chore: update tinker dependency version to 0.8.1 in pyproject.toml and uv.lock

- Upgraded the tinker dependency from version 0.7.0 to 0.8.1 in both pyproject.toml and uv.lock to ensure compatibility with the latest features and fixes.

Author: bradhilton

pyproject.toml

@@ -9,7 +9,7 @@ dependencies = [
     "typer>=0.15.2",
     "litellm>=1.71.1",
     "weave>=0.52.23",
-    "tinker>=0.7.0",
+    "tinker>=0.8.1",
     "tinker-cookbook>=0.1.0",
     "polars>=1.26.0",
     "tblib>=3.0.0",

src/art/local/backend.py

@@ -267,11 +267,12 @@ async def _prepare_backend_for_training(
         config: dev.OpenAIServerConfig | None = None,
     ) -> tuple[str, str]:
         service = await self._get_service(model)
-        await service.start_openai_server(config=config)
-        server_args = (config or {}).get("server_args", {})
+        host, port = await service.start_openai_server(config=config)
 
-        base_url = f"http://{server_args.get('host', '0.0.0.0')}:{server_args.get('port', 8000)}/v1"
-        api_key = server_args.get("api_key", None) or "default"
+        base_url = f"http://{host}:{port}/v1"
+        api_key = (config or {}).get("server_args", {}).get(
+            "api_key", None
+        ) or "default"
 
         def done_callback(_: asyncio.Task[None]) -> None:
             close_proxy(self._services.pop(model.name))

src/art/local/service.py

@@ -17,7 +17,7 @@ def __init__(
 
     async def start_openai_server(
         self, config: dev.OpenAIServerConfig | None
-    ) -> None: ...
+    ) -> tuple[str, int]: ...
 
     async def vllm_engine_is_sleeping(self) -> bool: ...
 

src/art/tinker/__init__.py

@@ -1,3 +1,5 @@
 from .backend import TinkerBackend
+from .renderers import get_renderer_name
+from .server import OpenAICompatibleTinkerServer
 
-__all__ = ["TinkerBackend"]
+__all__ = ["TinkerBackend", "get_renderer_name", "OpenAICompatibleTinkerServer"]

src/art/tinker/backend.py

@@ -1,13 +1,13 @@
 import os
-from typing import Any, cast
+from typing import cast
 
 from mp_actors import move_to_child_process
 
-from .. import dev
 from ..local.backend import LocalBackend
 from ..local.service import ModelService
 from ..model import TrainableModel
 from ..utils.output_dirs import get_model_dir
+from .renderers import get_renderer_name
 
 
 class TinkerBackend(LocalBackend):
@@ -26,38 +26,6 @@ def __init__(
             os.environ["TINKER_API_KEY"] = tinker_api_key
         super().__init__(in_process=in_process, path=path)
 
