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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 has-children"><a class="reference internal" href="../installation/index.html">Installation</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="../installation/containers.html">Pre-built release container images on NGC</a></li>
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<li class="toctree-l2"><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>
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<li class="toctree-l2"><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 current active has-children"><a class="reference internal" href="llm_api_examples.html">LLM 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_inference.html">Generate text</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_async_streaming.html">Generate text in streaming</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 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_logits_processor.html">Control generated text using logits processor</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_sparse_attention.html">Sparse Attention</a></li>
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<li class="toctree-l2"><a class="reference internal" href="llm_speculative_decoding.html">Speculative Decoding</a></li>
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<li class="toctree-l2"><a class="reference internal" href="llm_kv_cache_connector.html">KV Cache Connector</a></li>
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<li class="toctree-l2"><a class="reference internal" href="llm_kv_cache_offloading.html">KV Cache Offloading</a></li>
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<li class="toctree-l2"><a class="reference internal" href="llm_runtime.html">Runtime Configuration Examples</a></li>
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<li class="toctree-l2"><a class="reference internal" href="llm_sampling.html">Sampling Techniques Showcase</a></li>
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<li class="toctree-l2"><a class="reference internal" href="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="llm_mgmn_trtllm_bench.html">Run trtllm-bench with pytorch backend on Slurm</a></li>
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<li class="toctree-l2"><a class="reference internal" href="llm_mgmn_trtllm_serve.html">Run trtllm-serve with pytorch backend on Slurm</a></li>
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</ul>
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</details></li>
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<li class="toctree-l1 has-children"><a class="reference internal" href="trtllm_serve_examples.html">Online Serving Examples</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="curl_responses_client.html">Curl Responses Client</a></li>
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<li class="toctree-l2"><a class="reference internal" href="deepseek_r1_reasoning_parser.html">Deepseek R1 Reasoning Parser</a></li>
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<li class="toctree-l2"><a class="reference internal" href="openai_chat_client.html">OpenAI Chat Client</a></li>
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<li class="toctree-l2"><a class="reference internal" href="openai_chat_client_for_multimodal.html">OpenAI Chat Client for Multimodal</a></li>
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<li class="toctree-l2"><a class="reference internal" href="openai_completion_client_for_lora.html">Openai Completion Client For Lora</a></li>
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<li class="toctree-l2"><a class="reference internal" href="openai_completion_client_json_schema.html">OpenAI Completion Client with JSON Schema</a></li>
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<li class="toctree-l2"><a class="reference internal" href="openai_responses_client.html">OpenAI Responses Client</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="dynamo_k8s_example.html">Dynamo K8s Example</a></li>
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<li class="toctree-l1 has-children"><a class="reference internal" href="../deployment-guide/index.html">Model Recipes</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="../deployment-guide/deployment-guide-for-deepseek-r1-on-trtllm.html">Deployment Guide for DeepSeek R1 on TensorRT LLM - Blackwell & Hopper Hardware</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../deployment-guide/deployment-guide-for-llama3.3-70b-on-trtllm.html">Deployment Guide for Llama3.3 70B on TensorRT LLM - Blackwell & Hopper Hardware</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../deployment-guide/deployment-guide-for-llama4-scout-on-trtllm.html">Deployment Guide for Llama4 Scout 17B on TensorRT LLM - Blackwell & Hopper Hardware</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../deployment-guide/deployment-guide-for-gpt-oss-on-trtllm.html">Deployment Guide for GPT-OSS on TensorRT-LLM - Blackwell Hardware</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../deployment-guide/deployment-guide-for-qwen3-on-trtllm.html">Deployment Guide for Qwen3 on TensorRT LLM - Blackwell & Hopper Hardware</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../deployment-guide/deployment-guide-for-qwen3-next-on-trtllm.html">Deployment Guide for Qwen3 Next on TensorRT LLM - Blackwell & Hopper Hardware</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../deployment-guide/deployment-guide-for-kimi-k2-thinking-on-trtllm.html">Deployment Guide for Kimi K2 Thinking on TensorRT LLM - Blackwell</a></li>
|
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|
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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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<li class="toctree-l1"><a class="reference internal" href="../developer-guide/kv-transfer.html">Introduction to KV Cache Transmission</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../blogs/tech_blog/blog10_ADP_Balance_Strategy.html">ADP Balance Strategy</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../blogs/tech_blog/blog11_GPT_OSS_Eagle3.html">Running GPT-OSS-120B with Eagle3 Speculative Decoding on GB200/B200 (TensorRT LLM)</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../blogs/tech_blog/blog12_Combining_Guided_Decoding_and_Speculative_Decoding.html">Combining Guided Decoding and Speculative Decoding: Making CPU and GPU Cooperate Seamlessly</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../blogs/tech_blog/blog13_Inference_Time_Compute_Implementation_in_TensorRT-LLM.html">Inference Time Compute Implementation in TensorRT