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--- |
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library_name: transformers |
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widget: |
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- example_title: EMO 1 |
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messages: |
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- role: system |
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content: >- |
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You are a helpful and emotional assistant that will always respond in EMO |
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style. |
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- role: user |
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content: >- |
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Imagine you're helping someone who is feeling overhelmed. How do you feel |
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in this situation? |
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- example_title: EMO 2 |
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messages: |
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- role: system |
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content: >- |
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You are a helpful and emotional assistant that will always respond in EMO |
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style. |
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- role: user |
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content: >- |
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My best friend recently lost their parent to cancer after a long battle. |
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They are understandably devastated and struggling with grief. |
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- example_title: EMO 3 |
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messages: |
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- role: system |
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content: >- |
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You are a helpful and emotional assistant that will always respond in EMO |
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style. |
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- role: user |
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content: >- |
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I'm feeling really down today. Can you cheer me up? |
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inference: |
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parameters: |
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max_new_tokens: 1024 |
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license: mit |
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--- |
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# EMO-1.5B: |
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EMO-1.5B is a powerful language model designed to engage in emotionally intelligent conversations. |
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## Overview |
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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. |
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## Key Features |
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- **Emotional Intelligence**: EMO-1.5B can recognize and respond to various emotions, such as sadness, joy, anger, and fear, with appropriate emotional responses. |
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- **Contextual Understanding**: The model considers the broader context of the conversation to provide relevant and emotionally resonant responses. |
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- **Empathetic Dialogue**: EMO-1.5B excels at active listening, validating emotions, and offering compassionate advice or consolation when needed. |
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- **Adaptive Persona**: The model can adapt its persona and communication style to match the user's emotional state, providing a personalized and tailored experience. |
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## Usage |
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You can easily interact with EMO-1.5B using the provided example code: |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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device = "cuda" # the device to load the model onto |
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model = AutoModelForCausalLM.from_pretrained( |
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"OEvortex/EMO-1.5B", |
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torch_dtype="auto", |
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device_map="auto" |
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) |
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tokenizer = AutoTokenizer.from_pretrained("OEvortex/EMO-1.5B") |
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prompt = "Imagine you're helping someone who is feeling overwhelmed. How do you feel in this situation?" |
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messages = [ |
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{"role": "system", "content": "You are a helpful and emotional assistant that will always respond in EMO style"}, |
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{"role": "user", "content": prompt} |
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] |
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text = tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True |
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) |
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model_inputs = tokenizer([text], return_tensors="pt").to(device) |
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generated_ids = model.generate( |
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model_inputs.input_ids, |
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max_new_tokens=512 |
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) |
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generated_ids = [ |
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) |
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] |
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] |
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print(response) |
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``` |