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Available local models
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<div class="md-content" data-md-component="content">
<article class="md-content__inner md-typeset">
<h1>Vision models</h1>
<p>The <code>VlmPipeline</code> in Docling allows you to convert documents end-to-end using a vision-language model.</p>
<p>Docling supports vision-language models which output:</p>
<ul>
<li>DocTags (e.g. <a href="https://huggingface.co/ds4sd/SmolDocling-256M-preview">SmolDocling</a>), the preferred choice</li>
<li>Markdown</li>
<li>HTML</li>
</ul>
<p>For running Docling using local models with the <code>VlmPipeline</code>:</p>
<div class="tabbed-set tabbed-alternate" data-tabs="1:2"><input checked="checked" id="cli" name="__tabbed_1" type="radio" /><input id="python" name="__tabbed_1" type="radio" /><div class="tabbed-labels"><label for="cli">CLI</label><label for="python">Python</label></div>
<div class="tabbed-content">
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code>docling<span class="w"> </span>--pipeline<span class="w"> </span>vlm<span class="w"> </span>FILE
</code></pre></div>
</div>
<div class="tabbed-block">
<p>See also the example <a href="../../examples/minimal_vlm_pipeline/">minimal_vlm_pipeline.py</a>.</p>
<div class="highlight"><pre><span></span><code><span class="kn">from</span><span class="w"> </span><span class="nn">docling.datamodel.base_models</span><span class="w"> </span><span class="kn">import</span> <span class="n">InputFormat</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">docling.document_converter</span><span class="w"> </span><span class="kn">import</span> <span class="n">DocumentConverter</span><span class="p">,</span> <span class="n">PdfFormatOption</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">docling.pipeline.vlm_pipeline</span><span class="w"> </span><span class="kn">import</span> <span class="n">VlmPipeline</span>
<span class="n">converter</span> <span class="o">=</span> <span class="n">DocumentConverter</span><span class="p">(</span>
<span class="n">format_options</span><span class="o">=</span><span class="p">{</span>
<span class="n">InputFormat</span><span class="o">.</span><span class="n">PDF</span><span class="p">:</span> <span class="n">PdfFormatOption</span><span class="p">(</span>
<span class="n">pipeline_cls</span><span class="o">=</span><span class="n">VlmPipeline</span><span class="p">,</span>
<span class="p">),</span>
<span class="p">}</span>
<span class="p">)</span>
<span class="n">doc</span> <span class="o">=</span> <span class="n">converter</span><span class="o">.</span><span class="n">convert</span><span class="p">(</span><span class="n">source</span><span class="o">=</span><span class="s2">&quot;FILE&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">document</span>
</code></pre></div>
</div>
</div>
</div>
<h2 id="available-local-models">Available local models</h2>
<p>By default, the vision-language models are running locally.
Docling allows to choose between the Hugging Face <a href="https://github.com/huggingface/transformers">Transformers</a> framework and the <a href="https://github.com/Blaizzy/mlx-vlm">MLX</a> (for Apple devices with MPS acceleration) one.</p>
<p>The following table reports the models currently available out-of-the-box.</p>
<table>
<thead>
<tr>
<th>Model instance</th>
<th>Model</th>
<th>Framework</th>
<th>Device</th>
<th>Num pages</th>
<th>Inference time (sec)</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>vlm_model_specs.SMOLDOCLING_TRANSFORMERS</code></td>
<td><a href="https://huggingface.co/ds4sd/SmolDocling-256M-preview">ds4sd/SmolDocling-256M-preview</a></td>
<td><code>Transformers/AutoModelForVision2Seq</code></td>
<td>MPS</td>
<td>1</td>
<td>102.212</td>
</tr>
<tr>
<td><code>vlm_model_specs.SMOLDOCLING_MLX</code></td>
