Update README.md

README.md

@@ -8,33 +8,33 @@ base_model:
 pipeline_tag: text-generation
 ---
 
-# Gemma-2-9b-tr
+# Gemma-2-9b-it-tr
 
-Gemma-2-9b-tr is a finetuned version of [google/gemma-2-9b-it](https://huggingface.co/google/gemma-2-9b-it) on a carefully curated and manually filtered dataset of 55k question answering and conversational samples in Turkish.
+Gemma-2-9b-it-tr is a finetuned version of [google/gemma-2-9b-it](https://huggingface.co/google/gemma-2-9b-it) on a carefully curated and manually filtered dataset of 55k question answering and conversational samples in Turkish.
 
 
 ## Training Details
-**Base model:** [google/gemma-2-9b-it](https://huggingface.co/google/gemma-2-9b-it)
-**Training data:** A filtered version of [metedb/turkish_llm_datasets](https://huggingface.co/datasets/metedb/turkish_llm_datasets/) and a small private dataset of 8k conversational samples on various topics.
-**Training setup:** We performed supervised fine tuning with LoRA with `rank=128` and `lora_alpha`=64. Training took 4 days on a single  RTX 6000 Ada.
+**Base model:** [google/gemma-2-9b-it](https://huggingface.co/google/gemma-2-9b-it)  
+**Training data:** A filtered version of [metedb/turkish_llm_datasets](https://huggingface.co/datasets/metedb/turkish_llm_datasets/) and a small private dataset of 8k conversational samples on various topics.  
+**Training setup:** We performed supervised fine tuning with LoRA with `rank=128` and `lora_alpha`=64. Training took 4 days on a single  RTX 6000 Ada.  
 
 Compared to the base model, we find Gemma-2-9b-tr has superior conversational and reasoning skills.
 
 ## Usage
-You can load and use `Gemma-2-9b-tr`as follows.
+You can load and use `neuralwork/gemma-2-9b-it-tr`as follows.
 
 ```py
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
 model = AutoModelForCausalLM.from_pretrained(
-   "neuralwork/gemma-2-9b-tr",
+   "neuralwork/gemma-2-9b-it-tr",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
 )
 
-tokenizer = AutoTokenizer.from_pretrained("neuralwork/gemma-2-9b-tr")
+tokenizer = AutoTokenizer.from_pretrained("neuralwork/gemma-2-9b-it-tr")
 
 messages = [
    {"role": "user", "content": "Python'da bir öğenin bir listede geçip geçmediğini nasıl kontrol edebilirim?"},