diff --git a/docs/docs/integrations/chat/huggingface.mdx b/docs/docs/integrations/chat/huggingface.mdx
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+---
+title: Hugging Face (chat)
+sidebar_label: Hugging Face
+---
+
+## Overview
+
+This page shows how to use Hugging Face models as chat models in LangChain.
+
+## Setup
+
+Install the required packages:
+
+```bash
+pip install langchain-huggingface transformers
+```
+
+> For Hugging Face pipelines, prefer `max_new_tokens` (not `max_tokens`). The pipeline will use CPU/GPU automatically depending on availability.
+
+## Instantiation
+
+### Option 1 (works today): pipeline → wrap with `ChatHuggingFace`
+
+```python
+from transformers import pipeline
+from langchain_huggingface import ChatHuggingFace
+
+# Create a text-generation pipeline (CPU/GPU as available)
+pipe = pipeline(
+    "text-generation",
+    model="microsoft/Phi-3-mini-4k-instruct",
+    do_sample=False,        # deterministic
+    max_new_tokens=128,     # HF uses max_new_tokens (not max_tokens)
+)
+
+# Wrap the pipeline as a LangChain chat model
+llm = ChatHuggingFace(llm=pipe)
+```
+
+### Option 2 (coming after fix): `init_chat_model(..., model_provider="huggingface")`
+
+Once available in your version, you can initialize via `init_chat_model`:
+
+```python
+from langchain.chat_models import init_chat_model
+
+llm = init_chat_model(
+    model="microsoft/Phi-3-mini-4k-instruct",
+    model_provider="huggingface",
+    task="text-generation",
+    do_sample=False,
+    max_new_tokens=128,
+)
+```
+
+> If your version doesn’t support this yet, use **Option 1** above.
+
+## Invocation
+
+```python
+msg = llm.invoke("Say hi in one sentence.")
+print(msg.content)
+```
+
+## Chaining
+
+```python
+from langchain_core.prompts import ChatPromptTemplate
+
+prompt = ChatPromptTemplate.from_messages([
+    ("system", "You are helpful."),
+    ("human", "{question}"),
+])
+
+chain = prompt | llm
+result = chain.invoke({"question": "What is the capital of France?"})
+print(result.content)
+```
+
+## API reference
+
+- `langchain_huggingface.ChatHuggingFace`
+- `transformers.pipeline` (Hugging Face)
+- `langchain.chat_models.init_chat_model` (when available for Hugging Face)
+
+
diff --git a/libs/langchain/tests/unit_tests/chat_models/test_init_chat_model_hf.py b/libs/langchain/tests/unit_tests/chat_models/test_init_chat_model_hf.py
new file mode 100644
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+++ b/libs/langchain/tests/unit_tests/chat_models/test_init_chat_model_hf.py
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+import sys
+import types
+from importlib import util as import_util
+from types import SimpleNamespace
+from typing import Any, Optional
+
+import pytest
+
+from langchain.chat_models import init_chat_model
+
+
+@pytest.fixture
+def hf_fakes(monkeypatch: pytest.MonkeyPatch) -> SimpleNamespace:
+    """Install fakes for Hugging Face and transformers.
+
+    Capture call arguments and simulate module presence to test initialization
+    behavior, including current failure modes.
