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<li class="toctree-l1"><a class="reference internal" href="../../overview.html">Overview</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../../key-features.html">Key Features</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../torch.html">PyTorch Backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../release-notes.html">Release Notes</a></li>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Installation</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../installation/containers.html">Pre-built release container images on NGC</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../installation/linux.html">Installing on Linux via <code class="docutils literal notranslate"><span class="pre">pip</span></code></a></li>
<li class="toctree-l1"><a class="reference internal" href="../../installation/build-from-source-linux.html">Building from Source Code on Linux</a></li>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Deployment Guide</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../deployment-guide/quick-start-recipe-for-llama4-scout-on-trtllm.html">Quick Start Recipe for Llama4 Scout 17B on TensorRT-LLM - Blackwell &amp; Hopper Hardware</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../deployment-guide/quick-start-recipe-for-deepseek-r1-on-trtllm.html">Quick Start Recipe for DeepSeek R1 on TensorRT-LLM - Blackwell &amp; Hopper Hardware</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../deployment-guide/quick-start-recipe-for-llama3.3-70b-on-trtllm.html">Quick Start Recipe for Llama3.3 70B on TensorRT-LLM - Blackwell &amp; Hopper Hardware</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../deployment-guide/quick-start-recipe-for-gpt-oss-on-trtllm.html">Quick Start Recipe for GPT-OSS on TensorRT-LLM - Blackwell Hardware</a></li>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">LLM API</span></p>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Examples</span></p>
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<li class="toctree-l1 has-children"><a class="reference internal" href="../../examples/index.html">LLM Examples Introduction</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="simple">
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<li class="toctree-l1"><a class="reference internal" href="../../examples/customization.html">LLM Common Customizations</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../../examples/llm_inference.html">Generate text</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../examples/llm_inference_async.html">Generate text asynchronously</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../examples/llm_inference_async_streaming.html">Generate text in streaming</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../examples/llm_inference_distributed.html">Distributed LLM Generation</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../examples/llm_guided_decoding.html">Generate text with guided decoding</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../examples/llm_logits_processor.html">Control generated text using logits processor</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../examples/llm_multilora.html">Generate text with multiple LoRA adapters</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../examples/llm_speculative_decoding.html">Speculative Decoding</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../../examples/llm_mgmn_llm_distributed.html">Run LLM-API with pytorch backend on Slurm</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../../examples/llm_mgmn_trtllm_serve.html">Run trtllm-serve with pytorch backend on Slurm</a></li>
</ul>
</details></li>
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<li class="toctree-l2"><a class="reference internal" href="../../examples/deepseek_r1_reasoning_parser.html">Deepseek R1 Reasoning Parser</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../../examples/openai_completion_client.html">OpenAI Completion Client</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../examples/openai_completion_client_for_lora.html">Openai Completion Client For Lora</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../examples/openai_completion_client_json_schema.html">OpenAI Completion Client with JSON Schema</a></li>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Model Definition API</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../python-api/tensorrt_llm.runtime.html">Runtime</a></li>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Command-Line Reference</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../commands/trtllm-bench.html">trtllm-bench</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../../commands/trtllm-serve/run-benchmark-with-trtllm-serve.html">Run benchmarking with <code class="docutils literal notranslate"><span class="pre">trtllm-serve</span></code></a></li>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Architecture</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../architecture/overview.html">TensorRT-LLM Architecture</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../architecture/core-concepts.html">Model Definition</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../architecture/checkpoint.html">TensorRT-LLM Checkpoint</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../architecture/workflow.html">TensorRT-LLM Build Workflow</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../architecture/add-model.html">Adding a Model</a></li>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Advanced</span></p>
