Sync contents

.sync_state

@@ -1,4 +1,4 @@
 {
-  "last_synced_sha": "52e233ae8c999937881c32b6b15606de6b391789",
+  "last_synced_sha": "4431cf66bde9b717e16d5c9af17c5083e183ac09",
-  "last_sync_time": "2025-12-02T02:40:27.052745"
+  "last_sync_time": "2025-12-02T04:15:53.354450"
 }

docs/api/serviceclient.md

@@ -147,6 +147,46 @@ async def create_training_client_from_state_async(
 
 Async version of create_training_client_from_state.
 
+#### `create_training_client_from_state_with_optimizer`
+
+```python
+def create_training_client_from_state_with_optimizer(
+        path: str,
+        user_metadata: dict[str, str] | None = None) -> TrainingClient
+```
+
+Create a TrainingClient from saved model weights and optimizer state.
+
+This is similar to create_training_client_from_state but also restores
+optimizer state (e.g., Adam momentum), which is useful for resuming
+training exactly where it left off.
+
+Args:
+- `path`: Tinker path to saved weights (e.g., "tinker://run-id/weights/checkpoint-001")
+- `user_metadata`: Optional metadata to attach to the new training run
+
+Returns:
+- `TrainingClient` loaded with the specified weights and optimizer state
+
+Example:
+```python
+# Resume training from a checkpoint with optimizer state
+training_client = service_client.create_training_client_from_state_with_optimizer(
+    "tinker://run-id/weights/checkpoint-001"
+)
+# Continue training with restored optimizer momentum
+```
+
+#### `create_training_client_from_state_with_optimizer_async`
+
+```python
+async def create_training_client_from_state_with_optimizer_async(
+        path: str,
+        user_metadata: dict[str, str] | None = None) -> TrainingClient
+```
+
+Async version of create_training_client_from_state_with_optimizer.
+
 #### `create_sampling_client`
 
 ```python

docs/api/types.md

@@ -237,21 +237,16 @@ class ImageAssetPointerChunk(StrictBase)
 
 Image format
 
-#### `height`
-
-Image height in pixels
-
 #### `location`
 
 Path or URL to the image asset
 
-#### `tokens`
+#### `expected_tokens`
 
-Number of tokens this image represents
+Expected number of tokens this image represents.
-
+This is only advisory: the tinker backend will compute the number of tokens
-#### `width`
+from the image, and we can fail requests quickly if the tokens does not
-
+match expected_tokens.
-Image width in pixels
 
 ## `CheckpointsListResponse` Objects
 
@@ -605,18 +600,6 @@ Image data as bytes
 
 Image format
 
-#### `height`
-
-Image height in pixels
-
-#### `tokens`
-
-Number of tokens this image represents
-
-#### `width`
-
-Image width in pixels
-
 #### `expected_tokens`
 
 Expected number of tokens this image represents.

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "tinker"
-version = "0.6.0"
+version = "0.6.1"
 description = "The official Python SDK for the tinker API"
 readme = "README.md"
 license = "Apache-2.0"

src/tinker/lib/public_interfaces/service_client.py

@@ -275,6 +275,68 @@ class ServiceClient(TelemetryProvider):
         await load_future.result_async()
         return training_client
 
+    @sync_only
+    @capture_exceptions(fatal=True)
+    def create_training_client_from_state_with_optimizer(
+        self, path: str, user_metadata: dict[str, str] | None = None
+    ) -> TrainingClient:
+        """Create a TrainingClient from saved model weights and optimizer state.
+
+        This is similar to create_training_client_from_state but also restores
+        optimizer state (e.g., Adam momentum), which is useful for resuming
+        training exactly where it left off.
+
+        Args:
+        - `path`: Tinker path to saved weights (e.g., "tinker://run-id/weights/checkpoint-001")
+        - `user_metadata`: Optional metadata to attach to the new training run
+
+        Returns:
+        - `TrainingClient` loaded with the specified weights and optimizer state
+
+        Example:
+        ```python
+        # Resume training from a checkpoint with optimizer state
+        training_client = service_client.create_training_client_from_state_with_optimizer(
+            "tinker://run-id/weights/checkpoint-001"
+        )
+        # Continue training with restored optimizer momentum
+        ```
+        """
+        rest_client = self.create_rest_client()
+        # Use weights info endpoint which allows access to models with public checkpoints
+        weights_info = rest_client.get_weights_info_by_tinker_path(path).result()
+
+        training_client = self.create_lora_training_client(
+            base_model=weights_info.base_model,
+            rank=weights_info.lora_rank,
+            user_metadata=user_metadata,
+        )
+
+        training_client.load_state_with_optimizer(path).result()
+        return training_client
+
+    @capture_exceptions(fatal=True)
+    async def create_training_client_from_state_with_optimizer_async(
+        self, path: str, user_metadata: dict[str, str] | None = None
+    ) -> TrainingClient:
+        """Async version of create_training_client_from_state_with_optimizer."""
+        rest_client = self.create_rest_client()
+        # Use weights info endpoint which allows access to models with public checkpoints
+        weights_info = await rest_client.get_weights_info_by_tinker_path(path)
+
+        # Right now all training runs are LoRa runs.
+        assert weights_info.is_lora and weights_info.lora_rank is not None
+
+        training_client = await self.create_lora_training_client_async(
+            base_model=weights_info.base_model,
+            rank=weights_info.lora_rank,
+            user_metadata=user_metadata,
+        )
+
+        load_future = await training_client.load_state_with_optimizer_async(path)
+        await load_future.result_async()
+        return training_client
+
     @capture_exceptions(fatal=True)
     def create_sampling_client(
         self,