Phi-4 Empathetic [ Responsible Reasoning & Emotional Thought Generation ]
[Phi-4 Empathetic finetuned]
from Microsoft's Phi-4 is an advanced open model built upon a blend of high-quality synthetic datasets, data from filtered public domain websites, and carefully selected academic resources. It excels at responsible human-like reasoning, empathetic dialogue, and emotional thought generation. The model is designed to engage in nuanced, thoughtful conversations, with outputs that can include special characters and emojis for expressive communication. 🌟
Phi-4 Empathetic employs a sophisticated safety post-training approach, leveraging both open-source and proprietary datasets. Safety alignment is achieved using a combination of SFT (Supervised Fine-Tuning) and DPO (Direct Preference Optimization), targeting responsible interaction and emotional awareness in diverse contexts.
Dataset Info
Phi-4 Empathetic is fine-tuned on a carefully curated dataset tailored for empathetic and responsible reasoning tasks. The dataset incorporates the Chain of Thought (CoT) methodology, emphasizing logical reasoning, emotional nuance, and step-by-step thought processes. Additionally, it includes data optimized for generating responses that resonate with human emotions, making it ideal for:
- Emotional Support Applications 🤗
- Responsible Conversations 💬
- Thoughtful Problem-Solving 🧠
Run with Transformers
# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/Phi-4-Empathetic")
model = AutoModelForCausalLM.from_pretrained(
"prithivMLmods/Phi-4-Empathetic",
device_map="auto",
torch_dtype=torch.bfloat16,
)
input_text = "Can you share some words of encouragement for someone feeling down?"
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids, max_new_tokens=32)
print(tokenizer.decode(outputs[0]))
You can ensure correct formatting for empathetic dialogue by using tokenizer.apply_chat_template
as follows:
messages = [
{"role": "user", "content": "Can you share some words of encouragement for someone feeling down?"},
]
input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt", return_dict=True).to("cuda")
outputs = model.generate(**input_ids, max_new_tokens=256)
print(tokenizer.decode(outputs[0]))
Intended Use
The Phi-4 Empathetic model is optimized for applications that require thoughtful and emotionally aware interactions. Below are some suggested use cases:
Emotional Support & Counseling 💖
- Providing thoughtful responses to users seeking emotional encouragement or advice.
- Generating empathetic messages for mental health and well-being applications.
Responsible Dialogue Generation 🗣️
- Engaging in nuanced conversations with a focus on fairness, safety, and ethical considerations.
- Ensuring that interactions remain respectful and aligned with safety guidelines.
Creative Writing Assistance ✍️
- Helping users craft emotionally engaging content, including stories, poems, and personal messages.
- Assisting in generating content enriched with special characters and emojis for expressive communication.
Educational Tools 🎓
- Offering step-by-step explanations with an empathetic tone for better understanding.
- Generating thoughtful Q&A responses for various subjects.
Customer Support 🤝
- Automating empathetic responses to customer queries.
- Handling emotionally sensitive customer service interactions with care.
Social Media Engagement 📱
- Generating creative, engaging, and emotionally resonant posts for social media platforms.
- Providing personalized message suggestions enriched with emojis and special characters.
Limitations
While Phi-4 Empathetic is highly capable, it has certain limitations users should be aware of:
Bias and Fairness:
Despite extensive safety alignment, biases may still emerge in the model’s responses. Users should exercise discretion, particularly in sensitive contexts.Emotional Nuance:
The model may occasionally misinterpret the emotional tone of a prompt, leading to less relevant or inappropriate responses.Real-Time Knowledge:
The model's knowledge is based on the data it was trained on and does not include real-time or post-training updates. It may not reflect recent events or changes in knowledge.Safety and Harmlessness:
Although the model is aligned with safety standards, there may still be cases where outputs require human oversight to ensure appropriateness.Resource Requirements:
Running the model efficiently may require significant computational resources, especially in large-scale or real-time applications.Ethical Considerations:
The model must be used responsibly, avoiding any malicious applications such as generating harmful content or spreading misinformation.Domain-Specific Limitations:
While it performs well in general-purpose tasks, it may need further fine-tuning for highly specialized domains, such as legal, medical, or financial applications.
Special Features
Emojis & Special Characters 🎉💡
The model can generate responses with emojis and special characters for expressive communication, making it ideal for social media and personal messaging applications.Human-Like Reasoning 🧠
Fine-tuned for responsible reasoning and empathetic dialogue, it excels at generating thoughtful and human-like responses.Advanced Safety Alignment 🔒
The model employs iterative SFT and DPO techniques to ensure that its outputs are helpful, harmless, and aligned with ethical standards.
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