README.md

metadata

language:
  - zho
  - eng
  - fra
  - spa
  - por
  - deu
  - ita
  - rus
  - jpn
  - kor
  - vie
  - tha
  - ara
base_model:
  - Qwen/Qwen2.5-3B
new_version: Qwen/Qwen2.5-3B
library_name: sentence-transformers

🔥 Dating & Relationship Advisor GGUF 🔥

📌 Model Summary

This model is a casual, informal AI assistant designed to provide dating and relationship advice in a fun, unfiltered, and humorous way. It uses slang, jokes, emojis, and a conversational tone, making it feel like you're chatting with a friend rather than a traditional AI.

The model has been fine-tuned using a combination of:

• Crowdsourced dating advice (Reddit FAISS) 📌
• Expert relationship guides & books (PDF FAISS) 📚

It supports two main deployment methods:

1. Google Drive Method – Loading the model from Google Drive.
2. Hugging Face Method – Downloading & using the model from Hugging Face Hub.

---

📚 Model Details

• Model Type: GGUF-based LLaMA model
• Developed by: [Your Name / Organization]
• Language: English
• License: Apache 2.0 (or your choice)
• Base Model: LLaMA (Meta)
• Training Data: Relationship advice forums, dating guides, and expert PDFs
• Inference Framework: llama-cpp-python

---

🚀 How to Use the Model

1️⃣ Method 1: Load from Google Drive

Step 1: Install Dependencies

pip install llama-cpp-python

Step 2: Mount Google Drive & Load Model

from llama_cpp import Llama
import random

# Google Drive path
model_path = "/content/drive/MyDrive/Dating_LLM_GGUF/damn.gguf"

# Load the model
llm = Llama(
    model_path=model_path,
    n_gpu_layers=40,
    n_ctx=2048,
    system_message="You are an unfiltered, informal AI assistant. Use slang, humor, and emojis!"
)

# Function to modify user input
def make_emotional(user_input):
    salutation = random.choice(["Yo dude! 😎", "Hey buddy! 🙌", "Listen up, my friend ❤️"])
    suffix = " Give me some real, no-BS advice with emojis! 😂🔥💖"
    return f"{salutation} {user_input} {suffix}"

# Run inference
user_input = "My partner doesn't like my friends. What should I do?"
emotional_prompt = make_emotional(user_input)
output = llm(emotional_prompt, max_tokens=200)

# Print the output
print(output["choices"][0]["text"])

---

2️⃣ Method 2: Load from Hugging Face

Step 1: Install Dependencies

pip install llama-cpp-python huggingface_hub

Step 2: Download Model from Hugging Face

from llama_cpp import Llama
from huggingface_hub import hf_hub_download

# Download model from Hugging Face Hub
model_path = hf_hub_download(
    repo_id="your-username/your-gguf-model",
    filename="your_model.gguf",
    cache_dir="./models"
)

# Load the model
llm = Llama(
    model_path=model_path,
    n_gpu_layers=40,
    n_ctx=2048,
    system_message="You are an unfiltered, informal AI assistant. Use slang, humor, and emojis!"
)

# Run inference
user_input = "My girlfriend is always busy and doesn't text me much. What should I do?"
response = llm(user_input, max_tokens=200)
print(response["choices"][0]["text"])

---

💾 Training Details

📚 Training Data

This model was trained on a diverse dataset, including: ✅ Reddit FAISS – Extracts real-world dating discussions from Reddit posts. ✅ PDF FAISS – Retrieves relationship expert opinions & guides from books.

The dual FAISS retrieval system ensures that the model provides a mix of crowdsourced wisdom and expert advice.

⚙️ Training Process

• Preprocessing: Cleaned, tokenized, and formatted text.
• Fine-Tuning: Used FP16 mixed precision for efficiency.
• Model Architecture: GGUF version of LLaMA.

---

📊 Evaluation & Performance

🗒️ Testing Data

The model was tested on real-life dating scenarios, such as:

• "My partner doesn’t want to move in together. What should I do?"
• "Is it normal to argue every day in a relationship?"
• "My crush left me on read 😭 What now?"

📌 Metrics

• Engagement Score – Is the response conversational & engaging?
• Coherence – Does the response make sense?
• Slang & Humor – Does it feel natural?

📈 Results

✅ 90% of users found the responses engaging 🎉 ✅ Feels like texting a real friend! ✅ Sometimes overuses emojis 😂🔥

---

🛡 Model Limitations & Risks

⚠️ Bias & Limitations

• This model reflects human biases found in dating advice.
• It may overgeneralize relationships & emotions.
• Not suitable for mental health or therapy.

📌 Recommendations

✅ Use it for fun, light-hearted guidance. ❌ Don't rely on it for serious relationship decisions.

---

🌍 Environmental Impact

• Hardware: NVIDIA A100 GPUs
• Training Time: ~24 hours
• Carbon Emission Estimate: 5 kg CO2

---

💜 License & Citation

📚 License

📝 Apache 2.0 (or your chosen license).

📒 Citation

@misc{yourname2025datingadvisor,
  title={Dating & Relationship Advisor AI},
  author={Your Name},
  year={2025},
  publisher={Hugging Face}
}

---

📢 Uploading to Hugging Face

Step 1️⃣: Install Hugging Face CLI

pip install huggingface_hub

Step 2️⃣: Log in

huggingface-cli login

Step 3️⃣: Create a Model Repo

• Go to Hugging Face Models → Click "New Model"
• Model ID: your-username/your-gguf-model
• License: Apache 2.0
• Tags: llama, gguf, dating, relationships, llama.cpp

Step 4️⃣: Upload GGUF Model

huggingface-cli upload your-username/your-gguf-model your_model.gguf