-    async def _prepare_backend_for_training(
-        self,
-        model: TrainableModel,
-        config: dev.OpenAIServerConfig | None = None,
-    ) -> tuple[str, str]:
-        """Start the local OpenAI server and return its base URL + API key."""
-        service = await self._get_service(model)
-        raw_config: dict[str, Any] = cast(dict[str, Any], config) if config else {}
-
-        server_args = cast(dict[str, Any], raw_config.get("server_args", {}))
-        host = server_args.get("host", raw_config.get("host", "0.0.0.0"))
-        port = server_args.get("port", raw_config.get("port"))
-        if port is None:
-            from .service import get_free_port
-
-            port = get_free_port()
-        api_key = server_args.get("api_key", raw_config.get("api_key")) or "default"
-
-        # Ensure the Tinker server binds to the same host/port we return.
-        tinker_config = cast(
-            dev.OpenAIServerConfig,
-            {
-                **raw_config,
-                "host": host,
-                "port": port,
-            },
-        )
-        await service.start_openai_server(config=tinker_config)
-
-        base_url = f"http://{host}:{port}/v1"
-        return base_url, api_key
-
     async def _get_service(self, model: TrainableModel) -> ModelService:
         from ..dev.get_model_config import get_model_config
         from ..dev.model import TinkerArgs, TinkerTrainingClientArgs
@@ -88,52 +56,3 @@ async def _get_service(self, model: TrainableModel) -> ModelService:
                     process_name="tinker-service",
                 )
         return self._services[model.name]
-
-
-renderer_name_message = """
-To manually specify a renderer (and silence this message), you can set the "renderer_name" field like so:
-
-model = art.TrainableModel(
-    name="my-model",
-    project="my-project",
-    base_model="Qwen/Qwen3-8B",
-    _internal_config=art.dev.InternalModelConfig(
-        tinker_args=art.dev.TinkerArgs(renderer_name="qwen3_disable_thinking"),
-    ),
-)
-
-Valid renderer names are:
-
-- llama3
-- qwen3
-- qwen3_disable_thinking
-- qwen3_instruct
-- deepseekv3
-- deepseekv3_disable_thinking
-- gpt_oss_no_sysprompt
-- gpt_oss_low_reasoning
-- gpt_oss_medium_reasoning
-- gpt_oss_high_reasoning
-""".strip()
-
-
-def get_renderer_name(base_model: str) -> str:
-    if base_model.startswith("meta-llama/"):
-        return "llama3"
-    elif base_model.startswith("Qwen/Qwen3-"):
-        if "Instruct" in base_model:
-            return "qwen3_instruct"
-        else:
-            print("Defaulting to Qwen3 renderer without thinking for", base_model)
-            print(renderer_name_message)
-            return "qwen3_disable_thinking"
-    elif base_model.startswith("deepseek-ai/DeepSeek-V3"):
-        print("Defaulting to DeepSeekV3 renderer without thinking for", base_model)
-        print(renderer_name_message)
-        return "deepseekv3_disable_thinking"
-    elif base_model.startswith("openai/gpt-oss"):
-        print("Defaulting to GPT-OSS renderer without system prompt for", base_model)
-        print(renderer_name_message)
-        return "gpt_oss_no_sysprompt"
-    else:
-        raise ValueError(f"Unknown base model: {base_model}")

src/art/tinker/renderers.py

@@ -0,0 +1,47 @@
+def get_renderer_name(base_model: str) -> str:
+    if base_model.startswith("meta-llama/"):
+        return "llama3"
+    elif base_model.startswith("Qwen/Qwen3-"):
+        if "Instruct" in base_model:
+            return "qwen3_instruct"
+        else:
+            print("Defaulting to Qwen3 renderer without thinking for", base_model)
+            print(renderer_name_message)
+            return "qwen3_disable_thinking"
+    elif base_model.startswith("deepseek-ai/DeepSeek-V3"):
+        print("Defaulting to DeepSeekV3 renderer without thinking for", base_model)
+        print(renderer_name_message)
+        return "deepseekv3_disable_thinking"
+    elif base_model.startswith("openai/gpt-oss"):
+        print("Defaulting to GPT-OSS renderer without system prompt for", base_model)
+        print(renderer_name_message)
+        return "gpt_oss_no_sysprompt"
+    else:
+        raise ValueError(f"Unknown base model: {base_model}")
+
+
+renderer_name_message = """
+To manually specify a renderer (and silence this message), you can set the "renderer_name" field like so:
+
+model = art.TrainableModel(
+    name="my-model",
+    project="my-project",
+    base_model="Qwen/Qwen3-8B",
+    _internal_config=art.dev.InternalModelConfig(
+        tinker_args=art.dev.TinkerArgs(renderer_name="qwen3_disable_thinking"),
+    ),
+)
+
+Valid renderer names are:
+
+- llama3
+- qwen3
+- qwen3_disable_thinking
+- qwen3_instruct
+- deepseekv3
+- deepseekv3_disable_thinking
+- gpt_oss_no_sysprompt
+- gpt_oss_low_reasoning
+- gpt_oss_medium_reasoning
+- gpt_oss_high_reasoning
+""".strip()