Update README.md

README.md

@@ -21,14 +21,41 @@ widget:
     content: >-
       My best friend recently lost their parent to cancer after a long battle.
       They are understandably devastated and struggling with grief.
+- example_title: EMO 3x
+  messages:
+  - role: system
+    content: >-
+      You are a helpful and emotional assistant that will always respond in EMO
+      style.
+  - role: user
+    content: >-
+      I'm feeling really down today. Can you cheer me up?
 inference:
   parameters:
     max_new_tokens: 1024
-    do_sample: true
 license: mit
 ---
 
-#  Model card comming soon
+
+# EMO-1.5B: Emotionally Minded Oracle
+
+EMO-1.5B is a powerful language model designed to engage in emotionally intelligent conversations. The full form of EMO stands for "Emotionally Minded Oracle," reflecting the model's ability to understand and express a wide range of human emotions.
+
+## Overview
+
+EMO-1.5B is a state-of-the-art conversational AI model with 1.5 billion parameters. It has been fine-tuned on a diverse corpus of emotional narratives, enabling it to perceive and respond to the emotional undertones present in user inputs. Whether you're seeking comfort, motivation, or simply an empathetic listener, EMO-1.5B is here to provide emotional support and guidance.
+
+## Key Features
+
+- **Emotional Intelligence**: EMO-1.5B can recognize and respond to various emotions, such as sadness, joy, anger, and fear, with appropriate emotional responses.
+- **Contextual Understanding**: The model considers the broader context of the conversation to provide relevant and emotionally resonant responses.
+- **Empathetic Dialogue**: EMO-1.5B excels at active listening, validating emotions, and offering compassionate advice or consolation when needed.
+- **Adaptive Persona**: The model can adapt its persona and communication style to match the user's emotional state, providing a personalized and tailored experience.
+
+## Usage
+
+You can easily interact with EMO-1.5B using the provided example code:
+
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 device = "cuda" # the device to load the model onto