Improve language tag

Hi! As the model is multilingual, this is a PR to add other languages than English to the language tag to improve the referencing. Note that 29 languages are announced in the README, but only 13 are explicitly listed. I was therefore only able to add these 13 languages.

README.md

@@ -1,195 +1,207 @@
----
-language:
-- en
-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**
-```bash
-pip install llama-cpp-python
-```
-#### **Step 2: Mount Google Drive & Load Model**
-```python
-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**
-```bash
-pip install llama-cpp-python huggingface_hub
-```
-#### **Step 2: Download Model from Hugging Face**
-```python
-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**
-```bibtex
-@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**
-```bash
-pip install huggingface_hub
-```
-### **Step 2️⃣: Log in**
-```bash
-huggingface-cli login
-```
-
-### **Step 3️⃣: Create a Model Repo**
-- Go to [Hugging Face Models](https://huggingface.co/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**
-```bash
-huggingface-cli upload your-username/your-gguf-model your_model.gguf
+---
+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**
+```bash
+pip install llama-cpp-python
+```
+#### **Step 2: Mount Google Drive & Load Model**
+```python
+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**
+```bash
+pip install llama-cpp-python huggingface_hub
+```
+#### **Step 2: Download Model from Hugging Face**
+```python
+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**
+```bibtex
+@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**
+```bash
+pip install huggingface_hub
+```
+### **Step 2️⃣: Log in**
+```bash
+huggingface-cli login
+```
+
+### **Step 3️⃣: Create a Model Repo**
+- Go to [Hugging Face Models](https://huggingface.co/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**
+```bash
+huggingface-cli upload your-username/your-gguf-model your_model.gguf
 ```