Customizing Neural Machine Translation Models with NVIDIA NeMo, Part 1

Introduction to Neural Machine Translation (NMT) using NVIDIA NeMo and ALMA NMT: This post introduces the concept of NMT and showcases two NMT models, NVIDIA NeMo NMT and ALMA NMT, as examples of customizing NMT models with fine-tuning on custom datasets.

Overview of NVIDIA NeMo NMT models: NVIDIA NeMo NMT models are discussed as efficient tools for creating, customizing, and deploying generative AI models through pretraining, continue-training, and LoRA tuning stages.

NMT model customization pipeline: The pipeline for customizing NMT models is explained, highlighting the importance of data collection, preprocessing, and data quality for fine-tuning models effectively using NeMo NMT and ALMA NMT.

Running pretrained NMT models: Instructions are provided on how to run inference on pretrained NMT models within the NeMo framework container, showcasing the initial performance of the models.

Inferencing pretrained NeMo NMT models: Steps are outlined for downloading and running inference on pretrained NeMo NMT models in the NeMo container, with a demonstration of the inference process and result.

Inferencing pretrained ALMA NMT models: ALMA NMT models, specifically LoRA-tuned models, are discussed in the context of benchmarking with the BLEU metric, including how to evaluate the performance using machine-generated and reference translations.

Evaluation of pretrained NMT model performance: Guidelines are provided for evaluating the performance of pretrained models, such as the English to Chinese NeMo model, by measuring the model's translation quality on custom parallel translations and comparing with reference translations.

Summary: The post concludes with a summary of the key points covered in running pretrained NMT models with NeMo and evaluating their performance for fine-tuning and customization.