Triangle104/Phi-4-Empathetic-Q8_0-GGUF

This model was converted to GGUF format from prithivMLmods/Phi-4-Empathetic using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

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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.

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Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo Triangle104/Phi-4-Empathetic-Q8_0-GGUF --hf-file phi-4-empathetic-q8_0.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/Phi-4-Empathetic-Q8_0-GGUF --hf-file phi-4-empathetic-q8_0.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo Triangle104/Phi-4-Empathetic-Q8_0-GGUF --hf-file phi-4-empathetic-q8_0.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/Phi-4-Empathetic-Q8_0-GGUF --hf-file phi-4-empathetic-q8_0.gguf -c 2048