---

name: generative-workflows description: "Use Simple Transformers language modeling, language generation, T5, Seq2Seq, ConvAI, and text-to-text workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Simple Transformers Generative Workflows Sub-skill

Use this sub-skill for language-model fine-tuning or training from scratch, free-form generation, T5 text-to-text tasks, generic Seq2Seq encoder/decoder models, and ConvAI chatbot-style workflows.

Owns

• LanguageModelingModel / LanguageModelingArgs / GenerationArgs.
• LanguageGenerationModel / LanguageGenerationArgs.
• T5Model / T5Args for prefix-based text-to-text tasks.
• Seq2SeqModel / Seq2SeqArgs for encoder-decoder and separate encoder/decoder setups.
• ConvAIModel / ConvAIArgs for conversational training and interaction.
• Text, CSV/DataFrame, prefix/input/target, and conversation data validation.

Route elsewhere

• Ordinary binary/multiclass/multilabel/regression classification: classification.
• Extractive QA or NER: token-and-qa.
• Dense retrieval, representations, and BEIR/MSMARCO: retrieval-representation.

Read first

1. API reference for constructors and methods.
2. Data formats before preparing LM/T5/Seq2Seq/ConvAI inputs.
3. Workflows for CPU-safe recipes and constructor choices.
4. Troubleshooting for model downloads, text-to-text prefixes, tokenizer/from-scratch settings, and compatibility import issues.

Validation helper

python scripts/validate_generative_data.py --task lm-text --input train.txt
python scripts/validate_generative_data.py --task t5-csv --input t5.csv
python scripts/validate_generative_data.py --task seq2seq-csv --input pairs.csv
python scripts/validate_generative_data.py --task t5-predict-lines --input predict.txt
python scripts/validate_generative_data.py --task convai-json --input convai.json

The helper checks data shape only. It does not download models, train, generate, or open network connections.

Key decisions

• Language modeling: pretrained fine-tuning needs model_name; training from scratch needs model_name=None, train_files, and vocab_size/tokenizer settings.
• T5: training/eval DataFrames use prefix, input_text, target_text; prediction strings must already include "prefix: ".
• Seq2Seq: either pass encoder_decoder_type/encoder_decoder_name for unified models like BART, or pass separate encoder_type, encoder_name, and decoder_name.
• Generation: keep max_length, max_new_tokens, do_sample, top_k, top_p, temperature, and num_return_sequences explicit.
• ConvAI: expect additional compatibility and data-shape risk because upstream utility imports use old Transformers cache helpers.

Verification status

Constructor signatures were inspected for Simple Transformers 0.70.8. Native LM/Seq2Seq/T5 examples train or download models and are skipped by default; use validators and small cached-model smoke tests unless the user approves larger runs.