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<div class="bd-toc-item navbar-nav"><p aria-level="2" class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
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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="../quick-start-guide.html">Quick Start Guide</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../key-features.html">Key Features</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../torch.html">PyTorch Backend</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../release-notes.html">Release Notes</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../installation/grace-hopper.html">Installing on Grace Hopper</a></li>
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</ul>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">LLM API</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../llm-api/index.html">API Introduction</a></li>
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</ul>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">LLM API Examples</span></p>
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<ul class="current nav bd-sidenav">
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<li class="toctree-l1 current active has-children"><a class="reference internal" href="index.html">LLM Examples Introduction</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
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<li class="toctree-l2"><a class="reference internal" href="llm_medusa_decoding.html">Generate Text Using Medusa Decoding</a></li>
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<li class="toctree-l2"><a class="reference internal" href="llm_multilora.html">Generate text with multiple LoRA adapters</a></li>
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<li class="toctree-l2"><a class="reference internal" href="llm_inference_async.html">Generate Text Asynchronously</a></li>
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<li class="toctree-l2"><a class="reference internal" href="llm_inference_distributed.html">Distributed LLM Generation</a></li>
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<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-l2"><a class="reference internal" href="llm_lookahead_decoding.html">Generate Text Using Lookahead Decoding</a></li>
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<li class="toctree-l2"><a class="reference internal" href="llm_quantization.html">Generation with Quantization</a></li>
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<li class="toctree-l2"><a class="reference internal" href="llm_inference_async_streaming.html">Generate Text in Streaming</a></li>
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<li class="toctree-l2 current active"><a class="current reference internal" href="#">Generate text with guided decoding</a></li>
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<li class="toctree-l2"><a class="reference internal" href="llm_inference.html">Generate text</a></li>
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<li class="toctree-l2"><a class="reference internal" href="llm_inference_customize.html">Generate text with customization</a></li>
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<li class="toctree-l2"><a class="reference internal" href="llm_auto_parallel.html">Automatic Parallelism with LLM</a></li>
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</ul>
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</details></li>
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<li class="toctree-l1"><a class="reference internal" href="customization.html">Common Customizations</a></li>
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<li class="toctree-l1 current active has-children"><a class="reference internal" href="llm_api_examples.html">Examples</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
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<li class="toctree-l2"><a class="reference internal" href="llm_medusa_decoding.html">Generate Text Using Medusa Decoding</a></li>
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<li class="toctree-l2"><a class="reference internal" href="llm_multilora.html">Generate text with multiple LoRA adapters</a></li>
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<li class="toctree-l2"><a class="reference internal" href="llm_inference_async.html">Generate Text Asynchronously</a></li>
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<li class="toctree-l2"><a class="reference internal" href="llm_inference_distributed.html">Distributed LLM Generation</a></li>
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<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-l2"><a class="reference internal" href="llm_lookahead_decoding.html">Generate Text Using Lookahead Decoding</a></li>
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<li class="toctree-l2"><a class="reference internal" href="llm_quantization.html">Generation with Quantization</a></li>
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<li class="toctree-l2"><a class="reference internal" href="llm_inference_async_streaming.html">Generate Text in Streaming</a></li>
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<li class="toctree-l2 current active"><a class="current reference internal" href="#">Generate text with guided decoding</a></li>
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<li class="toctree-l2"><a class="reference internal" href="llm_inference.html">Generate text</a></li>
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<li class="toctree-l2"><a class="reference internal" href="llm_inference_customize.html">Generate text with customization</a></li>
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<li class="toctree-l2"><a class="reference internal" href="llm_auto_parallel.html">Automatic Parallelism with LLM</a></li>
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</ul>
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</details></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.layers.html">Layers</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../python-api/tensorrt_llm.functional.html">Functionals</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../python-api/tensorrt_llm.models.html">Models</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../python-api/tensorrt_llm.plugin.html">Plugin</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../python-api/tensorrt_llm.quantization.html">Quantization</a></li>
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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">C++ API</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../_cpp_gen/executor.html">Executor</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../_cpp_gen/runtime.html">Runtime</a></li>
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<ul class="nav bd-sidenav">
