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Model: prithivMLmods/Phi-4-Empathetic Source: Original Platform
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README.md
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README.md
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---
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license: mit
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language:
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- en
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base_model:
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- microsoft/phi-4
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- text-generation-inference
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- phi
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- phi3
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- llama
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- human_like_reasoning
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---
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# **Phi-4 Empathetic [ Responsible Reasoning & Emotional Thought Generation ]**
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`[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. 🌟
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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.
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---
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# **Dataset Info**
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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:
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- **Emotional Support Applications** 🤗
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- **Responsible Conversations** 💬
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- **Thoughtful Problem-Solving** 🧠
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---
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# **Run with Transformers**
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```python
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# pip install accelerate
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/Phi-4-Empathetic")
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model = AutoModelForCausalLM.from_pretrained(
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"prithivMLmods/Phi-4-Empathetic",
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device_map="auto",
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torch_dtype=torch.bfloat16,
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)
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input_text = "Can you share some words of encouragement for someone feeling down?"
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input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
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outputs = model.generate(**input_ids, max_new_tokens=32)
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print(tokenizer.decode(outputs[0]))
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```
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You can ensure correct formatting for empathetic dialogue by using `tokenizer.apply_chat_template` as follows:
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```python
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messages = [
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{"role": "user", "content": "Can you share some words of encouragement for someone feeling down?"},
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]
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input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt", return_dict=True).to("cuda")
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outputs = model.generate(**input_ids, max_new_tokens=256)
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print(tokenizer.decode(outputs[0]))
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```
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---
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# **Intended Use**
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The Phi-4 Empathetic model is optimized for applications that require thoughtful and emotionally aware interactions. Below are some suggested use cases:
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1. **Emotional Support & Counseling** 💖
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- Providing thoughtful responses to users seeking emotional encouragement or advice.
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- Generating empathetic messages for mental health and well-being applications.
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2. **Responsible Dialogue Generation** 🗣️
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- Engaging in nuanced conversations with a focus on fairness, safety, and ethical considerations.
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- Ensuring that interactions remain respectful and aligned with safety guidelines.
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3. **Creative Writing Assistance** ✍️
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- Helping users craft emotionally engaging content, including stories, poems, and personal messages.
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- Assisting in generating content enriched with special characters and emojis for expressive communication.
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4. **Educational Tools** 🎓
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- Offering step-by-step explanations with an empathetic tone for better understanding.
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- Generating thoughtful Q&A responses for various subjects.
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5. **Customer Support** 🤝
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- Automating empathetic responses to customer queries.
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- Handling emotionally sensitive customer service interactions with care.
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6. **Social Media Engagement** 📱
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- Generating creative, engaging, and emotionally resonant posts for social media platforms.
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- Providing personalized message suggestions enriched with emojis and special characters.
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---
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# **Limitations**
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While Phi-4 Empathetic is highly capable, it has certain limitations users should be aware of:
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1. **Bias and Fairness**:
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Despite extensive safety alignment, biases may still emerge in the model’s responses. Users should exercise discretion, particularly in sensitive contexts.
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2. **Emotional Nuance**:
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The model may occasionally misinterpret the emotional tone of a prompt, leading to less relevant or inappropriate responses.
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3. **Real-Time Knowledge**:
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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.
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4. **Safety and Harmlessness**:
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Although the model is aligned with safety standards, there may still be cases where outputs require human oversight to ensure appropriateness.
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5. **Resource Requirements**:
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Running the model efficiently may require significant computational resources, especially in large-scale or real-time applications.
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6. **Ethical Considerations**:
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The model must be used responsibly, avoiding any malicious applications such as generating harmful content or spreading misinformation.
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7. **Domain-Specific Limitations**:
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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.
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---
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# **Special Features**
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1. **Emojis & Special Characters** 🎉💡
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The model can generate responses with emojis and special characters for expressive communication, making it ideal for social media and personal messaging applications.
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2. **Human-Like Reasoning** 🧠
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Fine-tuned for **responsible reasoning** and **empathetic dialogue**, it excels at generating thoughtful and human-like responses.
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3. **Advanced Safety Alignment** 🔒
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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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