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Awni Hannun
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<meta charset="utf-8" />
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<title>mlx.nn.Transformer &#8212; MLX 0.3.0 documentation</title>
<title>mlx.nn.Transformer &#8212; MLX 0.5.0 documentation</title>
@@ -46,7 +46,7 @@
<script>DOCUMENTATION_OPTIONS.pagename = 'python/nn/_autosummary/mlx.nn.Transformer';</script>
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@@ -134,8 +134,8 @@
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@@ -233,6 +233,9 @@
<li class="toctree-l2"><a class="reference internal" href="../../_autosummary/mlx.core.argpartition.html">mlx.core.argpartition</a></li>
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@@ -240,6 +243,7 @@
<li class="toctree-l2"><a class="reference internal" href="../../_autosummary/mlx.core.convolve.html">mlx.core.convolve</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../../_autosummary/mlx.core.dequantize.html">mlx.core.dequantize</a></li>
@@ -436,19 +440,31 @@
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<li class="toctree-l3"><a class="reference internal" href="mlx.nn.Step.html">mlx.nn.Step</a></li>
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@@ -502,9 +518,11 @@
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@@ -692,7 +710,7 @@ document.write(`
<h1>mlx.nn.Transformer<a class="headerlink" href="#mlx-nn-transformer" title="Permalink to this heading">#</a></h1>
<dl class="py class">
<dt class="sig sig-object py" id="mlx.nn.Transformer">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">mlx.nn.</span></span><span class="sig-name descname"><span class="pre">Transformer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">dims:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">512,</span> <span class="pre">num_heads:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">8,</span> <span class="pre">num_encoder_layers:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">6,</span> <span class="pre">num_decoder_layers:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">6,</span> <span class="pre">mlp_dims:</span> <span class="pre">~typing.Optional[int]</span> <span class="pre">=</span> <span class="pre">None,</span> <span class="pre">dropout:</span> <span class="pre">float</span> <span class="pre">=</span> <span class="pre">0.0,</span> <span class="pre">activation:</span> <span class="pre">~typing.Callable[[~typing.Any],</span> <span class="pre">~typing.Any]</span> <span class="pre">=</span> <span class="pre">&lt;function</span> <span class="pre">relu&gt;,</span> <span class="pre">custom_encoder:</span> <span class="pre">~typing.Optional[~typing.Any]</span> <span class="pre">=</span> <span class="pre">None,</span> <span class="pre">custom_decoder:</span> <span class="pre">~typing.Optional[~typing.Any]</span> <span class="pre">=</span> <span class="pre">None,</span> <span class="pre">norm_first:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">True,</span> <span class="pre">checkpoint:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False</span></em><span class="sig-paren">)</span><a class="headerlink" href="#mlx.nn.Transformer" title="Permalink to this definition">#</a></dt>
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">mlx.nn.</span></span><span class="sig-name descname"><span class="pre">Transformer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">dims:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">512,</span> <span class="pre">num_heads:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">8,</span> <span class="pre">num_encoder_layers:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">6,</span> <span class="pre">num_decoder_layers:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">6,</span> <span class="pre">mlp_dims:</span> <span class="pre">~typing.Optional[int]</span> <span class="pre">=</span> <span class="pre">None,</span> <span class="pre">dropout:</span> <span class="pre">float</span> <span class="pre">=</span> <span class="pre">0.0,</span> <span class="pre">activation:</span> <span class="pre">~typing.Callable[[~typing.Any],</span> <span class="pre">~typing.Any]</span> <span class="pre">=</span> <span class="pre">&lt;built-in</span> <span class="pre">method</span> <span class="pre">relu</span> <span class="pre">of</span> <span class="pre">PyCapsule</span> <span class="pre">object&gt;,</span> <span class="pre">custom_encoder:</span> <span class="pre">~typing.Optional[~typing.Any]</span> <span class="pre">=</span> <span class="pre">None,</span> <span class="pre">custom_decoder:</span> <span class="pre">~typing.Optional[~typing.Any]</span> <span class="pre">=</span> <span class="pre">None,</span> <span class="pre">norm_first:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">True,</span> <span class="pre">checkpoint:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False</span></em><span class="sig-paren">)</span><a class="headerlink" href="#mlx.nn.Transformer" title="Permalink to this definition">#</a></dt>
<dd><p>Implements a standard Transformer model.</p>
<p>The implementation is based on <a class="reference external" href="https://arxiv.org/abs/1706.03762">Attention Is All You Need</a>.</p>
<p>The Transformer model contains an encoder and a decoder. The encoder
@@ -756,11 +774,11 @@ Default: <code class="docutils literal notranslate"><span class="pre">False</spa
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