chore: import upstream snapshot with attribution

doc/source/serve/doc_code/getting_started/models.py

@@ -0,0 +1,83 @@
+# flake8: noqa
+
+# __start_translation_model__
+# File name: model.py
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+
+
+class Translator:
+    def __init__(self):
+        # Load model
+        self.tokenizer = AutoTokenizer.from_pretrained("t5-small")
+        self.model = AutoModelForSeq2SeqLM.from_pretrained("t5-small")
+
+    def translate(self, text: str) -> str:
+        # Run inference
+        input_ids = self.tokenizer(
+            f"translate English to French: {text}", return_tensors="pt"
+        ).input_ids
+        output_ids = self.model.generate(
+            input_ids, num_beams=4, early_stopping=True, max_length=300
+        )
+
+        # Post-process output to return only the translation text
+        translation = self.tokenizer.decode(
+            output_ids[0], skip_special_tokens=True, clean_up_tokenization_spaces=False
+        )
+
+        return translation
+
+
+translator = Translator()
+
+translation = translator.translate("Hello world!")
+print(translation)
+# __end_translation_model__
+
+# Test model behavior
+assert translation == "Bonjour monde!"
+
+
+# __start_summarization_model__
+# File name: summary_model.py
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+
+
+class Summarizer:
+    def __init__(self):
+        # Load model
+        self.tokenizer = AutoTokenizer.from_pretrained("t5-small")
+        self.model = AutoModelForSeq2SeqLM.from_pretrained("t5-small")
+
+    def summarize(self, text: str) -> str:
+        # Run inference
+        input_ids = self.tokenizer(f"summarize: {text}", return_tensors="pt").input_ids
+        output_ids = self.model.generate(
+            input_ids,
+            num_beams=4,
+            early_stopping=True,
+            length_penalty=2.0,
+            no_repeat_ngram_size=3,
+            min_length=5,
+            max_length=15,
+        )
+
+        # Post-process output to return only the summary text
+        summary = self.tokenizer.decode(
+            output_ids[0], skip_special_tokens=True, clean_up_tokenization_spaces=False
+        )
+
+        return summary
+
+
+summarizer = Summarizer()
+
+summary = summarizer.summarize(
+    "It was the best of times, it was the worst of times, it was the age "
+    "of wisdom, it was the age of foolishness, it was the epoch of belief"
+)
+print(summary)
+# __end_summarization_model__
+
+# Test model behavior
+assert summary == "it was the best of times, it was worst of times ."