🤖 Igbo-Phi3-Bilingual-Chat

A specialized Bilingual AI Assistant trained to converse fluently in Igbo and English.

Unlike my previous attempt which was a simple translation model, this AI is a conversational agent. It can chat, explain concepts, reason, and define words in both languages while retaining the general intelligence of its base model (Phi-3).

📥 Download Models

⚡ Quick Colab Demo

If you don't have a Python environment set up, you can copy-paste this code into a Google Colab cell to test the model immediately.

# --- 1. Install Libraries ---
!pip install llama-cpp-python huggingface_hub

# --- 2. Download & Load Model ---
from huggingface_hub import hf_hub_download
from llama_cpp import Llama

REPO_ID = "nwokikeonyeka/Igbo-Phi3-Bilingual-Chat-v1-merged-Q5_K_M-GGUF"
FILENAME = "igbo-phi3-bilingual-chat-v1-merged-q5_k_m.gguf"

print(f"Downloading {FILENAME}...")
model_path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME)

print("Loading model...")
llm = Llama(model_path=model_path, n_ctx=2048, verbose=False)

# --- 3. Chat Loop ---
print("\n🤖 IGBO CHATBOT READY (Type 'exit' to quit)")
while True:
    user_input = input("\nYou: ")
    if user_input.lower() in ['exit', 'quit']: break
    
    # Correct Phi-3 Prompt Template
    prompt = f"<s><|user|>\n{user_input}<|end|>\n<|assistant|>\n"
    
    output = llm(prompt, max_tokens=256, stop=["<|end|>"], echo=False)
    print(f"AI: {output['choices'][0]['text']}")

📚 Training Data & Credits

This model was trained on a curated mix of over 700,000 examples to ensure a balance between language fluency and general logic. Grateful acknowledgment to the creators of these open datasets:

1. Fluency (522k pairs): ccibeekeoc42/english_to_igbo
   Primary source for sentence-level translation and grammar.
2. Vocabulary (5k definitions): nkowaokwu/ibo-dict
   Provides deep knowledge of specific Igbo words and definitions.
3. General Memory (200k chats): HuggingFaceH4/ultrachat_200k
   Used to maintain the model's ability to chat, reason, and follow instructions without "forgetting" general knowledge.

🚀 Quick Start (Local)

You can run the GGUF model on any computer with Python installed.

1. Install Dependencies

pip install llama-cpp-python huggingface_hub

2. Run the Chat Script

Download the chat.py file from this repository and run it:

python chat.py

🧠 Training Methodology: "The Colab Relay Race"

Training a full LLM on a free Google Colab GPU usually causes timeouts before completion. This project used a "Relay Race" strategy:

1. Checkpointing: The training script saves progress every 500 steps to Hugging Face.
2. Resuming: When Colab times out (approx. every 4 hours), a new session is started.
3. Relaying: The script automatically pulls the last checkpoint and resumes training exactly where it stopped.

Stats:

• Base Model: Microsoft Phi-3-mini-4k-instruct
• Total Steps: 44,500
• Epochs: 1
• Training Time: ~20 Hours (across multiple sessions)

🛠️ Prompt Template

If you use this model in Ollama, LM Studio, or Jan.ai, ensure you use the Phi-3 prompt format for the best results:

<s><|user|>
{Your Question Here}<|end|>
<|assistant|>
{AI Response Here}<|end|>