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TensorRT-LLMs/examples/llm_logits_processor.html
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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>
<li class="toctree-l1"><a class="reference internal" href="../quick-start-guide.html">Quick Start Guide</a></li>
<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">LLM API</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../llm-api/index.html">LLM API Introduction</a></li>
<li class="toctree-l1"><a class="reference internal" href="../llm-api/reference.html">API Reference</a></li>
</ul>
<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="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="customization.html">LLM Common Customizations</a></li>
<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">
<li class="toctree-l2"><a class="reference internal" href="llm_inference.html">Generate text</a></li>
<li class="toctree-l2"><a class="reference internal" href="llm_inference_async.html">Generate text asynchronously</a></li>
<li class="toctree-l2"><a class="reference internal" href="llm_inference_async_streaming.html">Generate text in streaming</a></li>
<li class="toctree-l2"><a class="reference internal" href="llm_inference_distributed.html">Distributed LLM Generation</a></li>
<li class="toctree-l2"><a class="reference internal" href="llm_guided_decoding.html">Generate text with guided decoding</a></li>
<li class="toctree-l2 current active"><a class="current reference internal" href="#">Control generated text using logits processor</a></li>
<li class="toctree-l2"><a class="reference internal" href="llm_multilora.html">Generate text with multiple LoRA adapters</a></li>
<li class="toctree-l2"><a class="reference internal" href="llm_speculative_decoding.html">Speculative Decoding</a></li>
<li class="toctree-l2"><a class="reference internal" href="llm_runtime.html">Runtime Configuration Examples</a></li>
<li class="toctree-l2"><a class="reference internal" href="llm_sampling.html">Sampling Techniques Showcase</a></li>
<li class="toctree-l2"><a class="reference internal" href="llm_mgmn_llm_distributed.html">Run LLM-API with pytorch backend on Slurm</a></li>
<li class="toctree-l2"><a class="reference internal" href="llm_mgmn_trtllm_bench.html">Run trtllm-bench with pytorch backend on Slurm</a></li>
<li class="toctree-l2"><a class="reference internal" href="llm_mgmn_trtllm_serve.html">Run trtllm-serve with pytorch backend on Slurm</a></li>
</ul>
</details></li>
<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>
<li class="toctree-l2"><a class="reference internal" href="curl_chat_client.html">Curl Chat Client</a></li>
<li class="toctree-l2"><a class="reference internal" href="curl_chat_client_for_multimodal.html">Curl Chat Client For Multimodal</a></li>
<li class="toctree-l2"><a class="reference internal" href="curl_completion_client.html">Curl Completion Client</a></li>
<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_completion_client_for_lora.html">Openai Completion Client For Lora</a></li>
<li class="toctree-l2"><a class="reference internal" href="openai_completion_client_json_schema.html">OpenAI Completion Client with JSON Schema</a></li>
</ul>
</details></li>
</ul>
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Model Definition API</span></p>
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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>
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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>
<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 (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>
<li class="toctree-l1"><a class="reference internal" href="../performance/perf-benchmarking.html">Benchmarking</a></li>
<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>
<li class="toctree-l2"><a class="reference internal" href="../performance/performance-tuning-guide/deciding-model-sharding-strategy.html">Deciding Model Sharding Strategy</a></li>
<li class="toctree-l2"><a class="reference internal" href="../performance/performance-tuning-guide/fp8-quantization.html">FP8 Quantization</a></li>
<li class="toctree-l2"><a class="reference internal" href="../performance/performance-tuning-guide/useful-runtime-flags.html">Useful Runtime Options</a></li>
</ul>
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<li class="toctree-l1"><a class="reference internal" href="../performance/perf-analysis.html">Performance Analysis</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../reference/troubleshooting.html">Troubleshooting</a></li>
