Update README.md

README.md

@@ -6,13 +6,16 @@ language:
 ---
 <img src="https://huggingface.co/CerebrumTech/cere-llama-3-8b-tr/resolve/main/cere2.png"
 alt="CEREBRUM LLM" width="420"/>
-# CERE-LLMA-3-8b-TR
 
-This model is an fine-tuned version of a Llama3 8b Large Language Model (LLM) for Turkish. It was trained on a high quality Turkish instruction sets created from various open-source and internal resources. Turkish Instruction dataset carefully annotated to carry out Turkish instructions in an accurate and organized manner. 
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6639e48c27ef2d37a71eb4aa/Ds_KOVYwhRQ1FQY8S4WqO.png)
+
+# CERE V2 -LLMA-3.1-8b-TR
+
+This model is an fine-tuned version of a Llama3.1 8b Large Language Model (LLM) for Turkish. It was trained on a high quality Turkish instruction sets created from various open-source and internal resources. Turkish Instruction dataset carefully annotated to carry out Turkish instructions in an accurate and organized manner. 
 
 ## Model Details
 
-- **Base Model**: LLMA 3 7B based LLM
+- **Base Model**: LLMA 3.1 8B based LLM
 - **Tokenizer Extension**: Specifically extended for Turkish
 - **Training Dataset**: Cleaned Turkish raw data with 5 billion tokens, custom Turkish instruction sets
 - **Training Method**: Initially with DORA, followed by fine-tuning with LORA
@@ -35,11 +38,11 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
 device = "cuda" # the device to load the model onto
 
 model = AutoModelForCausalLM.from_pretrained(
-    "Cerebrum/cere-llama-3-8b-tr",
+    "Cerebrum/cere-llama-3.1-8B-tr",
     torch_dtype="auto",
     device_map="auto"
 )
-tokenizer = AutoTokenizer.from_pretrained("Cerebrum/cere-llama-3-8b-tr")
+tokenizer = AutoTokenizer.from_pretrained("Cerebrum/cere-llama-3.1-8B-tr")
 
 prompt = "Python'da ekrana 'Merhaba Dünya' nasıl yazılır?"
 messages = [