Further simplify things

Author: vblagoje

haystack/nodes/prompt/prompt_node.py

@@ -9,7 +9,8 @@
 
 from haystack import MultiLabel
 from haystack.nodes.base import BaseComponent
-from haystack.nodes.prompt.providers import PromptModelInvocationLayer
+from haystack.nodes.prompt.providers import PromptModelInvocationLayer, instruction_following_models
+
 from haystack.schema import Document
 from haystack.telemetry_2 import send_event
 
@@ -215,6 +216,14 @@ def __init__(
 
         self.model_kwargs = model_kwargs if model_kwargs else {}
         self.model_invocation_layer = self.create_invocation_layer(invocation_layer_class=invocation_layer_class)
+        is_instruction_following: bool = any(m in model_name_or_path for m in instruction_following_models())
+        if not is_instruction_following:
+            logger.warning(
+                "PromptNode has been potentially initialized with a language model not fine-tuned on instruction following tasks. "
+                "Many of the default prompts and PromptTemplates will likely not work as intended. "
+                "Use custom prompts and PromptTemplates specific to the %s model",
+                model_name_or_path,
+            )
 
     def create_invocation_layer(
         self, invocation_layer_class: Optional[Type[PromptModelInvocationLayer]]

haystack/nodes/prompt/providers.py

@@ -116,6 +116,10 @@ def _ensure_token_limit(self, prompt: Union[str, List[Dict[str, str]]]) -> Union
         pass
 
 
+def instruction_following_models() -> List[str]:
+    return ["flan", "mt0", "bloomz", "davinci"]
+
+
 class StopWordsCriteria(StoppingCriteria):
     """
     Stops text generation if any one of the stop words is generated.
@@ -300,16 +304,9 @@ def supports(cls, model_name_or_path: str, **kwargs) -> bool:
         try:
             task_name = get_task(model_name_or_path)
         except RuntimeError:
-            # This is needed so OpenAI models are skipped over
+            # This will fail for all non-HF models
             return False
 
-        if not any(m in model_name_or_path for m in ["flan", "mt0", "bloomz"]):
-            logger.warning(
-                "PromptNode has been potentially initialized with a language model not fine-tuned on instruction following tasks. "
-                "Many of the default prompts and PromptTemplates will likely not work as intended. "
-                "Use custom prompts and PromptTemplates specific to the %s model",
-                model_name_or_path,
-            )
         return task_name in ["text2text-generation", "text-generation"]