diff --git a/packages/tasks/src/model-libraries-snippets.ts b/packages/tasks/src/model-libraries-snippets.ts
index 08921cf533..212686160e 100644
--- a/packages/tasks/src/model-libraries-snippets.ts
+++ b/packages/tasks/src/model-libraries-snippets.ts
@@ -132,6 +132,62 @@ wav = model.generate(text, audio_prompt_path=AUDIO_PROMPT_PATH)
 ta.save("test-2.wav", wav, model.sr)`,
 ];
 
+export const contexttab = (): string[] => {
+	const installSnippet = `pip install git+https://github.com/SAP-samples/contexttab`;
+
+	const classificationSnippet = `# Run a classification task
+from sklearn.datasets import load_breast_cancer
+from sklearn.metrics import accuracy_score
+from sklearn.model_selection import train_test_split
+
+from contexttab import ConTextTabClassifier
+
+# Load sample data
+X, y = load_breast_cancer(return_X_y=True)
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=42)
+
+# Initialize a classifier
+# You can omit checkpoint and checkpoint_revision to use the default model
+clf = ConTextTabClassifier(checkpoint="l2/base.pt", checkpoint_revision="v1.0.0", bagging=1, max_context_size=2048)
+
+clf.fit(X_train, y_train)
+
+# Predict probabilities
+prediction_probabilities = clf.predict_proba(X_test)
+# Predict labels
+predictions = clf.predict(X_test)
+print("Accuracy", accuracy_score(y_test, predictions))`;
+
+	const regressionsSnippet = `# Run a regression task
+from sklearn.datasets import fetch_openml
+from sklearn.metrics import r2_score
+from sklearn.model_selection import train_test_split
+
+from contexttab import ConTextTabRegressor
+
+
+# Load sample data
+df = fetch_openml(data_id=531, as_frame=True)
+X = df.data
+y = df.target.astype(float)
+
+# Train-test split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=42)
+
+# Initialize the regressor
+# You can omit checkpoint and checkpoint_revision to use the default model
+regressor = ConTextTabRegressor(checkpoint="l2/base.pt", checkpoint_revision="v1.0.0", bagging=1, max_context_size=2048)
+
+regressor.fit(X_train, y_train)
+
+# Predict on the test set
+predictions = regressor.predict(X_test)
+
+r2 = r2_score(y_test, predictions)
+print("R² Score:", r2)`;
+	return [installSnippet, classificationSnippet, regressionsSnippet];
+};
+
 export const cxr_foundation = (): string[] => [
 	`# pip install git+https://github.com/Google-Health/cxr-foundation.git#subdirectory=python
 
diff --git a/packages/tasks/src/model-libraries.ts b/packages/tasks/src/model-libraries.ts
index b51e19f3d6..9a961b8955 100644
--- a/packages/tasks/src/model-libraries.ts
+++ b/packages/tasks/src/model-libraries.ts
@@ -208,6 +208,13 @@ export const MODEL_LIBRARIES_UI_ELEMENTS = {
 		repoUrl: "https://github.com/Unbabel/COMET/",
 		countDownloads: `path:"hparams.yaml"`,
 	},
+	contexttab: {
+		prettyLabel: "ConTextTab",
+		repoName: "ConTextTab",
+		repoUrl: "https://github.com/SAP-samples/contexttab",
+		countDownloads: `path_extension:"pt"`,
+		snippets: snippets.contexttab,
+	},
 	cosmos: {
 		prettyLabel: "Cosmos",
 		repoName: "Cosmos",