README.md

---
license: mit
language:
- en
base_model: prithivMLmods/Phi-4-Empathetic
pipeline_tag: text-generation
library_name: transformers
tags:
- text-generation-inference
- phi
- phi3
- llama
- human_like_reasoning
- llama-cpp
- gguf-my-repo
---

# Triangle104/Phi-4-Empathetic-Q5_K_M-GGUF
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)

```bash
brew install llama.cpp

```
Invoke the llama.cpp server or the CLI.

### CLI:
```bash
llama-cli --hf-repo Triangle104/Phi-4-Empathetic-Q5_K_M-GGUF --hf-file phi-4-empathetic-q5_k_m.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/Phi-4-Empathetic-Q5_K_M-GGUF --hf-file phi-4-empathetic-q5_k_m.gguf -c 2048
```

Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) 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-Q5_K_M-GGUF --hf-file phi-4-empathetic-q5_k_m.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/Phi-4-Empathetic-Q5_K_M-GGUF --hf-file phi-4-empathetic-q5_k_m.gguf -c 2048
```