<ul class="nav bd-sidenav">
<li class="toctree-l1"><a class="reference internal" href="../../advanced/gpt-attention.html">Multi-Head, Multi-Query, and Group-Query Attention</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../advanced/gpt-runtime.html">C++ GPT Runtime</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../advanced/executor.html">Executor API</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../advanced/graph-rewriting.html">Graph Rewriting Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../advanced/lora.html">Run gpt-2b + LoRA using Executor / cpp runtime</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../advanced/expert-parallelism.html">Expert Parallelism in TensorRT-LLM</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../advanced/kv-cache-management.html">KV Cache Management: Pools, Blocks, and Events</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../advanced/kv-cache-reuse.html">KV cache reuse</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../advanced/speculative-decoding.html">Speculative Sampling</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../advanced/disaggregated-service.html">Disaggregated-Service (Prototype)</a></li>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Performance</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../performance/perf-overview.html">Overview</a></li>
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<li class="toctree-l1 has-children"><a class="reference internal" href="../../performance/performance-tuning-guide/index.html">Performance Tuning Guide</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="../../performance/performance-tuning-guide/benchmarking-default-performance.html">Benchmarking Default Performance</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../performance/performance-tuning-guide/useful-build-time-flags.html">Useful Build-Time Flags</a></li>
<li class="toctree-l2"><a class="reference internal" href="../../performance/performance-tuning-guide/tuning-max-batch-size-and-max-num-tokens.html">Tuning Max Batch Size and Max Num Tokens</a></li>
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<li class="breadcrumb-item active" aria-current="page"><span class="ellipsis">LoRA (Low-Rank Adaptation)</span></li>
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<section id="lora-low-rank-adaptation">
<h1>LoRA (Low-Rank Adaptation)<a class="headerlink" href="#lora-low-rank-adaptation" title="Link to this heading">#</a></h1>
<p>LoRA (Low-Rank Adaptation) is a parameter-efficient fine-tuning technique that enables adapting large language models to specific tasks without modifying the original model weights. Instead of fine-tuning all parameters, LoRA introduces small trainable rank decomposition matrices that are added to existing weights during inference.</p>
<section id="table-of-contents">
<h2>Table of Contents<a class="headerlink" href="#table-of-contents" title="Link to this heading">#</a></h2>
<ol class="arabic simple">
<li><p><a class="reference internal" href="#background">Background</a></p></li>
<li><p><a class="reference internal" href="#basic-usage">Basic Usage</a></p>
<ul class="simple">
<li><p><a class="reference internal" href="#single-lora-adapter">Single LoRA Adapter</a></p></li>
<li><p><a class="reference internal" href="#multi-lora-support">Multi-LoRA Support</a></p></li>
</ul>
</li>
<li><p><a class="reference internal" href="#advanced-usage">Advanced Usage</a></p>
<ul class="simple">
<li><p><a class="reference internal" href="#lora-with-quantization">LoRA with Quantization</a></p></li>
<li><p><a class="reference internal" href="#nemo-lora-format">NeMo LoRA Format</a></p></li>
<li><p><a class="reference internal" href="#cache-management">Cache Management</a></p></li>
</ul>
</li>
<li><p><a class="reference internal" href="#trtllm-serve-with-lora">TRTLLM serve with LoRA</a></p>
<ul class="simple">
<li><p><a class="reference internal" href="#yaml-configuration">YAML Configuration</a></p></li>
<li><p><a class="reference internal" href="#starting-the-server">Starting the Server</a></p></li>
<li><p><a class="reference internal" href="#client-usage">Client Usage</a></p></li>
</ul>
</li>
<li><p><a class="reference internal" href="#trtllm-bench-with-lora">TRTLLM bench with LORA</a></p>
<ul class="simple">
<li><p><a class="reference internal" href="#yaml-configuration">YAML Configuration</a></p></li>
<li><p><a class="reference internal" href="#run-trtllm-bench">Run trtllm-bench</a></p></li>
</ul>
</li>
</ol>
</section>
<section id="background">
<h2>Background<a class="headerlink" href="#background" title="Link to this heading">#</a></h2>
<p>The PyTorch backend provides LoRA support, allowing you to:</p>
<ul class="simple">
<li><p>Load and apply multiple LoRA adapters simultaneously</p></li>
