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:

$
uv run automodel 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

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# GLUE MRPC with RoBERTa-large + LoRA
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step_scheduler:
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  global_batch_size: 32
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  local_batch_size: 32
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  ckpt_every_steps: 200
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  val_every_steps: 100
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  num_epochs: 2
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  max_steps: 10
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dist_env:
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  backend: nccl
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  timeout_minutes: 1
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model:
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  _target_: nemo_automodel.NeMoAutoModelForSequenceClassification.from_pretrained
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  pretrained_model_name_or_path: roberta-large
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  num_labels: 2
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checkpoint:
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  enabled: true
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  checkpoint_dir: checkpoints/
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  model_save_format: safetensors
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  save_consolidated: final
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distributed:
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  strategy: fsdp2
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  dp_size: null
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  dp_replicate_size: null
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  tp_size: 1
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  cp_size: 1
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  sequence_parallel: false
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peft:
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  _target_: nemo_automodel.components._peft.lora.PeftConfig
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  target_modules:
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  - "*.query"
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  - "*.value"
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  dim: 8
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  alpha: 16
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  dropout: 0.1
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dataset:
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  _target_: nemo_automodel.components.datasets.llm.seq_cls.GLUE_MRPC
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  split: train
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dataloader:
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  _target_: torchdata.stateful_dataloader.StatefulDataLoader
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  collate_fn: nemo_automodel.components.datasets.utils.default_collater
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validation_dataset:
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  _target_: nemo_automodel.components.datasets.llm.seq_cls.GLUE_MRPC
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  split: validation
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validation_dataloader:
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  _target_: torchdata.stateful_dataloader.StatefulDataLoader
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  collate_fn: nemo_automodel.components.datasets.utils.default_collater
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optimizer:
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  _target_: torch.optim.AdamW
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  betas: [0.9, 0.999]
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  eps: 1e-8
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  lr: 3.0e-4
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  weight_decay: 0

Dataset Notes

• nemo_automodel.components.datasets.llm.seq_cls provides GLUE_MRPC, the implemented sentence-pair adapter. It tokenizes (sentence1, sentence2) with truncation; default_collater pads each batch.
• For other datasets, including single-sentence datasets such as yelp_review_full or imdb, provide a custom dataset class or factory through _target_. Accept a tokenizer argument so the recipe can inject the model tokenizer, and return input_ids, attention_mask, a one-element labels list, and the ___PAD_TOKEN_IDS___ mapping used by default_collater for each sample. Use GLUE_MRPC as the adapter pattern.

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 on Multiple GPUs

$
uv run automodel examples/llm_seq_cls/glue/mrpc_roberta_lora.yaml --nproc-per-node 2

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