nwokikeonyeka/igbo-phi3-translator

Igbo Basic Translator (GGUF)

This is a specialist AI model that was fine-tuned from Microsoft's Phi-3-mini-4k-instruct on over 522,000 English-to-Igbo translation pairs.

As a result of this deep, specialized training, this model is not a general-purpose chatbot. It is a dedicated, one-way translation tool.

It excels at one task: responding to the prompt Translate this English sentence to Igbo: '...'.

Final Model (GGUF): nwokikeonyeka/igbo-phi3-translator

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🚀 How to Test (Live Demo in Google Colab)

You can test this translator directly in your browser.

Note: This demo will install the model and run it on your Colab CPU. It may be slow (10-30 seconds per translation), but it is the simplest and most reliable way to run it.

One-Cell Colab Demo:

Copy and paste this entire block into a single Google Colab cell.

# --- 1. Install the SIMPLE, CPU-only version ---
print("---  installing llama-cpp-python (CPU)... ---")
# This is the simple install. No build tools needed.
!pip install llama-cpp-python

print("\n--- ✅ All libraries installed! ---")

# --- 2. Import Libraries ---
from llama_cpp import Llama
from huggingface_hub import hf_hub_download
import os

# --- 3. Define and Download Your Model ---
MODEL_NAME = "phi-3-mini-4k-instruct.Q4_K_M.gguf"
REPO_ID = "nwokikeonyeka/igbo-phi3-translator"

print(f"\n--- ⬇️ Downloading {MODEL_NAME} from {REPO_ID} ---")
model_path = hf_hub_download(
    repo_id=REPO_ID,
    filename=MODEL_NAME
)
print(f"--- ✅ Model downloaded to: {model_path} ---")

# --- 4. Load the Model onto the CPU ---
print("--- 🧠 Loading model onto CPU... (This may take a moment) ---")
llm = Llama(
    model_path=model_path,
    n_gpu_layers=0,  # 0 = Use CPU ONLY
    n_ctx=1024,      # Context size
    verbose=False    # Silence llama.cpp logs
)
print("--- ✅ Model loaded! Ready to test. ---")

# --- 5. Start the Interactive Test Loop ---
print("\n--- 🤖 Igbo Translator Test ---")
print("This model is a specialist. It only responds to the 'Translate' prompt.")
print("Type 'quit' or 'exit' to stop.")
print("-" * 40)

while True:
    try:
        user_prompt = str(input("English: "))
    except EOFError:
        break
        
    if user_prompt.lower() in ["quit", "exit"]:
        print("--- 👋 Test ended. ---")
        break
    if not user_prompt.strip():
        continue

    # 1. Format the prompt EXACTLY as it was trained
    full_prompt = f"Translate this English sentence to Igbo: '{user_prompt}'"
    formatted_input = f"<s>[INST] {full_prompt} [/INST]"

    print("Igbo AI: ...thinking...")

    # 2. Run Inference
    response = llm(
        formatted_input,
        max_tokens=100,          # Max length of the translation
        stop=["</s>", "[INST]"], # Stop when it finishes its response
        echo=False               # Don't repeat the prompt
    )
    
    # 3. Print the clean result
    try:
        ai_response = response["choices"][0]["text"].strip()
        print(f"Igbo AI: {ai_response}")
    except (IndexError, KeyError):
        print("Igbo AI: (No response generated. Make sure you typed in English.)")

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Local Usage (Ollama / llama.cpp)

You can also run this model locally.

For Ollama:

# Pull the model
ollama pull nwokikeonyeka/igbo-phi3-translator
  
# Run it
ollama run nwokikeonyeka/igbo-phi3-translator "Translate this English sentence to Igbo: 'Hello, how are you?'"

For llama.cpp:

./main -m ./phi-3-mini-4k-instruct.Q4_K_M.gguf -n 128 -p "<s>[INST] Translate this English sentence to Igbo: 'Hello, how are you?' [/INST]"