<li><p>Switch between different adapters for different requests</p></li>
<li><p>Use LoRA with quantized models</p></li>
<li><p>Support both HuggingFace and NeMo LoRA formats</p></li>
</ul>
</section>
<section id="basic-usage">
<h2>Basic Usage<a class="headerlink" href="#basic-usage" title="Link to this heading">#</a></h2>
<section id="single-lora-adapter">
<h3>Single LoRA Adapter<a class="headerlink" href="#single-lora-adapter" title="Link to this heading">#</a></h3>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">tensorrt_llm</span><span class="w"> </span><span class="kn">import</span> <span class="n">LLM</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">tensorrt_llm.lora_helper</span><span class="w"> </span><span class="kn">import</span> <span class="n">LoraConfig</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">tensorrt_llm.executor.request</span><span class="w"> </span><span class="kn">import</span> <span class="n">LoRARequest</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">tensorrt_llm.sampling_params</span><span class="w"> </span><span class="kn">import</span> <span class="n">SamplingParams</span>
<span class="c1"># Configure LoRA</span>
<span class="n">lora_config</span> <span class="o">=</span> <span class="n">LoraConfig</span><span class="p">(</span>
<span class="n">lora_dir</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;/path/to/lora/adapter&quot;</span><span class="p">],</span>
<span class="n">max_lora_rank</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span>
<span class="n">max_loras</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="n">max_cpu_loras</span><span class="o">=</span><span class="mi">1</span>
<span class="p">)</span>
<span class="c1"># Initialize LLM with LoRA support</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;/path/to/base/model&quot;</span><span class="p">,</span>
<span class="n">lora_config</span><span class="o">=</span><span class="n">lora_config</span>
<span class="p">)</span>
<span class="c1"># Create LoRA request</span>
<span class="n">lora_request</span> <span class="o">=</span> <span class="n">LoRARequest</span><span class="p">(</span><span class="s2">&quot;my-lora-task&quot;</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="s2">&quot;/path/to/lora/adapter&quot;</span><span class="p">)</span>
<span class="c1"># Generate with LoRA</span>
<span class="n">prompts</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;Hello, how are you?&quot;</span><span class="p">]</span>
<span class="n">sampling_params</span> <span class="o">=</span> <span class="n">SamplingParams</span><span class="p">(</span><span class="n">max_tokens</span><span class="o">=</span><span class="mi">50</span><span class="p">)</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="n">lora_request</span><span class="o">=</span><span class="p">[</span><span class="n">lora_request</span><span class="p">]</span>
<span class="p">)</span>
</pre></div>
</div>
</section>
<section id="multi-lora-support">
<h3>Multi-LoRA Support<a class="headerlink" href="#multi-lora-support" title="Link to this heading">#</a></h3>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Configure for multiple LoRA adapters</span>
<span class="n">lora_config</span> <span class="o">=</span> <span class="n">LoraConfig</span><span class="p">(</span>
<span class="n">lora_target_modules</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;attn_q&#39;</span><span class="p">,</span> <span class="s1">&#39;attn_k&#39;</span><span class="p">,</span> <span class="s1">&#39;attn_v&#39;</span><span class="p">],</span>
<span class="n">max_lora_rank</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span>
<span class="n">max_loras</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span>
<span class="n">max_cpu_loras</span><span class="o">=</span><span class="mi">8</span>
<span class="p">)</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;/path/to/base/model&quot;</span><span class="p">,</span> <span class="n">lora_config</span><span class="o">=</span><span class="n">lora_config</span><span class="p">)</span>
<span class="c1"># Create multiple LoRA requests</span>
<span class="n">lora_req1</span> <span class="o">=</span> <span class="n">LoRARequest</span><span class="p">(</span><span class="s2">&quot;task-1&quot;</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="s2">&quot;/path/to/adapter1&quot;</span><span class="p">)</span>
<span class="n">lora_req2</span> <span class="o">=</span> <span class="n">LoRARequest</span><span class="p">(</span><span class="s2">&quot;task-2&quot;</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="s2">&quot;/path/to/adapter2&quot;</span><span class="p">)</span>
<span class="n">prompts</span> <span class="o">=</span> <span class="p">[</span>
<span class="s2">&quot;Translate to French: Hello world&quot;</span><span class="p">,</span>
<span class="s2">&quot;Summarize: This is a long document...&quot;</span>
<span class="p">]</span>
<span class="c1"># Apply different LoRAs to different prompts</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="n">lora_request</span><span class="o">=</span><span class="p">[</span><span class="n">lora_req1</span><span class="p">,</span> <span class="n">lora_req2</span><span class="p">]</span>
