Update README.md

README.md

@@ -26,17 +26,25 @@ English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian,
 **Intended Use:**
 Prominent use cases of LLMs in text-to-text generation include summarization, text classification, extraction, question-answering, and other long-context tasks. All Granite Base models are able to handle these tasks as they were trained on a large amount of data from various domains. Moreover, they can serve as baseline to create specialized models for specific application scenarios.
 
-**Generation:** 
-This is a simple example of how to use Granite-4.0-Tiny-Base-Preview model.
+**Usage:** 
+You need to install transformer from source to use this checkpoint.
+<!-- This is a simple example of how to use Granite-4.0-Tiny-Base-Preview model. -->
 
-Install the following libraries:
+<!-- Usage: Install transformer from source or use transformer version v4.45 to use this checkpoint. -->
+
+HuggingFace PR: https://github.com/huggingface/transformers/pull/37658
+
+Install transformer from source: https://huggingface.co/docs/transformers/en/installation#install-from-source
+
+<!-- Install the following libraries:
 
 ```shell
 pip install torch torchvision torchaudio
 pip install accelerate
 pip install transformers
-```
-Then, copy the code snippet below to run the example.
+``` -->
+**Generation:**
+After installation, copy the code snippet below to run the example.
 
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer