Update cuML install docs

docs/getting_started/clustering/clustering.md

@@ -110,23 +110,18 @@ The great thing about using cuML's HDBSCAN implementation is that it supports ma
     However, it is still possible to calculate the topic-document probability matrix for the data on which the model was trained (i.e., `.fit` and `.fit_transform`).
 
 !!! note
-    To install cuML with BERTopic, run these commands:
+    To install cuML with BERTopic, run one of these commands:
 
     **For CUDA 12:**
     ```bash
-    !pip install cuml-cu12
-    !pip install bertopic
+    !pip install bertopic cuml-cu12
     ```
 
     **For CUDA 13:**
     ```bash
-    !pip install cuml-cu13
-    !pip install bertopic
+    !pip install bertopic cuml-cu13
     ```
 
-    !!! warning
-        Install cuML first, then BERTopic. Installing both in a single command can fail due to pip resolver limitations with CUDA runtime dependencies.
-
     **Note:** cuML is already installed on Google Colab.
 
     For more detailed information on installing cuML, including additional dependencies and platform-specific instructions, see the [RAPIDS installation guide](https://docs.rapids.ai/install/).

docs/getting_started/dim_reduction/dim_reduction.md

@@ -97,23 +97,18 @@ topic_model = BERTopic(umap_model=umap_model)
 ```
 
 !!! note
-    To install cuML with BERTopic, run these commands:
+    To install cuML with BERTopic, run one of these commands:
 
     **For CUDA 12:**
     ```bash
-    !pip install cuml-cu12
-    !pip install bertopic
+    !pip install bertopic cuml-cu12
     ```
 
     **For CUDA 13:**
     ```bash
-    !pip install cuml-cu13
-    !pip install bertopic
+    !pip install bertopic cuml-cu13
     ```
 
-    !!! warning
-        Install cuML first, then BERTopic. Installing both in a single command can fail due to pip resolver limitations with CUDA runtime dependencies.
-
     **Note:** cuML is already installed on Google Colab.
 
     For more detailed information on installing cuML, including additional dependencies and platform-specific instructions, see the [RAPIDS installation guide](https://docs.rapids.ai/install/).

docs/getting_started/tips_and_tricks/tips_and_tricks.md

@@ -181,23 +181,18 @@ embeddings = normalize(embeddings)
     However, it is still possible to calculate the topic-document probability matrix for the data on which the model was trained (i.e., `.fit` and `.fit_transform`).
 
 !!! note
-    To install cuML with BERTopic, run these commands:
+    To install cuML with BERTopic, run one of these commands:
 
     **For CUDA 12:**
     ```bash
-    !pip install cuml-cu12
-    !pip install bertopic
+    !pip install bertopic cuml-cu12
     ```
 
     **For CUDA 13:**
     ```bash
-    !pip install cuml-cu13
-    !pip install bertopic
+    !pip install bertopic cuml-cu13
     ```
 
-    !!! warning
-        Install cuML first, then BERTopic. Installing both in a single command can fail due to pip resolver limitations with CUDA runtime dependencies.
-
     **Note:** cuML is already installed on Google Colab.
 
     For more detailed information on installing cuML, including additional dependencies and platform-specific instructions, see the [RAPIDS installation guide](https://docs.rapids.ai/install/).