Update README.md

README.md

@@ -11,7 +11,7 @@ tags:
 **Model Summary:** 
 
 
-Granite-3.3-2B-Base is a decoder-only language model with a 128K token context window. It improves upon Granite-3.1-2B-Base by adding support for Fill-in-the-Middle (FIM) using specialized tokens, enabling the model to generate content conditioned on both prefix and suffix. This makes it well-suited for tasks like code completion tasks.
+Granite-3.3-2B-Base is a decoder-only language model with a 128K token context window. It improves upon Granite-3.1-2B-Base by adding support for Fill-in-the-Middle (FIM) using specialized tokens, enabling the model to generate content conditioned on both prefix and suffix. This makes it well-suited for code completion tasks.
 
 
 
@@ -42,7 +42,7 @@ Then, copy the code snippet below to run the example.
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 device = "auto"
-model_path = "ibm-granite/granite-3.3-2B-base"
+model_path = "ibm-granite/granite-3.3-2b-base"
 tokenizer = AutoTokenizer.from_pretrained(model_path)
 # drop device_map if running on CPU
 model = AutoModelForCausalLM.from_pretrained(model_path, device_map=device)
@@ -230,7 +230,7 @@ contains a recitation of the related paragraph before the answer.
 We train Granite 3.3 Language Models using IBM's super computing cluster, Blue Vela, which is outfitted with NVIDIA H100 GPUs. This cluster provides a scalable and efficient infrastructure for training our models over thousands of GPUs.
 
 **Ethical Considerations and Limitations:** 
-The use of Large Language Models involves risks and ethical considerations people must be aware of, including but not limited to: bias and fairness, misinformation, and autonomous decision-making. Granite-3.3-2B-Base model is not the exception in this regard. Even though this model is suited for multiple generative AI tasks, it has not undergone any safety alignment, there it may produce problematic outputs. Additionally, it remains uncertain whether smaller models might exhibit increased susceptibility to hallucination in generation scenarios by copying text verbatim from the training dataset due to their reduced sizes and memorization capacities. This aspect is currently an active area of research, and we anticipate more rigorous exploration, comprehension, and mitigations in this domain. Regarding ethics, a latent risk associated with all Large Language Models is their malicious utilization. We urge the community to use Granite-3.3-B-Base model with ethical intentions and in a responsible way. 
+The use of Large Language Models involves risks and ethical considerations people must be aware of, including but not limited to: bias and fairness, misinformation, and autonomous decision-making. Granite-3.3-2B-Base model is not the exception in this regard. Even though this model is suited for multiple generative AI tasks, it has not undergone any safety alignment, there it may produce problematic outputs. Additionally, it remains uncertain whether smaller models might exhibit increased susceptibility to hallucination in generation scenarios by copying text verbatim from the training dataset due to their reduced sizes and memorization capacities. This aspect is currently an active area of research, and we anticipate more rigorous exploration, comprehension, and mitigations in this domain. Regarding ethics, a latent risk associated with all Large Language Models is their malicious utilization. We urge the community to use Granite-3.3-2B-Base model with ethical intentions and in a responsible way. 
 
 **Resources**
 - ⭐️ Learn about the latest updates with Granite: https://www.ibm.com/granite