Change usage section

README.md

@@ -23,21 +23,30 @@ This enables to specify:
 ## Usage
 
 The model is instantiated the same way as in the Transformers library.
-The only difference is that there are a few new training arguments specific to HPUs:
-
-```
-from optimum.habana import GaudiTrainer, GaudiTrainingArguments
-from transformers import SwinForImageClassification
-model = SwinForImageClassification.from_pretrained('microsoft/swin-base-patch4-window7-224-in22k')
-args = GaudiTrainingArguments(
-    output_dir="/tmp/output_dir",
-    use_habana=True,
-    use_lazy_mode=True,
-    gaudi_config_name="Habana/swin",
-)
-trainer = GaudiTrainer(
-    model=model,
-    args=args,
-)
-trainer.train()
+The only difference is that there are a few new training arguments specific to HPUs.
+
+[Here](https://github.com/huggingface/optimum-habana/blob/main/examples/image-classification/run_image_classification.py) is an image classification example script to fine-tune a model. You can run it with Swin with the following command:
+```bash
+python run_image_classification.py \
+    --model_name_or_path microsoft/swin-base-patch4-window7-224 \
+    --dataset_name cifar10 \
+    --output_dir /tmp/outputs/ \
+    --remove_unused_columns False \
+    --do_train \
+    --do_eval \
+    --learning_rate 2e-5 \
+    --num_train_epochs 5 \
+    --per_device_train_batch_size 32 \
+    --per_device_eval_batch_size 32 \
+    --evaluation_strategy epoch \
+    --save_strategy epoch \
+    --load_best_model_at_end True \
+    --save_total_limit 3 \
+    --seed 1337 \
+    --use_habana \
+    --use_lazy_mode \
+    --gaudi_config_name Habana/swin \
+    --throughput_warmup_steps 2
 ```
+
+Check the [documentation](https://huggingface.co/docs/optimum/habana/index) out for more advanced usage and examples.