Update README.md

README.md

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 This model was converted to GGUF format from [`prithivMLmods/Phi-4-Empathetic`](https://huggingface.co/prithivMLmods/Phi-4-Empathetic) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
 Refer to the [original model card](https://huggingface.co/prithivMLmods/Phi-4-Empathetic) for more details on the model.
 
+---
+Model details:
+-
+[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.
+
+---
 ## Use with llama.cpp
 Install llama.cpp through brew (works on Mac and Linux)