LLM</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../blogs/tech_blog/blog14_Scaling_Expert_Parallelism_in_TensorRT-LLM_part3.html">Scaling Expert Parallelism in TensorRT LLM (Part 3: Pushing the Performance Boundary)</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../blogs/tech_blog/blog1_Pushing_Latency_Boundaries_Optimizing_DeepSeek-R1_Performance_on_NVIDIA_B200_GPUs.html">Pushing Latency Boundaries: Optimizing DeepSeek-R1 Performance on NVIDIA B200 GPUs</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../blogs/tech_blog/blog2_DeepSeek_R1_MTP_Implementation_and_Optimization.html">DeepSeek R1 MTP Implementation and Optimization</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../blogs/tech_blog/blog3_Optimizing_DeepSeek_R1_Throughput_on_NVIDIA_Blackwell_GPUs.html">Optimizing DeepSeek R1 Throughput on NVIDIA Blackwell GPUs: A Deep Dive for Developers</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../blogs/tech_blog/blog4_Scaling_Expert_Parallelism_in_TensorRT-LLM.html">Scaling Expert Parallelism in TensorRT LLM (Part 1: Design and Implementation of Large-scale EP)</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../blogs/tech_blog/blog5_Disaggregated_Serving_in_TensorRT-LLM.html">Disaggregated Serving in TensorRT LLM</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../blogs/tech_blog/blog6_Llama4_maverick_eagle_guide.html">How to launch Llama4 Maverick + Eagle3 TensorRT LLM server</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../blogs/tech_blog/blog7_NGram_performance_Analysis_And_Auto_Enablement.html">N-Gram Speculative Decoding in TensorRT LLM</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../blogs/tech_blog/blog8_Scaling_Expert_Parallelism_in_TensorRT-LLM_part2.html">Scaling Expert Parallelism in TensorRT LLM (Part 2: Performance Status and Optimization)</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../blogs/tech_blog/blog9_Deploying_GPT_OSS_on_TRTLLM.html">Running a High Performance GPT-OSS-120B Inference Server with TensorRT LLM</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../blogs/Best_perf_practice_on_DeepSeek-R1_in_TensorRT-LLM.html">How to get best performance on DeepSeek-R1 in TensorRT LLM</a></li>
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<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>
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<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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<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>
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<li class="breadcrumb-item active" aria-current="page"><span class="ellipsis">Generate text with guided decoding</span></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>
|
||
<p>Source <a class="github reference external" href="https://github.com/NVIDIA/TensorRT-LLM/blob/a65b0d4efa23e6802f391c21de90750169ecf1ea/examples/llm-api/llm_guided_decoding.py">NVIDIA/TensorRT-LLM</a>.</p>
|
||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="linenos"> 1</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>
|
||
<span class="linenos"> 2</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">GuidedDecodingParams</span>
|
||
<span class="linenos"> 3</span>
|
||
<span class="linenos"> 4</span>
|
||
<span class="linenos"> 5</span><span class="k">def</span><span class="w"> </span><span class="nf">main</span><span class="p">():</span>
|
||
<span class="linenos"> 6</span>
|
||
<span class="linenos"> 7</span> <span class="c1"># Specify the guided decoding backend; xgrammar and llguidance are supported currently.</span>
|
||
<span class="linenos"> 8</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>
|
||
<span class="linenos"> 9</span> <span class="n">guided_decoding_backend</span><span class="o">=</span><span class="s1">'xgrammar'</span><span class="p">)</span>
|
||
<span class="linenos">10</span>
|
||
<span class="linenos">11</span> <span class="c1"># An example from json-mode-eval</span>
|
||
<span class="linenos">12</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>
|
||
<span class="linenos">13</span>
|
||
<span class="linenos">14</span> <span class="n">prompt</span> <span class="o">=</span> <span class="p">[{</span>
|
||
<span class="linenos">15</span> <span class="s1">'role'</span><span class="p">:</span>
|
||
<span class="linenos">16</span> <span class="s1">'system'</span><span class="p">,</span>
|
||
<span class="linenos">17</span> <span class="s1">'content'</span><span class="p">:</span>
|
||
<span class="linenos">18</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>
|
||
<span class="linenos">19</span> <span class="p">},</span> <span class="p">{</span>
|
||
<span class="linenos">20</span> <span class="s1">'role'</span><span class="p">:</span>
|
||
<span class="linenos">21</span> <span class="s1">'user'</span><span class="p">,</span>
|
||
<span class="linenos">22</span> <span class="s1">'content'</span><span class="p">:</span>
|
||
<span class="linenos">23</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>
|
||
<span class="linenos">24</span> <span class="p">}]</span>
|
||
<span class="linenos">25</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>
|
||
<span class="linenos">26</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>
|
||
<span class="linenos">27</span>
|
||
<span class="linenos">28</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>
|
||
<span class="linenos">29</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>
|
||
<span class="linenos">30</span>
|
||
<span class="linenos">31</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="linenos">32</span> <span class="n">prompt</span><span class="p">,</span>
|
||
<span class="linenos">33</span> <span class="n">sampling_params</span><span class="o">=</span><span class="n">SamplingParams</span><span class="p">(</span>
|
||
<span class="linenos">34</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>
|
||
<span class="linenos">35</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">36</span>
|
||
<span class="linenos">37</span> <span class="c1"># Got output like</span>
|
||
<span class="linenos">38</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">39</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">40</span> <span class="c1"># Generated text (guided): '{"ssid": "OfficeNetSecure", "securityProtocol": "WPA2-Enterprise", "bandwidth": "1300 Mbps"}'</span>
|
||
<span class="linenos">41</span>
|
||
<span class="linenos">42</span>
|
||
<span class="linenos">43</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">44</span> <span class="n">main</span><span class="p">()</span>
|
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</pre></div>
|
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