TokenSmith: Streamlining Data Editing, Search, and Inspection for Large-Scale Language Model Training and Interpretability
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Abstract (arXiv)
Understanding the relationship between training data and model behavior during pretraining is crucial, but existing workflows make this process cumbersome, fragmented, and often inaccessible to researchers. We present TokenSmith, an open-source library for interactive editing, inspection, and analysis of datasets used in Megatron-style pretraining frameworks such as GPT-NeoX, Megatron, and NVIDIA NeMo. TokenSmith supports a wide range of operations including searching, viewing, ingesting, exporting, inspecting, and sampling data, all accessible through a simple user interface and a modular backend. It also enables structured editing of pretraining data without requiring changes to training code, simplifying dataset debugging, validation, and experimentation. TokenSmith is designed as a plug-and-play addition to existing large language model pretraining workflows, thereby democratizing access to production-grade dataset tooling. TokenSmith is hosted on GitHub, with accompanying documentation, tutorials, and a demonstration video (available on YouTube).
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References · 13
- 2019Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelismcited by 1,868
- 2023Pythia: A Suite for Analyzing Large Language Models Across Training and Scalingcited by 1,786
- 2023ProofNet: Autoformalizing and Formally Proving Undergraduate-Level Mathematicscited by 192
- 2024How to Train Long-Context Language Models (Effectively)cited by 146
- 2023Continual Pre-Training of Large Language Models: How to (re)warm your model?cited by 137
- 2022Measuring Forgetting of Memorized Training Examplescited by 118
- 2024How Do Large Language Models Acquire Factual Knowledge During Pretraining?cited by 90
- 2022Dissecting Transformer Length Extrapolation via the Lens of Receptive Field Analysiscited by 42
- 2024Proving membership in LLM pretraining data via data watermarkscited by 40
- 2025Metadata Conditioning Accelerates Language Model Pre-trainingcited by 22
- 2024Turn Waste into Worth: Rectifying Top-k Router of MoEcited by 11
- 2024Seq1F1B: Efficient Sequence-Level Pipeline Parallelism for Large Language Model Trainingcited by 11
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