import torch
from transformers import pipeline

model_ckpt = "e-hossam96/arabic-nano-gpt-v2"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")


lm = pipeline(task="text-generation", model=model_ckpt, device=device)

prompt = """المحرك النفاث هو محرك ينفث الموائع (الماء أو الهواء) بسرعة فائقة \
لينتج قوة دافعة اعتمادا على مبدأ قانون نيوتن الثالث للحركة. \
هذا التعريف الواسع للمحركات النفاثة يتضمن أيضا"""

output = lm(prompt, max_new_tokens=128)

print(output[0]["generated_text"])

Model description

Embedding Size: 384
Attention Heads: 6
Attention Layers: 8

Training and evaluation data

The entire wikipedia dataset was split into three splits based on the 90-5-5 ratios.

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0001
train_batch_size: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 256
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.01
num_epochs: 8

Training Loss

Validation Loss

Framework versions

Transformers 4.45.2
Pytorch 2.5.0
Datasets 3.0.1
Tokenizers 0.20.1