class TextClassificationModel(NLPModel, Exportable): ... def __init__(self, cfg: DictConfig, trainer: Trainer = None): """Initializes the BERTTextClassifier model.""" ... super().__init__(cfg=cfg, trainer=traine
Large Language Models & Multimodal (LLM & MM) Speech AI Data Augmentation Speech Data Explorer Using Kaldi Formatted Data Speech Command Recognition General Optimizations Mixed Precision Training Multi-GPU Training NeMo, PyTorch Lightning, and Hydra Optimized Pretrained Models Resources Quickstart Getting St...
An example script on how to train and evaluate the model can be found at:NeMo/examples/nlp/token_classification/punctuation_capitalization_train_evaluate.py. The default configuration file for the model can be found at:NeMo/examples/nlp/token_classification/conf/punctuation_capitalization_config.y...
NeMo Run. We are currently porting all features from NeMo 1.0 to 2.0. For documentation on previous versions or features not yet available in 2.0, please refer to theNeMo 24.07 documentation.
import nemo_run as run from nemo.collections import llm def configure_recipe(nodes: int = 1, gpus_per_node: int = 2): recipe = llm.nemotron3_4b.pretrain_recipe( dir="/checkpoints/nemotron", # Path to store checkpoints name="nemotron_pretraining", tensor_parallelism=2, num_nodes=no...
NeMo Speech Intent Classification and Slot Filling Configuration Files NeMo Speech Intent Classification and Slot Filling collection API Resources and Documentation SpeechLM2 Models Datasets Configuration Files Training and Scaling Text-to-Speech (TTS) Models Data Preprocessing Checkpoints NeMo TTS Configurat...
python_classification_responses = generator.classify_python_entity( entity="Recipes for blueberry pie", model=model, ) print(python_classification_responses[0]) # Output: # No ... Generate Asynchronously All of the code so far has been sending requests to the LLM service synchronously. This can...
importnemo_runasrunfromnemo.collectionsimportllmdefconfigure_recipe(nodes:int=1,gpus_per_node:int=2):recipe=llm.nemotron3_4b.pretrain_recipe(dir="/checkpoints/nemotron",# Path to store checkpointsname="nemotron_pretraining",tensor_parallelism=2,num_nodes=nodes,num_gpus_per_node=gpus_per_node...
nemo_client=NemoQueryLLM(url="localhost:8000",model_name=model). This initialization requires you to specify the model’s name. NemoQueryLLM is primarily built for querying a single LLM, but NeMo Curator allows you to change the model you are querying on your local server for each request....
pythonexamples/nlp/token_classification/data/create_punctuation_capitalization_tarred_dataset.py\--text<PATH/TO/LOWERCASED/TEXT/WITHOUT/PUNCTUATION>\--labels<PATH/TO/LABELS/IN/NEMO/FORMAT>\--output_dir<PATH/TO/DIRECTORY/WITH/OUTPUT/TARRED/DATASET>\--num_batches_per_tarfile100 ...