Update README.md

README.md

@@ -11,6 +11,35 @@ tags:
 - pytorch_model_hub_mixin
 license: apache-2.0
 pipeline_tag: time-series-forecasting
+thumbnail:
+results:
+  - task: time-series-forecasting
+    dataset: 
+      name: GIFT-Eval
+    metrics:
+      - name: MASE
+        type: mase
+        value: 0.757
+      - name: CRPS
+        type: brier_score
+        value: 0.524
+    source:
+      name: GIFT-Eval Time Series Forecasting Leaderboard
+      url: https://huggingface.co/spaces/Salesforce/GIFT-Eval
+
+  - task: time-series-forecasting
+    dataset:
+      name: BOOM
+    metrics:
+      - name: MASE
+        type: mase
+        value: 0.624
+      - name: CRPS
+        type: brier_score
+        value: 0.717
+    source:
+      name: BOOM 💥 Observability Time-Series Forecasting Leaderboard
+      url: https://huggingface.co/spaces/Datadog/BOOM
 ---
 # Toto-2.0-4m
 
@@ -51,19 +80,22 @@ pip install "toto-2 @ git+https://github.com/DataDog/toto.git#subdirectory=toto2
 import torch
 from toto2 import Toto2Model
 
-model = Toto2Model.from_pretrained("Datadog/Toto-2.0-4m")
-model = model.to("cuda").eval()
+model = Toto2Model.from_pretrained("Datadog/Toto-2.0-22m")
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = model.to(device).eval()
 
 # (batch, n_variates, time_steps)
-target = torch.randn(1, 1, 512, device="cuda")
+target = torch.randn(1, 1, 512, device=device)
 target_mask = torch.ones_like(target, dtype=torch.bool)
-series_ids = torch.zeros(1, 1, dtype=torch.long, device="cuda")
+series_ids = torch.zeros(1, 1, dtype=torch.long, device=device)
 
 # Returns quantiles of shape (9, batch, n_variates, horizon)
 # Quantile levels: [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
 quantiles = model.forecast(
     {"target": target, "target_mask": target_mask, "series_ids": series_ids},
     horizon=96,
+    decode_block_size=768,
+    has_missing_values=False,
 )
 ```