Docs: Inspect AI → Storage Buckets eval-log integration

docs/hub/eval-results.md

@@ -70,7 +70,7 @@ Anyone can submit evaluation results to any model via Pull Request:
 3. Add a `.eval_results/*.yaml` file with your results.
 4. The PR will show as "community-provided" on the model page while open.
 
-For help evaluating a model, see the [Evaluating models with Inspect](https://huggingface.co/docs/inference-providers/guides/evaluation-inspect-ai) guide.
+For help evaluating a model, see the [Evaluating models with Inspect](https://huggingface.co/docs/inference-providers/guides/evaluation-inspect-ai) guide. Evaluation logs from Inspect can be written directly to [HF Storage Buckets](./storage-buckets-integrations#inspect-ai).
 
 > [!TIP]
 > Community scores are visible while the PR is open. If a score is disputed, the model author can close the PR to remove it. The goal is to surface existing evaluation data transparently while building toward a fully reproducible standard via verified scores.

docs/hub/storage-buckets-integrations.md

@@ -51,6 +51,21 @@ from datasets import load_dataset
 ds = load_dataset("buckets/username/my-bucket", data_files=["data.parquet"])
 ```
 
+## Inspect AI
+
+[Inspect AI](https://inspect.aisi.org.uk/) can write evaluation logs directly to a bucket — point its log directory at an `hf://buckets/` path (requires `huggingface_hub>=1.6.0`). Create the bucket and authenticate first (Inspect won't create it for you):
+
+```bash
+hf auth login
+hf buckets create username/my-bucket --private
+
+export INSPECT_LOG_DIR=hf://buckets/username/my-bucket/eval-logs
+inspect eval popularity.py --model openai/gpt-4
+inspect view
+```
+
+See [Inspect's eval logs guide](https://inspect.aisi.org.uk/eval-logs.html#sec-hugging-face-storage-buckets) for details.
+
 ## Filesystem operations
 
 For direct file operations, `huggingface_hub` exposes a pre-instantiated [filesystem object](/docs/huggingface_hub/guides/hf_file_system), `hffs`: