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from optimum.bettertransformer import BetterTransformer
model_hf = AutoModelForSequenceClassification.from_pretrained("bert-base-cased")
model = BetterTransformer.transform(model_hf, keep_original_model=True)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForSequenceClassification
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.bettertransformer <span class="hljs-keyword">import</span> BetterTransformer
<span class="hljs-meta">&gt;&gt;&gt; </span>model_hf = AutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = BetterTransformer.transform(model_hf, keep_original_model=<span class="hljs-literal">True</span>)`}}),{c(){_=o("meta"),ot=u(),w=o("h1"),T=o("a"),ve=o("span"),Ze(S.$$.fragment),It=u(),me=o("span"),Bt=i("Overview"),at=u(),k=o("p"),Pt=i("\u{1F917} Optimum provides an integration with "),_e=o("code"),St=i("BetterTransformer"),Ot=i(", a stable API from PyTorch to benefit from interesting speedups on CPU & GPU through sparsity and fused kernels."),lt=u(),E=o("h2"),x=o("a"),we=o("span"),Ze(O.$$.fragment),Ct=u(),Ee=o("span"),Mt=i("Quickstart"),st=u(),L=o("p"),Dt=i("Since its 1.13 version, PyTorch released the stable version of "),ge=o("code"),Rt=i("BetterTransformer"),Nt=i(` in its library. You can benefit from interesting speedup on most consumer-type devices, including CPUs, older and newer versions of NIVIDIA GPUs.
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