<span class="p">)</span>
</pre></div>
</div>
</section>
</section>
<section id="advanced-usage">
<h2>Advanced Usage<a class="headerlink" href="#advanced-usage" title="Link to this heading">#</a></h2>
<section id="lora-with-quantization">
<h3>LoRA with Quantization<a class="headerlink" href="#lora-with-quantization" title="Link to this heading">#</a></h3>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">tensorrt_llm.models.modeling_utils</span><span class="w"> </span><span class="kn">import</span> <span class="n">QuantConfig</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">tensorrt_llm.quantization.mode</span><span class="w"> </span><span class="kn">import</span> <span class="n">QuantAlgo</span>
<span class="c1"># Configure quantization</span>
<span class="n">quant_config</span> <span class="o">=</span> <span class="n">QuantConfig</span><span class="p">(</span>
<span class="n">quant_algo</span><span class="o">=</span><span class="n">QuantAlgo</span><span class="o">.</span><span class="n">FP8</span><span class="p">,</span>
<span class="n">kv_cache_quant_algo</span><span class="o">=</span><span class="n">QuantAlgo</span><span class="o">.</span><span class="n">FP8</span>
<span class="p">)</span>
<span class="c1"># LoRA works with quantized models</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;/path/to/model&quot;</span><span class="p">,</span>
<span class="n">quant_config</span><span class="o">=</span><span class="n">quant_config</span><span class="p">,</span>
<span class="n">lora_config</span><span class="o">=</span><span class="n">lora_config</span>
<span class="p">)</span>
</pre></div>
</div>
</section>
<section id="nemo-lora-format">
<h3>NeMo LoRA Format<a class="headerlink" href="#nemo-lora-format" title="Link to this heading">#</a></h3>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># For NeMo-format LoRA checkpoints</span>
<span class="n">lora_config</span> <span class="o">=</span> <span class="n">LoraConfig</span><span class="p">(</span>
<span class="n">lora_dir</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;/path/to/nemo/lora&quot;</span><span class="p">],</span>
<span class="n">lora_ckpt_source</span><span class="o">=</span><span class="s2">&quot;nemo&quot;</span><span class="p">,</span>
<span class="n">max_lora_rank</span><span class="o">=</span><span class="mi">8</span>
<span class="p">)</span>
<span class="n">lora_request</span> <span class="o">=</span> <span class="n">LoRARequest</span><span class="p">(</span>
<span class="s2">&quot;nemo-task&quot;</span><span class="p">,</span>
<span class="mi">0</span><span class="p">,</span>
<span class="s2">&quot;/path/to/nemo/lora&quot;</span><span class="p">,</span>
<span class="n">lora_ckpt_source</span><span class="o">=</span><span class="s2">&quot;nemo&quot;</span>
<span class="p">)</span>
</pre></div>
</div>
</section>
<section id="cache-management">
<h3>Cache Management<a class="headerlink" href="#cache-management" title="Link to this heading">#</a></h3>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">tensorrt_llm.llmapi.llm_args</span><span class="w"> </span><span class="kn">import</span> <span class="n">PeftCacheConfig</span>
<span class="c1"># Fine-tune cache sizes</span>
<span class="n">peft_cache_config</span> <span class="o">=</span> <span class="n">PeftCacheConfig</span><span class="p">(</span>
<span class="n">host_cache_size</span><span class="o">=</span><span class="mi">1024</span><span class="o">*</span><span class="mi">1024</span><span class="o">*</span><span class="mi">1024</span><span class="p">,</span> <span class="c1"># 1GB CPU cache</span>
<span class="n">device_cache_percent</span><span class="o">=</span><span class="mf">0.1</span> <span class="c1"># 10% of free GPU memory</span>
<span class="p">)</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;/path/to/model&quot;</span><span class="p">,</span>
<span class="n">lora_config</span><span class="o">=</span><span class="n">lora_config</span><span class="p">,</span>
<span class="n">peft_cache_config</span><span class="o">=</span><span class="n">peft_cache_config</span>
<span class="p">)</span>
</pre></div>
</div>
</section>
</section>
<section id="trtllm-serve-with-lora">
<h2>TRTLLM serve with LoRA<a class="headerlink" href="#trtllm-serve-with-lora" title="Link to this heading">#</a></h2>
<section id="yaml-configuration">
<h3>YAML Configuration<a class="headerlink" href="#yaml-configuration" title="Link to this heading">#</a></h3>
<p>Create an <code class="docutils literal notranslate"><span class="pre">extra_llm_api_options.yaml</span></code> file:</p>
<div class="highlight-yaml notranslate"><div class="highlight"><pre><span></span><span class="nt">lora_config</span><span class="p">:</span>
<span class="w"> </span><span class="nt">lora_target_modules</span><span class="p">:</span><span class="w"> </span><span class="p p-Indicator">[</span><span class="s">&#39;attn_q&#39;</span><span class="p p-Indicator">,</span><span class="w"> </span><span class="s">&#39;attn_k&#39;</span><span class="p p-Indicator">,</span><span class="w"> </span><span class="s">&#39;attn_v&#39;</span><span class="p p-Indicator">]</span>
