Update minimal-example.mdx

Author: okhat

docs/quick-start/minimal-example.mdx

@@ -16,25 +16,15 @@ Before we delve into the example, let's ensure our environment is properly confi
 
 ```python
 import dspy
-# from dspy.datasets import DataLoader
-from dspy.datasets.gsm8k import gsm8k_metric
+from dspy.datasets.gsm8k import GSM8K, gsm8k_metric
 
 # Set up the LM
 turbo = dspy.OpenAI(model='gpt-3.5-turbo-instruct', max_tokens=250)
 dspy.settings.configure(lm=turbo)
 
 # Load math questions from the GSM8K dataset
-dl = DataLoader()
-
-gms8k = dl.from_huggingface('gsm8k', "main", input_keys=('question'))
-
-gsm8k_train = dl.sample(gms8k['train'], 10)
-gsm8k_valid = dl.sample(gms8k['test'], 10)
-
-# Split into test and dev sets
-split = dl.train_test_split(gsm8k_valid, train_size=0.5)
-gsm8k_valid = split['train']
-gsm8k_test = split['test']
+gms8k = GSM8K()
+gsm8k_trainset, gsm8k_devset = gms8k.train[:10], gms8k.dev[:10]
 ```
 
 ## Define the Module
@@ -63,7 +53,7 @@ config = dict(max_bootstrapped_demos=4, max_labeled_demos=4)
 
 # Optimize! Use the `gms8k_metric` here. In general, the metric is going to tell the optimizer how well it's doing.
 teleprompter = BootstrapFewShot(metric=gsm8k_metric, **config)
-optimized_cot = teleprompter.compile(CoT(), trainset=gsm8k_train, valset=gsm8k_valid)
+optimized_cot = teleprompter.compile(CoT(), trainset=gsm8k_trainset, valset=gsm8k_devset)
 ```
 
 ## Evaluate
@@ -97,4 +87,4 @@ Feel free to adapt and expand upon this example to suit your specific use case w
 
 ***
 
-<AuthorDetails name="Herumb Shandilya"/>
+<AuthorDetails name="Herumb Shandilya"/>