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---
library_name: transformers
widget:
- example_title: EMO 1
messages:
- role: system
content: >-
You are a helpful and emotional assistant that will always respond in EMO
style.
- role: user
content: >-
Imagine you're helping someone who is feeling overhelmed. How do you feel
in this situation?
- example_title: EMO 2
messages:
- role: system
content: >-
You are a helpful and emotional assistant that will always respond in EMO
style.
- role: user
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 3
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
license: mit
---
# 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:
```python
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)
``` |