Sequence Classification (SFT/PEFT) with NeMo Automodel#

Introduction#

Sequence classification tasks (e.g., sentiment analysis, topic classification, GLUE tasks) map input text to a discrete label. NeMo Automodel provides a lightweight recipe specialized for this setting that integrates with popular pretrained model formats and dataset sources. Integration with Hugging Face is supported.

This guide shows how to train a sequence classification model using the TrainFinetuneRecipeForSequenceClassification recipe, including optional Parameter-Efficient Fine-Tuning (LoRA).

Quickstart#

Use the example config for GLUE MRPC with RoBERTa-large + LoRA:

python3 examples/llm_seq_cls/seq_cls.py --config examples/llm_seq_cls/glue/mrpc_roberta_lora.yaml

  • Loads roberta-large with num_labels: 2

  • Builds GLUE MRPC datasets (train/validation)

  • Optionally, enables LoRA via the peft block

  • Trains and validates per step_scheduler

What is the Sequence Classification Recipe?#

TrainFinetuneRecipeForSequenceClassification is a config-driven trainer that orchestrates:

  • Model and optimizer construction

  • Dataset/Dataloader setup

  • Training and validation loops

  • Checkpointing and logging

It follows the same design as the SFT recipe in the fine-tune guide, but uses a standard cross-entropy classification loss and a simplified batching pipeline.

Minimal Config Anatomy#

# GLUE MRPC with RoBERTa-large + LoRA
step_scheduler:
  global_batch_size: 32
  local_batch_size: 32
  ckpt_every_steps: 200
  val_every_steps: 100
  num_epochs: 2
  max_steps: 10

dist_env:
  backend: nccl
  timeout_minutes: 1

model:
  _target_: nemo_automodel.NeMoAutoModelForSequenceClassification.from_pretrained
  pretrained_model_name_or_path: roberta-large
  num_labels: 2

checkpoint:
  enabled: true
  checkpoint_dir: checkpoints/
  model_save_format: safetensors
  save_consolidated: true

distributed:
  _target_: nemo_automodel.components.distributed.fsdp2.FSDP2Manager
  dp_size: none
  dp_replicate_size: 1
  tp_size: 1
  cp_size: 1
  sequence_parallel: false

peft:
  _target_: nemo_automodel.components._peft.lora.PeftConfig
  target_modules:
  - "*.query"
  - "*.value"
  dim: 8
  alpha: 16
  dropout: 0.1

dataset:
  _target_: nemo_automodel.components.datasets.llm.seq_cls.GLUE_MRPC
  split: train

dataloader:
  _target_: torchdata.stateful_dataloader.StatefulDataLoader
  collate_fn: nemo_automodel.components.datasets.utils.default_collater

validation_dataset:
  _target_: nemo_automodel.components.datasets.llm.seq_cls.GLUE_MRPC
  split: validation

validation_dataloader:
  _target_: torchdata.stateful_dataloader.StatefulDataLoader
  collate_fn: nemo_automodel.components.datasets.utils.default_collater

optimizer:
  _target_: torch.optim.AdamW
  betas: [0.9, 0.999]
  eps: 1e-8
  lr: 3.0e-4
  weight_decay: 0

Dataset Notes#

  • For single-sentence datasets (e.g., yelp_review_full, imdb), use YelpReviewFull or IMDB from nemo_automodel.components.datasets.llm.seq_cls.

  • For GLUE MRPC (sentence-pair classification), use GLUE_MRPC, which tokenizes (sentence1, sentence2) with padding/truncation.

LoRA (PEFT) Settings#

  • target_modules: glob to select linear layers (e.g., "*.proj").

  • dim (rank), alpha, dropout: tune per model/compute budget. Values dim=8, alpha=16, dropout=0.1 are a good starting point for RoBERTa.

  • The recipe automatically applies the adapters; no additional code changes are required.

Running with torchrun#

torchrun --nproc-per-node=2 examples/llm_seq_cls/seq_cls.py --config examples/llm_seq_cls/glue/mrpc_roberta_lora.yaml

You can adjust the number of GPUs as necessary via the --nproc-per-node knob.