feat: add scaleway inference provider (#3356)

* feat: add scaleway inference provider

* fix: alpha order

Author: Gnoale

src/huggingface_hub/inference/_client.py

@@ -133,7 +133,7 @@ class InferenceClient:
             Note: for better compatibility with OpenAI's client, `model` has been aliased as `base_url`. Those 2
             arguments are mutually exclusive. If a URL is passed as `model` or `base_url` for chat completion, the `(/v1)/chat/completions` suffix path will be appended to the URL.
         provider (`str`, *optional*):
-            Name of the provider to use for inference. Can be `"black-forest-labs"`, `"cerebras"`, `"cohere"`, `"fal-ai"`, `"featherless-ai"`, `"fireworks-ai"`, `"groq"`, `"hf-inference"`, `"hyperbolic"`, `"nebius"`, `"novita"`, `"nscale"`, `"openai"`, `"replicate"`, "sambanova"` or `"together"`.
+            Name of the provider to use for inference. Can be `"black-forest-labs"`, `"cerebras"`, `"cohere"`, `"fal-ai"`, `"featherless-ai"`, `"fireworks-ai"`, `"groq"`, `"hf-inference"`, `"hyperbolic"`, `"nebius"`, `"novita"`, `"nscale"`, `"openai"`, `"replicate"`, `"sambanova"`, `"scaleway"` or `"together"`.
             Defaults to "auto" i.e. the first of the providers available for the model, sorted by the user's order in https://hf.co/settings/inference-providers.
             If model is a URL or `base_url` is passed, then `provider` is not used.
         token (`str`, *optional*):

src/huggingface_hub/inference/_generated/_async_client.py

@@ -121,7 +121,7 @@ class AsyncInferenceClient:
             Note: for better compatibility with OpenAI's client, `model` has been aliased as `base_url`. Those 2
             arguments are mutually exclusive. If a URL is passed as `model` or `base_url` for chat completion, the `(/v1)/chat/completions` suffix path will be appended to the URL.
         provider (`str`, *optional*):
-            Name of the provider to use for inference. Can be `"black-forest-labs"`, `"cerebras"`, `"cohere"`, `"fal-ai"`, `"featherless-ai"`, `"fireworks-ai"`, `"groq"`, `"hf-inference"`, `"hyperbolic"`, `"nebius"`, `"novita"`, `"nscale"`, `"openai"`, `"replicate"`, "sambanova"` or `"together"`.
+            Name of the provider to use for inference. Can be `"black-forest-labs"`, `"cerebras"`, `"cohere"`, `"fal-ai"`, `"featherless-ai"`, `"fireworks-ai"`, `"groq"`, `"hf-inference"`, `"hyperbolic"`, `"nebius"`, `"novita"`, `"nscale"`, `"openai"`, `"replicate"`, `"sambanova"`, `"scaleway"` or `"together"`.
             Defaults to "auto" i.e. the first of the providers available for the model, sorted by the user's order in https://hf.co/settings/inference-providers.
             If model is a URL or `base_url` is passed, then `provider` is not used.
         token (`str`, *optional*):

src/huggingface_hub/inference/_providers/__init__.py

@@ -38,6 +38,7 @@
 from .openai import OpenAIConversationalTask
 from .replicate import ReplicateImageToImageTask, ReplicateTask, ReplicateTextToImageTask, ReplicateTextToSpeechTask
 from .sambanova import SambanovaConversationalTask, SambanovaFeatureExtractionTask
+from .scaleway import ScalewayConversationalTask, ScalewayFeatureExtractionTask
 from .together import TogetherConversationalTask, TogetherTextGenerationTask, TogetherTextToImageTask
 
 
@@ -60,6 +61,7 @@
     "openai",
     "replicate",
     "sambanova",
+    "scaleway",
     "together",
 ]
 
@@ -154,6 +156,10 @@
         "conversational": SambanovaConversationalTask(),
         "feature-extraction": SambanovaFeatureExtractionTask(),
     },
+    "scaleway": {
+        "conversational": ScalewayConversationalTask(),
+        "feature-extraction": ScalewayFeatureExtractionTask(),
+    },
     "together": {
         "text-to-image": TogetherTextToImageTask(),
         "conversational": TogetherConversationalTask(),

src/huggingface_hub/inference/_providers/_common.py

@@ -33,6 +33,7 @@
     "nscale": {},
     "replicate": {},
     "sambanova": {},
+    "scaleway": {},
     "together": {},
 }
 

