Add inference providers support for Hugging Face (#8258) (#9738) (#9773)

* Add inference providers support for Hugging Face (#8258)

* add first version of inference providers for huggingface

* temporarily skipping tests

* Add documentation

* Fix titles

* remove max_retries from params and clean up

* add suggestions

* use llm http handler

* update doc

* add suggestions

* run formatters

* add tests

* revert

* revert

* rename file

* set maxsize for lru cache

* fix embeddings

* fix inference url

* fix tests following breaking change in main

* use ChatCompletionRequest

* fix tests and lint

* [Hugging Face] Remove outdated chat completion tests and fix embedding tests (#9749)

* remove or fix tests

* fix link in doc

* fix(config_settings.md): document hf api key

---------

Co-authored-by: célina <hanouticelina@gmail.com>

litellm/llms/huggingface/common_utils.py

@@ -1,18 +1,30 @@
+import os
+from functools import lru_cache
 from typing import Literal, Optional, Union
 
 import httpx
 
 from litellm.llms.base_llm.chat.transformation import BaseLLMException
 
+HF_HUB_URL = "https://huggingface.co"
 
-class HuggingfaceError(BaseLLMException):
+
+class HuggingFaceError(BaseLLMException):
     def __init__(
         self,
-        status_code: int,
-        message: str,
-        headers: Optional[Union[dict, httpx.Headers]] = None,
+        status_code,
+        message,
+        request: Optional[httpx.Request] = None,
+        response: Optional[httpx.Response] = None,
+        headers: Optional[Union[httpx.Headers, dict]] = None,
     ):
-        super().__init__(status_code=status_code, message=message, headers=headers)
+        super().__init__(
+            status_code=status_code,
+            message=message,
+            request=request,
+            response=response,
+            headers=headers,
+        )
 
 
 hf_tasks = Literal[
@@ -43,3 +55,48 @@ def output_parser(generated_text: str):
         if generated_text.endswith(token):
             generated_text = generated_text[::-1].replace(token[::-1], "", 1)[::-1]
     return generated_text
+
+
+@lru_cache(maxsize=128)
+def _fetch_inference_provider_mapping(model: str) -> dict:
+    """
+    Fetch provider mappings for a model from the Hugging Face Hub.
+
+    Args:
+        model: The model identifier (e.g., 'meta-llama/Llama-2-7b')
+
+    Returns:
+        dict: The inference provider mapping for the model
+
+    Raises:
+        ValueError: If no provider mapping is found
+        HuggingFaceError: If the API request fails
+    """
+    headers = {"Accept": "application/json"}
+    if os.getenv("HUGGINGFACE_API_KEY"):
+        headers["Authorization"] = f"Bearer {os.getenv('HUGGINGFACE_API_KEY')}"
+
+    path = f"{HF_HUB_URL}/api/models/{model}"
+    params = {"expand": ["inferenceProviderMapping"]}
+
+    try:
+        response = httpx.get(path, headers=headers, params=params)
+        response.raise_for_status()
+        provider_mapping = response.json().get("inferenceProviderMapping")
+
+        if provider_mapping is None:
+            raise ValueError(f"No provider mapping found for model {model}")
+
+        return provider_mapping
+    except httpx.HTTPError as e:
+        if hasattr(e, "response"):
+            status_code = getattr(e.response, "status_code", 500)
+            headers = getattr(e.response, "headers", {})
+        else:
+            status_code = 500
+            headers = {}
+        raise HuggingFaceError(
+            message=f"Failed to fetch provider mapping: {str(e)}",
+            status_code=status_code,
+            headers=headers,
+        )