<li class="toctree-l1"><a class="reference internal" href="../reference/support-matrix.html">Support Matrix</a></li>
<li class="toctree-l1"><a class="reference internal" href="../reference/precision.html">Numerical Precision</a></li>
<li class="toctree-l1"><a class="reference internal" href="../reference/memory.html">Memory Usage of TensorRT-LLM</a></li>
<li class="toctree-l1"><a class="reference internal" href="../reference/ci-overview.html">Continuous Integration Overview</a></li>
<li class="toctree-l1"><a class="reference internal" href="../reference/dev-containers.html">Using Dev Containers</a></li>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Blogs</span></p>
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<section id="control-generated-text-using-logits-processor">
<h1>Control generated text using logits processor<a class="headerlink" href="#control-generated-text-using-logits-processor" title="Link to this heading">#</a></h1>
<p>Source <a class="github reference external" href="https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples/llm-api/llm_logits_processor.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">typing</span><span class="w"> </span><span class="kn">import</span> <span class="n">List</span><span class="p">,</span> <span class="n">Optional</span>
<span class="linenos"> 2</span>
<span class="linenos"> 3</span><span class="kn">import</span><span class="w"> </span><span class="nn">torch</span>
<span class="linenos"> 4</span><span class="kn">from</span><span class="w"> </span><span class="nn">transformers</span><span class="w"> </span><span class="kn">import</span> <span class="n">PreTrainedTokenizer</span>
<span class="linenos"> 5</span>
<span class="linenos"> 6</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="linenos"> 7</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">LogitsProcessor</span><span class="p">,</span> <span class="n">SamplingParams</span>
<span class="linenos"> 8</span>
<span class="linenos"> 9</span>
<span class="linenos"> 10</span><span class="k">def</span><span class="w"> </span><span class="nf">text_to_token</span><span class="p">(</span><span class="n">tokenizer</span><span class="p">:</span> <span class="n">PreTrainedTokenizer</span><span class="p">,</span> <span class="n">text</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">last</span><span class="p">:</span> <span class="nb">bool</span><span class="p">):</span>
<span class="linenos"> 11</span> <span class="n">tokens</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="n">text</span><span class="p">,</span> <span class="n">add_special_tokens</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="linenos"> 12</span>
<span class="linenos"> 13</span> <span class="n">max_token_count</span> <span class="o">=</span> <span class="mi">1</span>
<span class="linenos"> 14</span> <span class="n">bos_token_added</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">tokenizer</span><span class="p">,</span> <span class="s1">&#39;bos_token&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">getattr</span><span class="p">(</span>
<span class="linenos"> 15</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="s1">&#39;bos_token_id&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">in</span> <span class="n">tokens</span>
<span class="linenos"> 16</span> <span class="n">prefix_token_added</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">tokenizer</span><span class="p">,</span> <span class="s1">&#39;add_prefix_space&#39;</span><span class="p">,</span>
<span class="linenos"> 17</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">False</span>
<span class="linenos"> 18</span> <span class="k">if</span> <span class="n">bos_token_added</span> <span class="ow">or</span> <span class="n">prefix_token_added</span><span class="p">:</span>
<span class="linenos"> 19</span> <span class="n">max_token_count</span> <span class="o">=</span> <span class="mi">2</span>
<span class="linenos"> 20</span>
<span class="linenos"> 21</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">last</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">tokens</span><span class="p">)</span> <span class="o">&gt;</span> <span class="n">max_token_count</span><span class="p">:</span>
<span class="linenos"> 22</span> <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span>
<span class="linenos"> 23</span> <span class="sa">f</span><span class="s2">&quot;Can&#39;t convert </span><span class="si">{</span><span class="n">text</span><span class="si">}</span><span class="s2"> to token. It has </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">tokens</span><span class="p">)</span><span class="si">}</span><span class="s2"> tokens.&quot;</span><span class="p">)</span>