<span class="w"> </span><span class="nt">max_lora_rank</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">8</span>
</pre></div>
</div>
</section>
<section id="starting-the-server">
<h3>Starting the Server<a class="headerlink" href="#starting-the-server" title="Link to this heading">#</a></h3>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>python<span class="w"> </span>-m<span class="w"> </span>tensorrt_llm.commands.serve
<span class="w"> </span>/path/to/model<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--extra_llm_api_options<span class="w"> </span>extra_llm_api_options.yaml
</pre></div>
</div>
</section>
<section id="client-usage">
<h3>Client Usage<a class="headerlink" href="#client-usage" title="Link to this heading">#</a></h3>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">openai</span>
<span class="n">client</span> <span class="o">=</span> <span class="n">openai</span><span class="o">.</span><span class="n">OpenAI</span><span class="p">(</span><span class="n">base_url</span><span class="o">=</span><span class="s2">&quot;http://localhost:8000/v1&quot;</span><span class="p">,</span> <span class="n">api_key</span><span class="o">=</span><span class="s2">&quot;dummy&quot;</span><span class="p">)</span>
<span class="n">response</span> <span class="o">=</span> <span class="n">client</span><span class="o">.</span><span class="n">completions</span><span class="o">.</span><span class="n">create</span><span class="p">(</span>
<span class="n">model</span><span class="o">=</span><span class="s2">&quot;/path/to/model&quot;</span><span class="p">,</span>
<span class="n">prompt</span><span class="o">=</span><span class="s2">&quot;What is the capital city of France?&quot;</span><span class="p">,</span>
<span class="n">max_tokens</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span>
<span class="n">extra_body</span><span class="o">=</span><span class="p">{</span>
<span class="s2">&quot;lora_request&quot;</span><span class="p">:</span> <span class="p">{</span>
<span class="s2">&quot;lora_name&quot;</span><span class="p">:</span> <span class="s2">&quot;lora-example-0&quot;</span><span class="p">,</span>
<span class="s2">&quot;lora_int_id&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span>
<span class="s2">&quot;lora_path&quot;</span><span class="p">:</span> <span class="s2">&quot;/path/to/lora_adapter&quot;</span>
<span class="p">}</span>
<span class="p">},</span>
<span class="p">)</span>
</pre></div>
</div>
</section>
</section>
<section id="trtllm-bench-with-lora">
<h2>TRTLLM bench with LORA<a class="headerlink" href="#trtllm-bench-with-lora" title="Link to this heading">#</a></h2>
<section id="id1">
<h3>YAML Configuration<a class="headerlink" href="#id1" title="Link to this heading">#</a></h3>
<p>Create an <code class="docutils literal notranslate"><span class="pre">extra_llm_api_options.yaml</span></code> file:</p>
<div class="highlight-yaml notranslate"><div class="highlight"><pre><span></span><span class="nt">lora_config</span><span class="p">:</span>
<span class="w"> </span><span class="nt">lora_dir</span><span class="p">:</span>
<span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">/workspaces/tensorrt_llm/loras/0</span>
<span class="w"> </span><span class="nt">max_lora_rank</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">64</span>
<span class="w"> </span><span class="nt">max_loras</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">8</span>
<span class="w"> </span><span class="nt">max_cpu_loras</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">8</span>
<span class="w"> </span><span class="nt">lora_target_modules</span><span class="p">:</span>
<span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">attn_q</span>
<span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">attn_k</span>
<span class="w"> </span><span class="p p-Indicator">-</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">attn_v</span>
<span class="w"> </span><span class="nt">trtllm_modules_to_hf_modules</span><span class="p">:</span>
<span class="w"> </span><span class="nt">attn_q</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">q_proj</span>
<span class="w"> </span><span class="nt">attn_k</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">k_proj</span>
<span class="w"> </span><span class="nt">attn_v</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">v_proj</span>
</pre></div>
</div>
</section>
<section id="run-trtllm-bench">
<h3>Run trtllm-bench<a class="headerlink" href="#run-trtllm-bench" title="Link to this heading">#</a></h3>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>trtllm-bench<span class="w"> </span>--model<span class="w"> </span><span class="nv">$model_path</span><span class="w"> </span>throughput<span class="w"> </span>--dataset<span class="w"> </span><span class="nv">$dataset_path</span><span class="w"> </span>--extra_llm_api_options<span class="w"> </span>extra-llm-api-options.yaml<span class="w"> </span>--num_requests<span class="w"> </span><span class="m">64</span><span class="w"> </span>--concurrency<span class="w"> </span><span class="m">16</span>
</pre></div>
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