Update links

README.md

@@ -51,6 +51,10 @@ These series vary widely in sampling frequency, temporal length, and number of v
 
 - High cardinality: Observability data is often segmented by tags such as service, region, or instance, producing large families of multivariate series with high dimensionality but limited history per variate.
 
+## Evaluating Models on BOOM
+
+We provide code with example evaluations of existing models; see the [code repository](https://github.com/DataDog/toto).
+
 ## Dataset Structure
 
 Each entry in the dataset consists of:
@@ -84,10 +88,10 @@ Principal Component Analysis (PCA) applied to the full feature set (Figure 1) re
 ![Metadata](histograms.png)
 *<center>Figure 3: Distributional comparison of 12 statistical features computed on normalized time series from the Boom dataset and the [GiftEval](https://huggingface.co/datasets/Salesforce/GiftEval) dataset. The broader and often shifted distributions in the Boom series reflect the increased diversity, irregularity, and non-stationarity characteristic of observability data.</center>*
 
-Links:
+## Links:
   - Paper (To add)
-  - Codebase (To add)
-  - Toto model (to add)
+  - [Codebase](https://github.com/DataDog/toto)
+  - [Toto model (Datadog's open-weights model with state-of-the-art performance on BOOM)](https://huggingface.co/Datadog/Toto-Open-Base-1.0)
 
 ## Citation