<span class="linenos"> 24</span>
<span class="linenos"> 25</span> <span class="k">return</span> <span class="n">tokens</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="linenos"> 26</span>
<span class="linenos"> 27</span>
<span class="linenos"> 28</span><span class="c1"># The recommended way to create a customized logits processor:</span>
<span class="linenos"> 29</span><span class="c1"># * Subclass LogitsProcessor and implement the processing logics in the __call__ method.</span>
<span class="linenos"> 30</span><span class="c1"># * Create an instance and pass to SamplingParams.</span>
<span class="linenos"> 31</span><span class="c1"># More LogitsProcessors references can be found at https://github.com/NVIDIA/logits-processor-zoo.</span>
<span class="linenos"> 32</span><span class="k">class</span><span class="w"> </span><span class="nc">GenLengthLogitsProcessor</span><span class="p">(</span><span class="n">LogitsProcessor</span><span class="p">):</span>
<span class="linenos"> 33</span><span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="linenos"> 34</span><span class="sd"> A logits processor that adjusts the likelihood of the end-of-sequence (EOS) token</span>
<span class="linenos"> 35</span><span class="sd"> based on the length of the generated sequence, encouraging or discouraging shorter answers.</span>
<span class="linenos"> 36</span><span class="sd"> WARNING: Create a new object before every model.generate call since token_count is accumulated.</span>
<span class="linenos"> 37</span>
<span class="linenos"> 38</span><span class="sd"> Parameters</span>
<span class="linenos"> 39</span><span class="sd"> ----------</span>
<span class="linenos"> 40</span><span class="sd"> tokenizer: The tokenizer used by the LLM.</span>
<span class="linenos"> 41</span><span class="sd"> boost_factor (float): A factor to boost the likelihood of the EOS token as the sequence length increases.</span>
<span class="linenos"> 42</span><span class="sd"> Suggested value range is [-1.0, 1.0]. Negative values are used for the opposite effect.</span>
<span class="linenos"> 43</span><span class="sd"> p (int, optional): The power to which the token count is raised when computing the boost value. Default is 2.</span>
<span class="linenos"> 44</span><span class="sd"> complete_sentences (bool, optional): If True, boosts EOS token likelihood only when the last token is a full stop</span>
<span class="linenos"> 45</span><span class="sd"> or a new line. Default is False.</span>
<span class="linenos"> 46</span>
<span class="linenos"> 47</span><span class="sd"> &quot;&quot;&quot;</span>
<span class="linenos"> 48</span>
<span class="linenos"> 49</span> <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
<span class="linenos"> 50</span> <span class="n">tokenizer</span><span class="p">,</span>
<span class="linenos"> 51</span> <span class="n">boost_factor</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
<span class="linenos"> 52</span> <span class="n">p</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span>
<span class="linenos"> 53</span> <span class="n">complete_sentences</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">):</span>
<span class="linenos"> 54</span> <span class="bp">self</span><span class="o">.</span><span class="n">eos_token</span> <span class="o">=</span> <span class="n">tokenizer</span><span class="o">.</span><span class="n">eos_token_id</span>
<span class="linenos"> 55</span> <span class="bp">self</span><span class="o">.</span><span class="n">boost_factor</span> <span class="o">=</span> <span class="n">boost_factor</span>
<span class="linenos"> 56</span> <span class="bp">self</span><span class="o">.</span><span class="n">p</span> <span class="o">=</span> <span class="n">p</span>
<span class="linenos"> 57</span> <span class="bp">self</span><span class="o">.</span><span class="n">token_count</span> <span class="o">=</span> <span class="mi">0</span>
<span class="linenos"> 58</span> <span class="bp">self</span><span class="o">.</span><span class="n">full_stop_token</span> <span class="o">=</span> <span class="n">text_to_token</span><span class="p">(</span><span class="n">tokenizer</span><span class="p">,</span>
<span class="linenos"> 59</span> <span class="s2">&quot;It is a sentence.&quot;</span><span class="p">,</span>
<span class="linenos"> 60</span> <span class="n">last</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="linenos"> 61</span> <span class="bp">self</span><span class="o">.</span><span class="n">new_line_token</span> <span class="o">=</span> <span class="n">text_to_token</span><span class="p">(</span><span class="n">tokenizer</span><span class="p">,</span>