src/huggingface_hub/inference/_providers/scaleway.py

@@ -0,0 +1,28 @@
+from typing import Any, Dict, Optional, Union
+
+from huggingface_hub.inference._common import RequestParameters, _as_dict
+
+from ._common import BaseConversationalTask, InferenceProviderMapping, TaskProviderHelper, filter_none
+
+
+class ScalewayConversationalTask(BaseConversationalTask):
+    def __init__(self):
+        super().__init__(provider="scaleway", base_url="https://api.scaleway.ai")
+
+
+class ScalewayFeatureExtractionTask(TaskProviderHelper):
+    def __init__(self):
+        super().__init__(provider="scaleway", base_url="https://api.scaleway.ai", task="feature-extraction")
+
+    def _prepare_route(self, mapped_model: str, api_key: str) -> str:
+        return "/v1/embeddings"
+
+    def _prepare_payload_as_dict(
+        self, inputs: Any, parameters: Dict, provider_mapping_info: InferenceProviderMapping
+    ) -> Optional[Dict]:
+        parameters = filter_none(parameters)
+        return {"input": inputs, "model": provider_mapping_info.provider_id, **parameters}
+
+    def get_response(self, response: Union[bytes, Dict], request_params: Optional[RequestParameters] = None) -> Any:
+        embeddings = _as_dict(response)["data"]
+        return [embedding["embedding"] for embedding in embeddings]

tests/test_inference_providers.py

@@ -50,6 +50,7 @@
     ReplicateTextToSpeechTask,
 )
 from huggingface_hub.inference._providers.sambanova import SambanovaConversationalTask, SambanovaFeatureExtractionTask
+from huggingface_hub.inference._providers.scaleway import ScalewayConversationalTask, ScalewayFeatureExtractionTask
 from huggingface_hub.inference._providers.together import TogetherTextToImageTask
 
 from .testing_utils import assert_in_logs
@@ -1052,6 +1053,75 @@ def test_prepare_url_conversational(self):
         assert url == "https://api.novita.ai/v3/openai/chat/completions"
 
 
+class TestScalewayProvider:
+    def test_prepare_hf_url_conversational(self):
+        helper = ScalewayConversationalTask()
+        url = helper._prepare_url("hf_token", "username/repo_name")
+        assert url == "https://router.huggingface.co/scaleway/v1/chat/completions"
+
+    def test_prepare_url_conversational(self):
+        helper = ScalewayConversationalTask()
+        url = helper._prepare_url("scw_token", "username/repo_name")
+        assert url == "https://api.scaleway.ai/v1/chat/completions"
+
+    def test_prepare_payload_as_dict(self):
+        helper = ScalewayConversationalTask()
+        payload = helper._prepare_payload_as_dict(
+            [
+                {"role": "system", "content": "You are a helpful assistant"},
+                {"role": "user", "content": "Hello!"},
+            ],
+            {
+                "max_tokens": 512,
+                "temperature": 0.15,
+                "top_p": 1,
+                "presence_penalty": 0,
+                "stream": True,
+            },
+            InferenceProviderMapping(
+                provider="scaleway",
+                hf_model_id="meta-llama/Llama-3.1-8B-Instruct",
+                providerId="meta-llama/llama-3.1-8B-Instruct",
+                task="conversational",
+                status="live",
+            ),
+        )
+        assert payload == {
+            "max_tokens": 512,
+            "messages": [
+                {"content": "You are a helpful assistant", "role": "system"},
+                {"role": "user", "content": "Hello!"},
+            ],
+            "model": "meta-llama/llama-3.1-8B-Instruct",
+            "presence_penalty": 0,
+            "stream": True,
+            "temperature": 0.15,
+            "top_p": 1,
+        }
+
+    def test_prepare_url_feature_extraction(self):
+        helper = ScalewayFeatureExtractionTask()
+        assert (
+            helper._prepare_url("hf_token", "username/repo_name")
+            == "https://router.huggingface.co/scaleway/v1/embeddings"
+        )
+
+    def test_prepare_payload_as_dict_feature_extraction(self):
+        helper = ScalewayFeatureExtractionTask()
+        payload = helper._prepare_payload_as_dict(
+            "Example text to embed",
+            {"truncate": True},
+            InferenceProviderMapping(
+                provider="scaleway",
+                hf_model_id="username/repo_name",
+                providerId="provider-id",
+                task="feature-extraction",
+                status="live",
+            ),
+        )
+        assert payload == {"input": "Example text to embed", "model": "provider-id", "truncate": True}
+
+
 class TestNscaleProvider:
     def test_prepare_route_text_to_image(self):
         helper = NscaleTextToImageTask()