import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
import logging

logging.basicConfig(level=logging.INFO)

def load_model():
    """Load the model and tokenizer."""
    logger.info("Loading model...")
    model = AutoModelForCausalLM.from_pretrained("iben/Abuja-01", trust_remote_code=True)
    tokenizer = AutoTokenizer.from_pretrained("iben/Abuja-01", trust_remote_code=True)
    return model, tokenizer

View the full code on GitHub Gist

Training Details

Base Model: <a href="https://huggingface.co/Qwen/Qwen2-0.5B-Instruct" target="_blank" rel="noopener noreferrer" class="text-blue-400 hover:text-blue-300 underline underline-offset-2 transition-colors">Qwen/Qwen2-0.5B-Instruct
Training Method: ORPO (Odds Ratio Preference Optimization)
Training Dataset: trl-lib/ultrafeedback_binarized
Training Time: 1 hour 32 minutes
Hardware: Single GPU

This model was trained using ORPO (Odds Ratio Preference Optimization), a method that doesn't require a reference model. The training configuration included:

Learning Rate: 1e-5
Batch Size: 4
Gradient Accumulation Steps: 4
Training Epochs: 1

Framework Versions

TRL: 0.13.0
Transformers: 4.48.1
PyTorch: 2.5.1+cu121
Datasets: 3.2.0
Tokenizers: 0.21.0