partners/huggingface[patch]: fix HuggingFacePipeline model_id parameter (#27514)

**Description:** Fixes issue with model parameter not getting
initialized correctly when passing transformers pipeline
**Issue:** https://github.com/langchain-ai/langchain/issues/25915

libs/partners/huggingface/langchain_huggingface/llms/huggingface_pipeline.py

@@ -2,12 +2,12 @@ from __future__ import annotations  # type: ignore[import-not-found]
 
 import importlib.util
 import logging
-from typing import Any, Iterator, List, Mapping, Optional
+from typing import Any, Dict, Iterator, List, Mapping, Optional
 
 from langchain_core.callbacks import CallbackManagerForLLMRun
 from langchain_core.language_models.llms import BaseLLM
 from langchain_core.outputs import Generation, GenerationChunk, LLMResult
-from pydantic import ConfigDict
+from pydantic import ConfigDict, model_validator
 
 DEFAULT_MODEL_ID = "gpt2"
 DEFAULT_TASK = "text-generation"
@@ -55,8 +55,10 @@ class HuggingFacePipeline(BaseLLM):
     """
 
     pipeline: Any = None  #: :meta private:
-    model_id: str = DEFAULT_MODEL_ID
-    """Model name to use."""
+    model_id: Optional[str] = None
+    """The model name. If not set explicitly by the user,
+    it will be inferred from the provided pipeline (if available).
+    If neither is provided, the DEFAULT_MODEL_ID will be used."""
     model_kwargs: Optional[dict] = None
     """Keyword arguments passed to the model."""
     pipeline_kwargs: Optional[dict] = None
@@ -68,6 +70,17 @@ class HuggingFacePipeline(BaseLLM):
         extra="forbid",
     )
 
+    @model_validator(mode="before")
+    @classmethod
+    def pre_init_validator(cls, values: Dict[str, Any]) -> Dict[str, Any]:
+        """Ensure model_id is set either by pipeline or user input."""
+        if "model_id" not in values:
+            if "pipeline" in values and values["pipeline"]:
+                values["model_id"] = values["pipeline"].model.name_or_path
+            else:
+                values["model_id"] = DEFAULT_MODEL_ID
+        return values
+
     @classmethod
     def from_model_id(
         cls,

libs/partners/huggingface/tests/unit_tests/test_huggingface_pipeline.py

@@ -0,0 +1,50 @@
+from unittest.mock import MagicMock, patch
+
+from langchain_huggingface import HuggingFacePipeline
+
+DEFAULT_MODEL_ID = "gpt2"
+
+
+def test_initialization_default() -> None:
+    """Test default initialization"""
+
+    llm = HuggingFacePipeline()
+
+    assert llm.model_id == DEFAULT_MODEL_ID
+
+
+@patch("transformers.pipeline")
+def test_initialization_with_pipeline(mock_pipeline: MagicMock) -> None:
+    """Test initialization with a pipeline object"""
+
+    mock_pipe = MagicMock()
+    mock_pipe.model.name_or_path = "mock-model-id"
+    mock_pipeline.return_value = mock_pipe
+
+    llm = HuggingFacePipeline(pipeline=mock_pipe)
+
+    assert llm.model_id == "mock-model-id"
+
+
+@patch("transformers.AutoTokenizer.from_pretrained")
+@patch("transformers.AutoModelForCausalLM.from_pretrained")
+@patch("transformers.pipeline")
+def test_initialization_with_from_model_id(
+    mock_pipeline: MagicMock, mock_model: MagicMock, mock_tokenizer: MagicMock
+) -> None:
+    """Test initialization with the from_model_id method"""
+
+    mock_tokenizer.return_value = MagicMock(pad_token_id=0)
+    mock_model.return_value = MagicMock()
+
+    mock_pipe = MagicMock()
+    mock_pipe.task = "text-generation"
+    mock_pipe.model = mock_model.return_value
+    mock_pipeline.return_value = mock_pipe
+
+    llm = HuggingFacePipeline.from_model_id(
+        model_id="mock-model-id",
+        task="text-generation",
+    )
+
+    assert llm.model_id == "mock-model-id"