Upload h2oai_pipeline.py

h2oai_pipeline.py

@@ -0,0 +1,42 @@
+from transformers import TextGenerationPipeline
+from transformers.pipelines.text_generation import ReturnType
+
+STYLE = "<|user|>{instruction}</s><|assistant|>"
+
+
+class H2OTextGenerationPipeline(TextGenerationPipeline):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.prompt = STYLE
+
+    def preprocess(
+        self, prompt_text, prefix="", handle_long_generation=None, **generate_kwargs
+    ):
+        prompt_text = self.prompt.format(instruction=prompt_text)
+        return super().preprocess(
+            prompt_text,
+            prefix=prefix,
+            handle_long_generation=handle_long_generation,
+            **generate_kwargs,
+        )
+
+    def postprocess(
+        self,
+        model_outputs,
+        return_type=ReturnType.FULL_TEXT,
+        clean_up_tokenization_spaces=True,
+    ):
+        records = super().postprocess(
+            model_outputs,
+            return_type=return_type,
+            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+        )
+        for rec in records:
+            rec["generated_text"] = (
+                rec["generated_text"]
+                .split("<|assistant|>")[1]
+                .strip()
+                .split("<|user|>")[0]
+                .strip()
+            )
+        return records