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<li class="toctree-l1"><a class="reference internal" href="../commands/trtllm-build.html">trtllm-build</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../commands/trtllm-serve.html">trtllm-serve</a></li>
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</ul>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Architecture</span></p>
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<ul class="nav bd-sidenav">
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<li class="toctree-l1"><a class="reference internal" href="../architecture/overview.html">TensorRT-LLM Architecture</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../architecture/core-concepts.html">Model Definition</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../architecture/checkpoint.html">TensorRT-LLM Checkpoint</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../architecture/workflow.html">TensorRT-LLM Build Workflow</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../architecture/add-model.html">Adding a Model</a></li>
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</ul>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Advanced</span></p>
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<ul class="nav bd-sidenav">
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<li class="toctree-l1"><a class="reference internal" href="../advanced/gpt-attention.html">Multi-Head, Multi-Query, and Group-Query Attention</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../advanced/gpt-runtime.html">C++ GPT Runtime</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../advanced/executor.html">Executor API</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../advanced/graph-rewriting.html">Graph Rewriting Module</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../advanced/inference-request.html">Inference Request</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../advanced/lora.html">Run gpt-2b + LoRA using GptManager / cpp runtime</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../advanced/expert-parallelism.html">Expert Parallelism in TensorRT-LLM</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../advanced/kv-cache-reuse.html">KV cache reuse</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../advanced/speculative-decoding.html">Speculative Sampling</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../advanced/disaggregated-service.html">Disaggregated-Service (experimental)</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>
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<li class="toctree-l2"><a class="reference internal" href="../performance/performance-tuning-guide/benchmarking-default-performance.html">Benchmarking Default Performance</a></li>
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<section id="generate-text-with-guided-decoding">
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<h1>Generate text with guided decoding<a class="headerlink" href="#generate-text-with-guided-decoding" title="Link to this heading">#</a></h1>
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<p>Source <a class="github reference external" href="https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples/llm-api/llm_guided_decoding.py">NVIDIA/TensorRT-LLM</a>.</p>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="linenos"> 1</span><span class="c1">### Generate text with guided decoding</span>
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<span class="linenos"> 2</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="p">,</span> <span class="n">SamplingParams</span>
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<span class="linenos"> 3</span><span class="kn">from</span><span class="w"> </span><span class="nn">tensorrt_llm.llmapi</span><span class="w"> </span><span class="kn">import</span> <span class="n">BuildConfig</span><span class="p">,</span> <span class="n">GuidedDecodingParams</span>
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<span class="linenos"> 4</span>
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<span class="linenos"> 5</span>
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<span class="linenos"> 6</span><span class="k">def</span><span class="w"> </span><span class="nf">main</span><span class="p">():</span>
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<span class="linenos"> 7</span>
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<span class="linenos"> 8</span> <span class="c1"># TODO(jiahanc): Clean up build_config when use_paged_context_fmha is by default enabled</span>
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<span class="linenos"> 9</span> <span class="n">build_config</span> <span class="o">=</span> <span class="n">BuildConfig</span><span class="p">()</span>
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<span class="linenos">10</span> <span class="n">build_config</span><span class="o">.</span><span class="n">plugin_config</span><span class="o">.</span><span class="n">use_paged_context_fmha</span> <span class="o">=</span> <span class="kc">True</span>
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<span class="linenos">11</span>
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<span class="linenos">12</span> <span class="c1"># Specify the guided decoding backend; xgrammar is supported currently.</span>
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<span class="linenos">13</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">"TinyLlama/TinyLlama-1.1B-Chat-v1.0"</span><span class="p">,</span>
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<span class="linenos">14</span> <span class="n">build_config</span><span class="o">=</span><span class="n">build_config</span><span class="p">,</span>
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<span class="linenos">15</span> <span class="n">guided_decoding_backend</span><span class="o">=</span><span class="s1">'xgrammar'</span><span class="p">)</span>
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<span class="linenos">16</span>
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<span class="linenos">17</span> <span class="c1"># An example from json-mode-eval</span>
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<span class="linenos">18</span> <span class="n">schema</span> <span class="o">=</span> <span class="s1">'{"title": "WirelessAccessPoint", "type": "object", "properties": {"ssid": {"title": "SSID", "type": "string"}, "securityProtocol": {"title": "SecurityProtocol", "type": "string"}, "bandwidth": {"title": "Bandwidth", "type": "string"}}, "required": ["ssid", "securityProtocol", "bandwidth"]}'</span>
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<span class="linenos">19</span>
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<span class="linenos">20</span> <span class="n">prompt</span> <span class="o">=</span> <span class="p">[{</span>
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<span class="linenos">21</span> <span class="s1">'role'</span><span class="p">:</span>
