InternScience/GraphGen

Health v2 · maintenance only

Adoption not part of the score

1212 stars · 97 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

GraphGen is a Python framework for knowledge-graph-guided synthetic data generation for LLM training. It builds fine-grained knowledge graphs from source text, identifies knowledge gaps via expected calibration error, and generates targeted QA pairs for supervised fine-tuning.

Use cases

• generate synthetic sft training data for llms
• build qa pairs from documents using knowledge graphs
• create fine-tuning datasets for domain-specific knowledge
• synthesize long-tail knowledge data for model training
• generate evaluation datasets for llm benchmarks
• extract knowledge graphs from text corpora

When to choose

• you need synthetic instruction or QA data to fine-tune an LLM
• domain-specific or long-tail knowledge is scarce in your training corpus
• you want calibration-aware prioritization of what data to generate
• you plan to train with LLaMA-Factory or xtuner

When to avoid

• you need general-purpose data labeling or annotation tools
• you lack access to an LLM to drive the generation pipeline
• you want simple paraphrase-style augmentation without knowledge graph overhead

Facets

framework · maturity active

data-generation rag llm-training nlp machine-learning large-language-models machine-learning python cli cross-platform synthetic-data knowledge-graph sft qa-pairs fine-tuning data-synthesis llm data-engineering natural-language-processing

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markdown · JSON · MCP: product_card(name="InternScience/GraphGen")