Parallelogram is an open-source command-line tool designed to validate and diagnose fine-tuning datasets for large language model training. It addresses the risk of subtle data issues—such as broken role sequences, empty messages, duplicates, encoding artifacts, and context-window overflows—that can silently compromise model quality if left undetected before training. The tool specifically supports OpenAI/Qwen chat JSONL and ShareGPT-style datasets, normalizing these formats internally to ensure consistent diagnostics across different data sources.
The tool operates locally and is distributed via PyPI, allowing users to install and run it as a CLI without uploading data or requiring an SDK. Parallelogram integrates with existing machine learning pipelines by emitting exit codes suitable for use in continuous integration workflows. Its checks are tailored for conversational fine-tuning data, going beyond generic JSON validation to catch issues like role alternation errors, exact duplicate records, invalid JSONL, mojibake or encoding artifacts, context-window overflows, format drift between supported dataset types, and conversations ending on a user message. For each detected issue, Parallelogram provides diagnostics, safe-fix notes, and output reasons, with the ability to repair certain mechanical problems and re-run checks for verification.
A browser-based demo is also available, running entirely client-side and mirroring the CLI's diagnostic capabilities, though with estimated token counts rather than exact tokenizer integration. The tool supports both tiktoken and Hugging Face tokenizers for precise context-window analysis when used in the CLI.
Parallelogram is open source, with its source code available on GitHub, and is positioned to work with a variety of fine-tuning stacks without vendor lock-in. Its primary audience includes machine learning practitioners and engineers seeking to ensure dataset integrity and prevent silent training failures due to data quality issues.
parallelogram is a LLM eval & observability product. It focuses on ensuring the quality and correctness of datasets before fine-tuning machine learning models. It is built as an open-source project for machine learning engineers and data scientists. parallelogram is open source under the Apache-2.0 license. The product ships for the web and the command line.
parallelogram first shipped in 2026. Key capabilities include dataset validation, fine-tuning support, and strict schema checks.
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