<td><a href="https://huggingface.co/ds4sd/SmolDocling-256M-preview-mlx-bf16">ds4sd/SmolDocling-256M-preview-mlx-bf16</a></td>
<td><code>MLX</code></td>
<td>MPS</td>
<td>1</td>
<td>6.15453</td>
</tr>
<tr>
<td><code>vlm_model_specs.QWEN25_VL_3B_MLX</code></td>
<td><a href="https://huggingface.co/mlx-community/Qwen2.5-VL-3B-Instruct-bf16">mlx-community/Qwen2.5-VL-3B-Instruct-bf16</a></td>
<td><code>MLX</code></td>
<td>MPS</td>
<td>1</td>
<td>23.4951</td>
</tr>
<tr>
<td><code>vlm_model_specs.PIXTRAL_12B_MLX</code></td>
<td><a href="https://huggingface.co/mlx-community/pixtral-12b-bf16">mlx-community/pixtral-12b-bf16</a></td>
<td><code>MLX</code></td>
<td>MPS</td>
<td>1</td>
<td>308.856</td>
</tr>
<tr>
<td><code>vlm_model_specs.GEMMA3_12B_MLX</code></td>
<td><a href="https://huggingface.co/mlx-community/gemma-3-12b-it-bf16">mlx-community/gemma-3-12b-it-bf16</a></td>
<td><code>MLX</code></td>
<td>MPS</td>
<td>1</td>
<td>378.486</td>
</tr>
<tr>
<td><code>vlm_model_specs.GRANITE_VISION_TRANSFORMERS</code></td>
<td><a href="https://huggingface.co/ibm-granite/granite-vision-3.2-2b">ibm-granite/granite-vision-3.2-2b</a></td>
<td><code>Transformers/AutoModelForVision2Seq</code></td>
<td>MPS</td>
<td>1</td>
<td>104.75</td>
</tr>
<tr>
<td><code>vlm_model_specs.PHI4_TRANSFORMERS</code></td>
<td><a href="https://huggingface.co/microsoft/Phi-4-multimodal-instruct">microsoft/Phi-4-multimodal-instruct</a></td>
<td><code>Transformers/AutoModelForCasualLM</code></td>
<td>CPU</td>
<td>1</td>
<td>1175.67</td>
</tr>
<tr>
<td><code>vlm_model_specs.PIXTRAL_12B_TRANSFORMERS</code></td>
<td><a href="https://huggingface.co/mistral-community/pixtral-12b">mistral-community/pixtral-12b</a></td>
<td><code>Transformers/AutoModelForVision2Seq</code></td>
<td>CPU</td>
<td>1</td>
<td>1828.21</td>
</tr>
</tbody>
</table>
<p><em>Inference time is computed on a Macbook M3 Max using the example page <code>tests/data/pdf/2305.03393v1-pg9.pdf</code>. The comparison is done with the example <a href="../../examples/compare_vlm_models/">compare_vlm_models.py</a>.</em></p>
<p>For choosing the model, the code snippet above can be extended as follow</p>
<div class="highlight"><pre><span></span><code><span class="kn">from</span><span class="w"> </span><span class="nn">docling.datamodel.base_models</span><span class="w"> </span><span class="kn">import</span> <span class="n">InputFormat</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">docling.document_converter</span><span class="w"> </span><span class="kn">import</span> <span class="n">DocumentConverter</span><span class="p">,</span> <span class="n">PdfFormatOption</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">docling.pipeline.vlm_pipeline</span><span class="w"> </span><span class="kn">import</span> <span class="n">VlmPipeline</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">docling.datamodel.pipeline_options</span><span class="w"> </span><span class="kn">import</span> <span class="p">(</span>
<span class="n">VlmPipelineOptions</span><span class="p">,</span>
<span class="p">)</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">docling.datamodel</span><span class="w"> </span><span class="kn">import</span> <span class="n">vlm_model_specs</span>
<span class="n">pipeline_options</span> <span class="o">=</span> <span class="n">VlmPipelineOptions</span><span class="p">(</span>
<span class="n">vlm_options</span><span class="o">=</span><span class="n">vlm_model_specs</span><span class="o">.</span><span class="n">SMOLDOCLING_MLX</span><span class="p">,</span> <span class="c1"># &lt;-- change the model here</span>
<span class="p">)</span>
<span class="n">converter</span> <span class="o">=</span> <span class="n">DocumentConverter</span><span class="p">(</span>
<span class="n">format_options</span><span class="o">=</span><span class="p">{</span>