+    """
+    pipeline_calls: list[tuple[str, dict[str, Any]]] = []
+    init_calls: list[dict[str, Any]] = []
+
+    # Fake transformers.pipeline
+    def fake_pipeline(task: str, **kwargs: Any) -> SimpleNamespace:
+        pipeline_calls.append((task, dict(kwargs)))
+        return SimpleNamespace(_kind="dummy_hf_pipeline")
+
+    transformers_mod = types.ModuleType("transformers")
+    setattr(transformers_mod, "pipeline", fake_pipeline)  # noqa: B010
+    monkeypatch.setitem(sys.modules, "transformers", transformers_mod)
+
+    # Fake langchain_huggingface.ChatHuggingFace that REQUIRES `llm`
+    class FakeChatHuggingFace:
+        def __init__(self, *, llm: object, **kwargs: Any) -> None:
+            init_calls.append({"llm": llm, "kwargs": dict(kwargs)})
+            self._llm = llm
+            self._kwargs = kwargs
+
+    # Build full package path:
+    # langchain_huggingface.chat_models.huggingface
+    hf_pkg = types.ModuleType("langchain_huggingface")
+    hf_pkg.__path__ = []  # mark as package
+
+    hf_chat_models_pkg = types.ModuleType("langchain_huggingface.chat_models")
+    hf_chat_models_pkg.__path__ = []  # mark as package
+
+    hf_chat_hf_mod = types.ModuleType(
+        "langchain_huggingface.chat_models.huggingface",
+    )
+    setattr(hf_chat_hf_mod, "ChatHuggingFace", FakeChatHuggingFace)  # noqa: B010
+
+    # Also expose at package root for top-level imports
+    setattr(hf_pkg, "ChatHuggingFace", FakeChatHuggingFace)  # noqa: B010
+
+    monkeypatch.setitem(sys.modules, "langchain_huggingface", hf_pkg)
+    monkeypatch.setitem(
+        sys.modules,
+        "langchain_huggingface.chat_models",
+        hf_chat_models_pkg,
+    )
+    monkeypatch.setitem(
+        sys.modules,
+        "langchain_huggingface.chat_models.huggingface",
+        hf_chat_hf_mod,
+    )
+
+    # Ensure _check_pkg sees both packages as installed
+    orig_find_spec = import_util.find_spec
+
+    def fake_find_spec(name: str) -> Optional[object]:
+        if name in {
+            "transformers",
+            "langchain_huggingface",
+            "langchain_huggingface.chat_models",
+            "langchain_huggingface.chat_models.huggingface",
+        }:
+            return object()
+        return orig_find_spec(name)
+
+    monkeypatch.setattr("importlib.util.find_spec", fake_find_spec)
+
+    return SimpleNamespace(
+        pipeline_calls=pipeline_calls,
+        init_calls=init_calls,
+    )
+
+
+def test_hf_current_bug_basic_raises_typeerror(
+    hf_fakes: SimpleNamespace,
+) -> None:
+    """Current behavior raises TypeError when using Hugging Face provider.
+
+    init_chat_model constructs ChatHuggingFace without ``llm`` and never builds
+    a pipeline. Verify that explicitly.
+    """
+    with pytest.raises(TypeError):
+        _ = init_chat_model(
+            "huggingface:microsoft/Phi-3-mini-4k-instruct",
+            task="text-generation",
+            temperature=0,
+        )
+    # Buggy path should not touch transformers.pipeline
+    assert not hf_fakes.pipeline_calls, "pipeline should NOT be called"
+
+
+def test_hf_current_bug_max_tokens_case_raises_typeerror(
+    hf_fakes: SimpleNamespace,
+) -> None:
+    """Same failure when passing ``max_tokens``.
+
+    Should raise and avoid constructing a pipeline.
+    """
+    with pytest.raises(TypeError):
+        _ = init_chat_model(
+            model="mistralai/Mistral-7B-Instruct-v0.2",
+            model_provider="huggingface",
+            task="text-generation",
+            max_tokens=42,
+        )
+    assert not hf_fakes.pipeline_calls, "pipeline should NOT be called"
+
+
+def test_hf_current_bug_timeout_retries_case_raises_typeerror(
+    hf_fakes: SimpleNamespace,
+) -> None:
+    """Same failure when passing ``timeout``/``max_retries``.
+
+    Should raise and avoid constructing a pipeline.
+    """
+    with pytest.raises(TypeError):
+        _ = init_chat_model(
+            model="microsoft/Phi-3-mini-4k-instruct",
+            model_provider="huggingface",
+            task="text-generation",
+            temperature=0.1,
+            timeout=7,
+            max_retries=3,
+        )
+    assert not hf_fakes.pipeline_calls, "pipeline should NOT be called"