Merge branch 'main' into henrytu/cerebras-integration

llama_stack/providers/remote/datasetio/__init__.py

@@ -0,0 +1,5 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.

llama_stack/providers/remote/datasetio/huggingface/config.py

@@ -3,12 +3,13 @@
 #
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
+from pydantic import BaseModel
+
 from llama_stack.distribution.utils.config_dirs import RUNTIME_BASE_DIR
 from llama_stack.providers.utils.kvstore.config import (
     KVStoreConfig,
     SqliteKVStoreConfig,
 )
-from pydantic import BaseModel
 
 
 class HuggingfaceDatasetIOConfig(BaseModel):

llama_stack/providers/remote/datasetio/huggingface/huggingface.py

@@ -9,6 +9,7 @@ from llama_stack.apis.datasetio import *  # noqa: F403
 
 
 import datasets as hf_datasets
+
 from llama_stack.providers.datatypes import DatasetsProtocolPrivate
 from llama_stack.providers.utils.datasetio.url_utils import get_dataframe_from_url
 from llama_stack.providers.utils.kvstore import kvstore_impl

llama_stack/providers/remote/inference/nvidia/config.py

@@ -35,7 +35,9 @@ class NVIDIAConfig(BaseModel):
     """
 
     url: str = Field(
-        default="https://integrate.api.nvidia.com",
+        default_factory=lambda: os.getenv(
+            "NVIDIA_BASE_URL", "https://integrate.api.nvidia.com"
+        ),
         description="A base url for accessing the NVIDIA NIM",
     )
     api_key: Optional[str] = Field(

llama_stack/providers/remote/inference/tgi/tgi.py

@@ -89,8 +89,9 @@ class _HfAdapter(Inference, ModelsProtocolPrivate):
         stream: Optional[bool] = False,
         logprobs: Optional[LogProbConfig] = None,
     ) -> AsyncGenerator:
+        model = await self.model_store.get_model(model_id)
         request = CompletionRequest(
-            model=model_id,
+            model=model.provider_resource_id,
             content=content,
             sampling_params=sampling_params,
             response_format=response_format,
@@ -194,8 +195,9 @@ class _HfAdapter(Inference, ModelsProtocolPrivate):
         stream: Optional[bool] = False,
         logprobs: Optional[LogProbConfig] = None,
     ) -> AsyncGenerator:
+        model = await self.model_store.get_model(model_id)
         request = ChatCompletionRequest(
-            model=model_id,
+            model=model.provider_resource_id,
             messages=messages,
             sampling_params=sampling_params,
             tools=tools or [],
@@ -249,7 +251,7 @@ class _HfAdapter(Inference, ModelsProtocolPrivate):
 
     def _get_params(self, request: ChatCompletionRequest) -> dict:
         prompt, input_tokens = chat_completion_request_to_model_input_info(
-            request, self.formatter
+            request, self.register_helper.get_llama_model(request.model), self.formatter
         )
         return dict(
             prompt=prompt,