feat(cerebras): Add model listing support in CerebrasProvider

src/any_llm/providers/cerebras/cerebras.py

@@ -1,4 +1,4 @@
-from collections.abc import AsyncIterator, Iterator
+from collections.abc import AsyncIterator, Iterator, Sequence
 from typing import Any, cast
 
 from pydantic import BaseModel
@@ -15,10 +15,12 @@
 from any_llm.exceptions import UnsupportedParameterError
 from any_llm.provider import ApiConfig, Provider
 from any_llm.providers.cerebras.utils import (
+    _convert_models_list,
     _convert_response,
     _create_openai_chunk_from_cerebras_chunk,
 )
 from any_llm.types.completion import ChatCompletion, ChatCompletionChunk, CompletionParams
+from any_llm.types.model import Model
 from any_llm.utils.instructor import _convert_instructor_response
 
 
@@ -34,7 +36,7 @@ class CerebrasProvider(Provider):
     SUPPORTS_RESPONSES = False
     SUPPORTS_COMPLETION_REASONING = False
     SUPPORTS_EMBEDDING = False
-    SUPPORTS_LIST_MODELS = False
+    SUPPORTS_LIST_MODELS = True
 
     PACKAGES_INSTALLED = PACKAGES_INSTALLED
 
@@ -194,3 +196,10 @@ def completion(
             raise ValueError(msg)
 
         return _convert_response(response_data)
+
+    def list_models(self, **kwargs: Any) -> Sequence[Model]:
+        """
+        Fetch available models from the /v1/models endpoint.
+        """
+        models_list = self.client.models.list(**kwargs)
+        return _convert_models_list(models_list)

src/any_llm/providers/cerebras/utils.py

@@ -1,6 +1,7 @@
 from typing import Any
 
 try:
+    from cerebras.cloud.sdk.types import ModelListResponse as CerebrasModelListResponse
     from cerebras.cloud.sdk.types.chat.chat_completion import ChatChunkResponse
 except ImportError as exc:
     msg = "cerebras is not installed. Please install it with `pip install any-llm-sdk[cerebras]`"
@@ -16,6 +17,7 @@
     CompletionUsage,
     Function,
 )
+from any_llm.types.model import Model
 
 
 def _create_openai_chunk_from_cerebras_chunk(chunk: ChatChunkResponse) -> ChatCompletionChunk:
@@ -154,3 +156,10 @@ def _convert_response(response_data: dict[str, Any]) -> ChatCompletion:
         choices=choices_out,
         usage=usage,
     )
+
+
+def _convert_models_list(models_list: CerebrasModelListResponse) -> list[Model]:
+    return [
+        Model(id=model.id, object="model", created=model.created or 0, owned_by="cerebras")
+        for model in models_list.data
+    ]