EMO-1.5B:

EMO-1.5B is a powerful language model designed to engage in emotionally intelligent conversations.

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:

from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto

model = AutoModelForCausalLM.from_pretrained(
    "OEvortex/EMO-1.5B",
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("OEvortex/EMO-1.5B")

prompt = "Imagine you're helping someone who is feeling overwhelmed. How do you feel in this situation?"
messages = [
    {"role": "system", "content": "You are a helpful and emotional assistant that will always respond in EMO style"},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)

generated_ids = model.generate(
    model_inputs.input_ids,
    max_new_tokens=512
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)