Update README.md

README.md

@@ -17,7 +17,7 @@ Summary description and brief definition of inputs and outputs.
 
 ### Description
 
-Athene CodeGemma 2 7B v1.1 is a collection of lightweight open code models built on top of Gemma. CodeGemma models are text-to-text and text-to-code decoder-only models and are available as a 7 billion pretrained variant that specializes in code completion and code generation tasks, a 7 billion parameter instruction-tuned variant for code chat and instruction following and a 2 billion parameter pretrained variant for fast code completion.
+Athene CodeGemma 2 7B v1.2 is a collection of lightweight open code models built on top of Gemma. CodeGemma models are text-to-text and text-to-code decoder-only models and are available as a 7 billion pretrained variant that specializes in code completion and code generation tasks, a 7 billion parameter instruction-tuned variant for code chat and instruction following and a 2 billion parameter pretrained variant for fast code completion.
 Supervised Fine-tuning with coding datasets.
 
 similar to:
@@ -37,8 +37,8 @@ This model is intended to answer questions about code fragments, to generate cod
 
 ```python
 from transformers import GemmaTokenizer, AutoModelForCausalLM
-tokenizer = GemmaTokenizer.from_pretrained("EpistemeAI/Athene-codegemma-2-7b-it-alpaca-v1.1")
-model = AutoModelForCausalLM.from_pretrained("EpistemeAI/Athene-codegemma-2-7b-it-alpaca-v1.1")
+tokenizer = GemmaTokenizer.from_pretrained("EpistemeAI/Athene-codegemma-2-7b-it-alpaca-v1.2")
+model = AutoModelForCausalLM.from_pretrained("EpistemeAI/Athene-codegemma-2-7b-it-alpaca-v1.2")
 input_text = "Write me a Python function to calculate the nth fibonacci number."
 input_ids = tokenizer(input_text, return_tensors="pt")
 outputs = model.generate(**input_ids)
@@ -56,7 +56,7 @@ Let's load the model and apply the chat template to a conversation. In this exam
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import transformers
 import torch
-model_id = "EpistemeAI/Athene-codegemma-2-7b-it-alpaca-v1.1"
+model_id = "EpistemeAI/Athene-codegemma-2-7b-it-alpaca-v1.2"
 dtype = torch.bfloat16
 tokenizer = AutoTokenizer.from_pretrained(model_id)
 model = AutoModelForCausalLM.from_pretrained(
@@ -110,7 +110,7 @@ Data used for model training and how the data was processed.
 
 Supervised Fine-tuning with coding python, java datasets
  
-### Example: Athene CodeGemma 2 7B v1.1
+### Example: Athene CodeGemma 2 7B v1.2
 Athene CodeGemma 2 7B v1.1 successfully created snake game without errors compare to original codegemma-7b-it