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<span class="linenos">22</span> <span class="s1">'system'</span><span class="p">,</span>
|
|
<span class="linenos">23</span> <span class="s1">'content'</span><span class="p">:</span>
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<span class="linenos">24</span> <span class="s2">"You are a helpful assistant that answers in JSON. Here's the json schema you must adhere to:</span><span class="se">\n</span><span class="s2"><schema></span><span class="se">\n</span><span class="s2">{'title': 'WirelessAccessPoint', 'type': 'object', 'properties': {'ssid': {'title': 'SSID', 'type': 'string'}, 'securityProtocol': {'title': 'SecurityProtocol', 'type': 'string'}, 'bandwidth': {'title': 'Bandwidth', 'type': 'string'}}, 'required': ['ssid', 'securityProtocol', 'bandwidth']}</span><span class="se">\n</span><span class="s2"></schema></span><span class="se">\n</span><span class="s2">"</span>
|
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<span class="linenos">25</span> <span class="p">},</span> <span class="p">{</span>
|
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<span class="linenos">26</span> <span class="s1">'role'</span><span class="p">:</span>
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<span class="linenos">27</span> <span class="s1">'user'</span><span class="p">,</span>
|
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<span class="linenos">28</span> <span class="s1">'content'</span><span class="p">:</span>
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<span class="linenos">29</span> <span class="s2">"I'm currently configuring a wireless access point for our office network and I need to generate a JSON object that accurately represents its settings. The access point's SSID should be 'OfficeNetSecure', it uses WPA2-Enterprise as its security protocol, and it's capable of a bandwidth of up to 1300 Mbps on the 5 GHz band. This JSON object will be used to document our network configurations and to automate the setup process for additional access points in the future. Please provide a JSON object that includes these details."</span>
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<span class="linenos">30</span> <span class="p">}]</span>
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<span class="linenos">31</span> <span class="n">prompt</span> <span class="o">=</span> <span class="n">llm</span><span class="o">.</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">apply_chat_template</span><span class="p">(</span><span class="n">prompt</span><span class="p">,</span> <span class="n">tokenize</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
|
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<span class="linenos">32</span> <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Prompt: </span><span class="si">{</span><span class="n">prompt</span><span class="si">!r}</span><span class="s2">"</span><span class="p">)</span>
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<span class="linenos">33</span>
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<span class="linenos">34</span> <span class="n">output</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">prompt</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>
|
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<span class="linenos">35</span> <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Generated text (unguided): </span><span class="si">{</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="si">!r}</span><span class="s2">"</span><span class="p">)</span>
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<span class="linenos">36</span>
|
|
<span class="linenos">37</span> <span class="n">output</span> <span class="o">=</span> <span class="n">llm</span><span class="o">.</span><span class="n">generate</span><span class="p">(</span>
|
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<span class="linenos">38</span> <span class="n">prompt</span><span class="p">,</span>
|
|
<span class="linenos">39</span> <span class="n">sampling_params</span><span class="o">=</span><span class="n">SamplingParams</span><span class="p">(</span>
|
|
<span class="linenos">40</span> <span class="n">max_tokens</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">guided_decoding</span><span class="o">=</span><span class="n">GuidedDecodingParams</span><span class="p">(</span><span class="n">json</span><span class="o">=</span><span class="n">schema</span><span class="p">)))</span>
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<span class="linenos">41</span> <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Generated text (guided): </span><span class="si">{</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="si">!r}</span><span class="s2">"</span><span class="p">)</span>
|
|
<span class="linenos">42</span>
|
|
<span class="linenos">43</span> <span class="c1"># Got output like</span>
|
|
<span class="linenos">44</span> <span class="c1"># Prompt: "<|system|>\nYou are a helpful assistant that answers in JSON. Here's the json schema you must adhere to:\n<schema>\n{'title': 'WirelessAccessPoint', 'type': 'object', 'properties': {'ssid': {'title': 'SSID', 'type': 'string'}, 'securityProtocol': {'title': 'SecurityProtocol', 'type': 'string'}, 'bandwidth': {'title': 'Bandwidth', 'type': 'string'}}, 'required': ['ssid', 'securityProtocol', 'bandwidth']}\n</schema>\n</s>\n<|user|>\nI'm currently configuring a wireless access point for our office network and I need to generate a JSON object that accurately represents its settings. The access point's SSID should be 'OfficeNetSecure', it uses WPA2-Enterprise as its security protocol, and it's capable of a bandwidth of up to 1300 Mbps on the 5 GHz band. This JSON object will be used to document our network configurations and to automate the setup process for additional access points in the future. Please provide a JSON object that includes these details.</s>\n"</span>
|
|
<span class="linenos">45</span> <span class="c1"># Generated text (unguided): '<|assistant|>\nHere\'s a JSON object that accurately represents the settings of a wireless access point for our office network:\n\n```json\n{\n "title": "WirelessAccessPoint",\n "'</span>
|
|
<span class="linenos">46</span> <span class="c1"># Generated text (guided): '{"ssid": "OfficeNetSecure", "securityProtocol": "WPA2-Enterprise", "bandwidth": "1300 Mbps"}'</span>
|
|
<span class="linenos">47</span>
|
|
<span class="linenos">48</span>
|
|
<span class="linenos">49</span><span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">'__main__'</span><span class="p">:</span>
|
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<span class="linenos">50</span> <span class="n">main</span><span class="p">()</span>
|
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</pre></div>
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