRT-LLM engine from the Hugging Face model directly with the <a class="reference external" href="https://github.com/NVIDIA/TensorRT-LLM/blob/main/setup.py#L126"><code class="docutils literal notranslate"><span class="pre">trtllm-build</span></code></a> 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 <a class="reference external" href="https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples/llama"><code class="docutils literal notranslate"><span class="pre">examples/llama</span></code></a> repository on GitHub.</p>
<p>After the engine building is 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_trt_engine</span><span class="o">&gt;</span><span class="p">)</span>
</pre></div>
</div>
</li>
<li><p>Use an <code class="docutils literal notranslate"><span class="pre">LLM</span></code> instance to create the engine and persist to local disk:</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="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>
</li>
</ol>
<p>The engine can be reloaded like above.</p>
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