codelion/sutra-10B

Sutra 10B Pretraining Dataset

A high-quality pedagogical dataset designed for LLM pretraining, containing 10,193,029 educational entries totaling over 10 billion tokens. This is the largest dataset in the Sutra series, designed to demonstrate that dense, curated datasets can provide best-in-class pretraining performance for small language models.

Dataset Description

This dataset was generated using the Sutra framework, which creates structured educational content optimized for language model pretraining. Each entry is designed to maximize learning efficiency through:

• Clear pedagogical structure: Content follows proven educational patterns
• Cross-domain connections: Concepts are linked across disciplines
• Varied complexity levels: From foundational (level 1) to advanced (level 10)
• Quality-controlled generation: All entries meet minimum quality thresholds
• Diverse content types: 33 different pedagogical formats
• Rich metadata: Every entry annotated with 13 structured fields

Dataset Statistics

Metric	Value
Total Entries	10,193,029
Total Tokens	10,218,677,925
Avg Tokens/Entry	1002
Avg Quality Score	0.701
Tokenizer	SmolLM2 (HuggingFaceTB/SmolLM2-135M)

Domain Distribution

Domain	Entries	Tokens	Percentage
interdisciplinary	3,561,052	3570.0M	34.9%
technology	2,154,481	2159.9M	21.1%
science	1,456,708	1460.3M	14.3%
social_studies	862,288	864.4M	8.5%
mathematics	830,414	832.5M	8.1%
life_skills	559,667	561.1M	5.5%
arts_and_creativity	455,738	456.9M	4.5%
language_arts	235,957	236.5M	2.3%
philosophy_and_ethics	76,724	76.9M	0.8%

Content Type Distribution (Top 15)

Content Type	Count	Percentage
historical_context	3,082,957	30.2%
concept_introduction	928,244	9.1%
data_analysis	776,495	7.6%
worked_examples	697,861	6.8%
problem_set	676,977	6.6%
tutorial	620,163	6.1%
technical_documentation	520,246	5.1%
research_summary	494,023	4.8%
code_implementation	473,056	4.6%
practical_application	438,157	4.3%
creative_writing	337,065	3.3%
reasoning_demonstration	227,343	2.2%
qa_pairs	200,076	2.0%
ethical_analysis	157,882	1.5%
experiment_design	141,859	1.4%

Data Sources

Sutra-10B was created by scaling the same recipe used for Sutra-1B from 1 billion to 10 billion tokens. The core pedagogical content was generated using the Sutra framework, then mixed with several high-quality open datasets for diversity:

Source	Description	Approximate Tokens
Sutra (core)	Pedagogical content generated with the Sutra framework, scaled from the 1B recipe	~7.8B
Nemotron-CC-Math v1	High-quality mathematical content (NVIDIA)	~0.5B
OpenWebMath	Mathematical web content	~0.5B
Wikipedia (English)	Encyclopedic knowledge	~0.5B
Cosmopedia	Synthetic educational content (multiple subsets)	~0.5B
FineWeb-Edu	High-quality educational web content	~0.5B

Data Fields

Each entry contains 13 structured fields:

Field	Type	Description
id	string	Unique identifier (UUID)
concept_name	string	The concept being taught (2-5 words)
domain	string	Primary knowledge domain (9 domains)
content_type	string	Type of pedagogical content (33 types)
text	string	The main educational content
quality_score	float	Quality assessment score (0.0-1.0)
information_density	string	Measure of information per token (low/medium/high)
complexity_level	integer	Difficulty level (1-10)
token_count	integer	Number of tokens (SmolLM2 tokenizer)
prerequisites	list[string]	Required prior knowledge concepts
builds_to	list[string]	Advanced concepts this enables
cross_domain_connections	list[string]	Related knowledge domains
quality_assessment	object	Multi-dimensional quality scores

Quality Assessment Sub-fields

Sub-field	Type	Description
clarity	float	How clear and readable (0.0-1.0)
accuracy	float	Factual correctness (0.0-1.0)
pedagogy	float	Educational structure quality (0.0-1.0)
engagement	float	How engaging the content is (0.0-1.0)
depth	float	Depth of coverage (0.0-1.0)
creativity	float	Creative presentation (0.0-1.0)

Valid Domains (9)

mathematics, science, technology, language_arts, social_studies, arts_and_creativity, life_skills, philosophy_and_ethics, interdisciplinary

Valid Content Types (33)

concept_introduction, reasoning_demonstration, code_implementation, technical_documentation, tutorial, cross_domain_bridge, worked_examples, qa_pairs, common_misconceptions, meta_learning, synthesis, prerequisite_scaffolding, code_explanation, diagnostic_assessment, code_debugging, historical_context, research_summary, problem_set, case_study, analogy, experiment_design, proof, algorithm_analysis, data_analysis, ethical_analysis, comparative_analysis, creative_writing, debate_argument, practical_application, thought_experiment, visualization, system_design, review_summary

Data Cleaning

The dataset underwent comprehensive cleaning:

• Deduplication: SHA-256 hash-based exact duplicate removal across all sources
• Quality Filtering: Entries below quality_score 0.3 removed
• Length Filtering: Entries shorter than 50 tokens or longer than 65,536 tokens removed
• Garbage Detection: Repetitive content, control characters, non-English content filtered
• Field Validation: All 13 fields validated and normalized

Metadata Generation

Metadata was generated using heuristic keyword-based classification:

• Domain and content type classification via pattern matching and text analysis
• Quality scores computed from text statistics (vocabulary diversity, structure, length)
• Token counts computed using SmolLM2 tokenizer for accuracy

Usage

from datasets import load_dataset

# Load the full dataset
ds = load_dataset("codelion/sutra-10B", split="train")

# Stream for large-scale training
ds = load_dataset("codelion/sutra-10B", split="train", streaming=True)

# Filter by domain
math_ds = ds.filter(lambda x: x["domain"] == "mathematics")

# Filter by quality
high_quality = ds.filter(lambda x: x["quality_score"] > 0.7)

# Filter by complexity
beginner = ds.filter(lambda x: x["complexity_level"] <= 3)

Scaling Trajectory

Sutra-10B is the largest dataset in the Sutra series, scaling the original 1B recipe by 10x. When evaluated on SmolLM2-70M (69M parameters), benchmark performance remains consistent across scales, suggesting the model has reached its capacity ceiling. Larger models are expected to benefit more from the additional data and diversity.

Intended Use

This dataset is designed for:

• LLM Pretraining: High-quality educational content for foundational model training
• Domain-specific fine-tuning: Subset by domain for specialized training
• Educational AI research: Studying pedagogical content generation
• Curriculum learning: Progressive complexity for staged training
• Small model optimization: Demonstrating data quality > quantity for small LMs

Related Datasets

• sutra-1B: 1B token pretraining dataset
• sutra-100M: 100M token subset
• sutra-30k-seeds: Instruction prompts for post-training
• sutra-magpie-sft: SFT dataset

Citation

@article{sharma2026sutra,
  title={Scaling Pedagogical Pretraining: From Optimal Mixing to 10 Billion Tokens},
  author={Sharma, Asankhaya},
  year={2026},
  url={https://huggingface.co/blog/codelion/scaling-pedagogical-pretraining-10-billion-tokens}
}

License

Apache 2.0