Passing a generic word after the <name-identifier> that is in a non-English language helps ensure the model responds in the question language (see: any example).
Model generations (in general) remain semantically consistent, even if the generations switch from <language>to English in the middle of the generated text. This demonstrates some sort of "universal concept understanding"

Usage in python

Install the transformers library if you don't have it:

pip install -U transformers

load the model into a pipeline object:

from transformers import pipeline
import torch
device = 'cuda' if torch.cuda.is_available() else 'cpu'
my_chatbot = pipeline('text-generation', 
                      'pszemraj/mGPT-Peter-2E',
                      device=0 if device == 'cuda' else -1,
                    )

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 4
eval_batch_size: 4
seed: 42
distributed_type: multi-GPU
gradient_accumulation_steps: 8
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine_with_restarts
lr_scheduler_warmup_ratio: 0.05
num_epochs: 1 (in addition to all training on prior checkpoints)

Framework versions

Transformers 4.18.0
Pytorch 1.11.0+cu113
Datasets 2.1.0
Tokenizers 0.12.1