<span class="linenos"> 62</span> <span class="s2">&quot;It is a new line</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">,</span>
<span class="linenos"> 63</span> <span class="n">last</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="linenos"> 64</span> <span class="bp">self</span><span class="o">.</span><span class="n">complete_sentences</span> <span class="o">=</span> <span class="n">complete_sentences</span>
<span class="linenos"> 65</span>
<span class="linenos"> 66</span> <span class="k">def</span><span class="w"> </span><span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">req_ids</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">logits</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">ids</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">]],</span>
<span class="linenos"> 67</span> <span class="n">stream_ptr</span><span class="p">,</span> <span class="n">client_id</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]):</span>
<span class="linenos"> 68</span> <span class="n">boost_val</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">boost_factor</span> <span class="o">*</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">token_count</span><span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="mi">10</span><span class="o">**</span>
<span class="linenos"> 69</span> <span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="p">)</span>
<span class="linenos"> 70</span>
<span class="linenos"> 71</span> <span class="n">stream</span> <span class="o">=</span> <span class="kc">None</span> <span class="k">if</span> <span class="n">stream_ptr</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">ExternalStream</span><span class="p">(</span>
<span class="linenos"> 72</span> <span class="n">stream_ptr</span><span class="p">)</span>
<span class="linenos"> 73</span>
<span class="linenos"> 74</span> <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">stream</span><span class="p">(</span><span class="n">stream</span><span class="p">):</span>
<span class="linenos"> 75</span> <span class="n">ids</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">LongTensor</span><span class="p">(</span><span class="n">ids</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">logits</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">non_blocking</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="linenos"> 76</span>
<span class="linenos"> 77</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">complete_sentences</span><span class="p">:</span>
<span class="linenos"> 78</span> <span class="n">enabled</span> <span class="o">=</span> <span class="p">(</span><span class="n">ids</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">full_stop_token</span><span class="p">)</span> <span class="o">|</span> <span class="p">(</span>
<span class="linenos"> 79</span> <span class="n">ids</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">new_line_token</span><span class="p">)</span>
<span class="linenos"> 80</span> <span class="n">logits</span><span class="p">[:,</span> <span class="p">:,</span> <span class="bp">self</span><span class="o">.</span><span class="n">eos_token</span><span class="p">]</span> <span class="o">+=</span> <span class="n">enabled</span> <span class="o">*</span> <span class="n">boost_val</span>
<span class="linenos"> 81</span> <span class="k">else</span><span class="p">:</span>
<span class="linenos"> 82</span> <span class="n">logits</span><span class="p">[:,</span> <span class="p">:,</span> <span class="bp">self</span><span class="o">.</span><span class="n">eos_token</span><span class="p">]</span> <span class="o">+=</span> <span class="n">boost_val</span>
<span class="linenos"> 83</span>
<span class="linenos"> 84</span> <span class="bp">self</span><span class="o">.</span><span class="n">token_count</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="linenos"> 85</span>
<span class="linenos"> 86</span>
<span class="linenos"> 87</span><span class="k">def</span><span class="w"> </span><span class="nf">main</span><span class="p">():</span>
<span class="linenos"> 88</span>
<span class="linenos"> 89</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"> 90</span>
<span class="linenos"> 91</span> <span class="c1"># Sample prompts</span>
<span class="linenos"> 92</span> <span class="n">prompts</span> <span class="o">=</span> <span class="p">[</span>
<span class="linenos"> 93</span> <span class="s2">&quot;The future of AI is&quot;</span><span class="p">,</span>
<span class="linenos"> 94</span> <span class="s2">&quot;The future of AI is&quot;</span><span class="p">,</span>
