local persistent for hf dataset provider (#451)

# What does this PR do?

- local persistence for HF dataset provider
- follow https://github.com/meta-llama/llama-stack/pull/375

## Test Plan

**e2e**
1. fresh llama stack run w/ yaml
2. kill server
3. restart llama stack run w/ yaml

```yaml
datasets:
  - dataset_id: mmlu
    provider_id: huggingface-0
    url:
      uri: https://huggingface.co/datasets/llamastack/evals
    metadata:
      path: llamastack/evals
      name: evals__mmlu__details
      split: train
    dataset_schema:
      input_query:
        type: string
      expected_answer:
        type: string
```
<img width="686" alt="image"
src="https://github.com/user-attachments/assets/d7737931-6a7d-400a-a17d-fef6cbd97eea">


## Before submitting

- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [ ] Ran pre-commit to handle lint / formatting issues.
- [ ] Read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
      Pull Request section?
- [ ] Updated relevant documentation.
- [ ] Wrote necessary unit or integration tests.

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

@@ -3,7 +3,15 @@
 #
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
-from llama_stack.apis.datasetio import *  # noqa: F401, F403
+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): ...
+class HuggingfaceDatasetIOConfig(BaseModel):
+    kvstore: KVStoreConfig = SqliteKVStoreConfig(
+        db_path=(RUNTIME_BASE_DIR / "huggingface_datasetio.db").as_posix()
+    )  # Uses SQLite config specific to HF storage