fix(langchain-classic): fix init_chat_model for HuggingFace models (#33943)

libs/partners/huggingface/langchain_huggingface/chat_models/huggingface.py

@@ -627,8 +627,54 @@ class ChatHuggingFace(BaseChatModel):
                 HuggingFacePipeline,
             )
 
+            task = task if task is not None else "text-generation"
+
+            # Separate pipeline-specific kwargs from ChatHuggingFace kwargs
+            # Parameters that should go to HuggingFacePipeline.from_model_id
+            pipeline_specific_kwargs = {}
+
+            # Extract pipeline-specific parameters
+            pipeline_keys = [
+                "backend",
+                "device",
+                "device_map",
+                "model_kwargs",
+                "pipeline_kwargs",
+                "batch_size",
+            ]
+            for key in pipeline_keys:
+                if key in kwargs:
+                    pipeline_specific_kwargs[key] = kwargs.pop(key)
+
+            # Remaining kwargs (temperature, max_tokens, etc.) should go to
+            # pipeline_kwargs for generation parameters, which ChatHuggingFace
+            # will inherit from the LLM
+            if "pipeline_kwargs" not in pipeline_specific_kwargs:
+                pipeline_specific_kwargs["pipeline_kwargs"] = {}
+
+            # Add generation parameters to pipeline_kwargs
+            # Map max_tokens to max_new_tokens for HuggingFace pipeline
+            generation_params = {}
+            for k, v in list(kwargs.items()):
+                if k == "max_tokens":
+                    generation_params["max_new_tokens"] = v
+                    kwargs.pop(k)
+                elif k in (
+                    "temperature",
+                    "max_new_tokens",
+                    "top_p",
+                    "top_k",
+                    "repetition_penalty",
+                    "do_sample",
+                ):
+                    generation_params[k] = v
+                    kwargs.pop(k)
+
+            pipeline_specific_kwargs["pipeline_kwargs"].update(generation_params)
+
+            # Create the HuggingFacePipeline
             llm = HuggingFacePipeline.from_model_id(
-                model_id=model_id, task=cast(str, task), **kwargs
+                model_id=model_id, task=task, **pipeline_specific_kwargs
             )
         elif backend == "endpoint":
             from langchain_huggingface.llms.huggingface_endpoint import (