<span class="linenos"> 95</span> <span class="p">]</span>
<span class="linenos"> 96</span>
<span class="linenos"> 97</span> <span class="c1"># Generate text</span>
<span class="linenos"> 98</span> <span class="k">for</span> <span class="n">prompt_id</span><span class="p">,</span> <span class="n">prompt</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">prompts</span><span class="p">):</span>
<span class="linenos"> 99</span> <span class="k">if</span> <span class="n">prompt_id</span> <span class="o">%</span> <span class="mi">2</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="linenos">100</span> <span class="c1"># Without logit processor</span>
<span class="linenos">101</span> <span class="n">sampling_params</span> <span class="o">=</span> <span class="n">SamplingParams</span><span class="p">(</span><span class="n">top_p</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">max_tokens</span><span class="o">=</span><span class="mi">200</span><span class="p">)</span>
<span class="linenos">102</span> <span class="k">else</span><span class="p">:</span>
<span class="linenos">103</span> <span class="c1"># Each prompt can be specified with a logits processor at runtime</span>
<span class="linenos">104</span> <span class="n">sampling_params</span> <span class="o">=</span> <span class="n">SamplingParams</span><span class="p">(</span>
<span class="linenos">105</span> <span class="n">temperature</span><span class="o">=</span><span class="mf">0.8</span><span class="p">,</span>
<span class="linenos">106</span> <span class="n">top_p</span><span class="o">=</span><span class="mf">0.95</span><span class="p">,</span>
<span class="linenos">107</span> <span class="n">logits_processor</span><span class="o">=</span><span class="n">GenLengthLogitsProcessor</span><span class="p">(</span>
<span class="linenos">108</span> <span class="n">llm</span><span class="o">.</span><span class="n">tokenizer</span><span class="p">,</span> <span class="n">boost_factor</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">complete_sentences</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
<span class="linenos">109</span>
<span class="linenos">110</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="p">)</span>
<span class="linenos">111</span> <span class="nb">print</span><span class="p">(</span>
<span class="linenos">112</span> <span class="sa">f</span><span class="s2">&quot;Prompt: </span><span class="si">{</span><span class="n">output</span><span class="o">.</span><span class="n">prompt</span><span class="si">!r}</span><span class="s2">, Generated text: </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">&quot;</span>
<span class="linenos">113</span> <span class="p">)</span>
<span class="linenos">114</span>
<span class="linenos">115</span> <span class="c1"># Got output like:</span>
<span class="linenos">116</span> <span class="c1"># Prompt (original): &quot;bright, and it&#39;s not just for big companies. Small businesses can also benefit from AI technology. Here are some ways:\n\n1. Improved customer service: AI can help businesses provide better customer service by analyzing customer data and providing personalized recommendations.</span>
<span class="linenos">117</span> <span class="c1"># This can help businesses improve their customer experience and increase customer loyalty.\n\n2. Increased productivity: AI can help businesses automate repetitive tasks, freeing up employees to focus on more complex tasks. This can</span>
<span class="linenos">118</span> <span class="c1"># help businesses increase productivity and reduce costs.\n\n3. Enhanced marketing: AI can help businesses create more personalized marketing campaigns by analyzing customer data and targeting specific audiences. This can help businesses</span>
<span class="linenos">119</span> <span class="c1"># increase their marketing ROI and drive more sales.\n\n4. Improved supply chain management: AI can help businesses optimize their supply chain by analyzing data on demand,&quot;&#39;</span>
<span class="linenos">120</span> <span class="c1">#</span>
<span class="linenos">121</span> <span class="c1"># Prompt (with GenLenthLogitsProcesor): &quot;bright, and it&#39;s not just for big companies. Small businesses can also benefit from AI technology.&quot;</span>
<span class="linenos">122</span>
<span class="linenos">123</span>
<span class="linenos">124</span><span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
<span class="linenos">125</span> <span class="n">main</span><span class="p">()</span>
</pre></div>
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