Update README.md

README.md

@@ -11,7 +11,7 @@ More information about making the model available here: 🔗[Don't stop DPOptimi
 
 Author: Jan Kocoń     🔗[LinkedIn](https://www.linkedin.com/in/jankocon/)     🔗[Google Scholar](https://scholar.google.com/citations?user=pmQHb5IAAAAJ&hl=en&oi=ao)     🔗[ResearchGate](https://www.researchgate.net/profile/Jan-Kocon-2)
 
-The "Neuronovo/neuronovo-7B-v0.2" model represents an advanced and fine-tuned version of a large language model, initially based on "CultriX/MistralTrix-v1." Several key characteristics and features of this model:
+The "Neuronovo/neuronovo-9B-v0.2" model represents an advanced and fine-tuned version of a large language model, initially based on "CultriX/MistralTrix-v1." Several key characteristics and features of this model:
 
 1. **Training Dataset**: The model is trained on a dataset named "Intel/orca_dpo_pairs," likely specialized for dialogue and interaction scenarios. This dataset is formatted to differentiate between system messages, user queries, chosen and rejected answers, indicating a focus on natural language understanding and response generation in conversational contexts.
 
@@ -25,9 +25,9 @@ The "Neuronovo/neuronovo-7B-v0.2" model represents an advanced and fine-tuned ve
 
 6. **Performance and Output Capabilities**: Configured for causal language modeling, the model is capable of handling tasks that involve generating text or continuing dialogues, with a maximum prompt length of 1024 tokens and a maximum generation length of 1536 tokens. This suggests its aptitude for extended dialogues and complex language generation scenarios.
 
-7. **Special Features and Efficiency**: The use of techniques like LoRA, DPO training, and specific fine-tuning methods indicates that the "Neuronovo/neuronovo-7B-v0.2" model is not only powerful in terms of language generation but also optimized for efficiency, particularly in terms of computational resource management.
+7. **Special Features and Efficiency**: The use of techniques like LoRA, DPO training, and specific fine-tuning methods indicates that the "Neuronovo/neuronovo-9B-v0.2" model is not only powerful in terms of language generation but also optimized for efficiency, particularly in terms of computational resource management.
 
-In summary, "Neuronovo/neuronovo-7B-v0.2" is a highly specialized, efficient, and capable large language model, fine-tuned for complex language generation tasks in conversational AI, leveraging state-of-the-art techniques in model adaptation and efficient training methodologies.
+In summary, "Neuronovo/neuronovo-9B-v0.2" is a highly specialized, efficient, and capable large language model, fine-tuned for complex language generation tasks in conversational AI, leveraging state-of-the-art techniques in model adaptation and efficient training methodologies.
 
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/605f77e5575f3b3e6beb9067/c7AQrpmJVC6X-6cz3OHfc.png)