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<h1 class="relative group"><a id="quickstart" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#quickstart"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a>
	<span>Quickstart
	</span></h1>

<p>Here is an example on how to combine magnitude pruning with dynamic quantization while fine-tuning a DistilBERT on the sst-2 task.
Note that quantization is currently only supported for CPUs (only CPU backends are available), so we will not be utilizing GPUs / CUDA in this example.</p>
<p>To apply our pruning methodology, we need to create an instance of IncTrainer, which is very similar to the 🤗 Transformers <a href="https://huggingface.co/docs/transformers/main_classes/trainer" rel="nofollow">Trainer</a>.
We will fine-tune our model for 3 epochs while applying pruning.</p>

	<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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	Copied</div></button></div>
	<pre><!-- HTML_TAG_START --><span class="hljs-deletion">-from transformers import Trainer</span>
<span class="hljs-addition">+from optimum.intel.neural_compressor import IncTrainer</span>

# Initialize our IncTrainer
<span class="hljs-deletion">-trainer = Trainer(</span>
<span class="hljs-addition">+trainer = IncTrainer(</span>
    model=model,
    args=TrainingArguments(output_dir, num_train_epochs=3.0),
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    compute_metrics=compute_metrics,
    tokenizer=tokenizer,
    data_collator=default_data_collator,
)<!-- HTML_TAG_END --></pre></div>
<p>To apply our quantization and pruning methodologies, we first need to create the corresponding configuration describing how we want those methodologies to be applied :</p>

	<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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	<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> optimum.intel.neural_compressor <span class="hljs-keyword">import</span> IncOptimizer, IncPruner, IncQuantizer
<span class="hljs-keyword">from</span> optimum.intel.neural_compressor.configuration <span class="hljs-keyword">import</span> IncPruningConfig, IncQuantizationConfig

<span class="hljs-comment"># The targeted sparsity is set to 10%</span>
target_sparsity = <span class="hljs-number">0.1</span>
config_path = <span class="hljs-string">&quot;echarlaix/distilbert-sst2-inc-dynamic-quantization-magnitude-pruning-0.1&quot;</span>
<span class="hljs-comment"># Load the quantization configuration detailing the quantization we wish to apply</span>
quantization_config = IncQuantizationConfig.from_pretrained(config_path, config_file_name=<span class="hljs-string">&quot;quantization.yml&quot;</span>)
<span class="hljs-comment"># Load the pruning configuration detailing the pruning we wish to apply</span>
pruning_config = IncPruningConfig.from_pretrained(config_path, config_file_name=<span class="hljs-string">&quot;prune.yml&quot;</span>)

<span class="hljs-comment"># Instantiate our IncQuantizer using the desired configuration</span>
quantizer = IncQuantizer(quantization_config, eval_func=eval_func)
<span class="hljs-comment"># Instantiate our IncPruner using the desired configuration</span>
pruner = IncPruner(pruning_config, eval_func=eval_func, train_func=train_func)
optimizer = IncOptimizer(model, quantizer=quantizer, pruner=pruner)
<span class="hljs-comment"># Apply pruning and quantization </span>
optimized_model = optimizer.fit()

<span class="hljs-comment"># Save the resulting model and its corresponding configuration in the given directory</span>
optimizer.save_pretrained(output_dir)
<!-- HTML_TAG_END --></pre></div>
<p>To load a quantized model hosted locally or on the 🤗 hub, you can do as follows :</p>

	<div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5   text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
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	Copied</div></button></div>
	<pre><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> optimum.intel.neural_compressor.quantization <span class="hljs-keyword">import</span> IncQuantizedModelForSequenceClassification

loaded_model_from_hub = IncQuantizedModelForSequenceClassification.from_pretrained(
    <span class="hljs-string">&quot;Intel/distilbert-base-uncased-finetuned-sst-2-english-int8-static&quot;</span>
)<!-- HTML_TAG_END --></pre></div>
<p>You can load many more quantized models hosted on the hub under the Intel organization <a href="https://huggingface.co/Intel" rel="nofollow"><code>here</code></a>.</p>
<p>Check out the <a href="https://github.com/huggingface/optimum-intel/tree/main/examples" rel="nofollow"><code>examples</code></a> directory for more sophisticated usage.</p>


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