<span class="n">InputFormat</span><span class="o">.</span><span class="n">PDF</span><span class="p">:</span> <span class="n">PdfFormatOption</span><span class="p">(</span>
<span class="n">pipeline_cls</span><span class="o">=</span><span class="n">VlmPipeline</span><span class="p">,</span>
<span class="n">pipeline_options</span><span class="o">=</span><span class="n">pipeline_options</span><span class="p">,</span>
<span class="p">),</span>
<span class="p">}</span>
<span class="p">)</span>
<span class="n">doc</span> <span class="o">=</span> <span class="n">converter</span><span class="o">.</span><span class="n">convert</span><span class="p">(</span><span class="n">source</span><span class="o">=</span><span class="s2">&quot;FILE&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">document</span>
</code></pre></div>
<h3 id="other-models">Other models</h3>
<p>Other models can be configured by directly providing the Hugging Face <code>repo_id</code>, the prompt and a few more options.</p>
<p>For example:</p>
<div class="highlight"><pre><span></span><code><span class="kn">from</span><span class="w"> </span><span class="nn">docling.datamodel.pipeline_options_vlm_model</span><span class="w"> </span><span class="kn">import</span> <span class="n">InlineVlmOptions</span><span class="p">,</span> <span class="n">InferenceFramework</span><span class="p">,</span> <span class="n">TransformersModelType</span>
<span class="n">pipeline_options</span> <span class="o">=</span> <span class="n">VlmPipelineOptions</span><span class="p">(</span>
<span class="n">vlm_options</span><span class="o">=</span><span class="n">InlineVlmOptions</span><span class="p">(</span>
<span class="n">repo_id</span><span class="o">=</span><span class="s2">&quot;ibm-granite/granite-vision-3.2-2b&quot;</span><span class="p">,</span>
<span class="n">prompt</span><span class="o">=</span><span class="s2">&quot;Convert this page to markdown. Do not miss any text and only output the bare markdown!&quot;</span><span class="p">,</span>
<span class="n">response_format</span><span class="o">=</span><span class="n">ResponseFormat</span><span class="o">.</span><span class="n">MARKDOWN</span><span class="p">,</span>
<span class="n">inference_framework</span><span class="o">=</span><span class="n">InferenceFramework</span><span class="o">.</span><span class="n">TRANSFORMERS</span><span class="p">,</span>
<span class="n">transformers_model_type</span><span class="o">=</span><span class="n">TransformersModelType</span><span class="o">.</span><span class="n">AUTOMODEL_VISION2SEQ</span><span class="p">,</span>
<span class="n">supported_devices</span><span class="o">=</span><span class="p">[</span>
<span class="n">AcceleratorDevice</span><span class="o">.</span><span class="n">CPU</span><span class="p">,</span>
<span class="n">AcceleratorDevice</span><span class="o">.</span><span class="n">CUDA</span><span class="p">,</span>
<span class="n">AcceleratorDevice</span><span class="o">.</span><span class="n">MPS</span><span class="p">,</span>
<span class="p">],</span>
<span class="n">scale</span><span class="o">=</span><span class="mf">2.0</span><span class="p">,</span>
<span class="n">temperature</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span>
<span class="p">)</span>
<span class="p">)</span>
</code></pre></div>
<h2 id="remote-models">Remote models</h2>
<p>Additionally to local models, the <code>VlmPipeline</code> allows to offload the inference to a remote service hosting the models.
Many remote inference services are provided, the key requirement is to offer an OpenAI-compatible API. This includes vLLM, Ollama, etc.</p>
<p>More examples on how to connect with the remote inference services can be found in the following examples:</p>
<ul>
<li><a href="../../examples/vlm_pipeline_api_model/">vlm_pipeline_api_model.py</a></li>
</ul>
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