Synthetic Math Test: Chain-of-Thought Reasoning Data Generated via Llama.cpp

A synthetic dataset generated on 2026-05-31 by author kskkoba using the Chain-of-Thought Self-Instruct methodology. It contains reasoning data produced by the unsloth/Qwen3-30B-A3B-Thinking-2507-GGUF model, served locally via llama.cpp. The source data is derived from the gretelai/gretel-math-gsm8k-v1 dataset.

Use Cases

• Fine-tuning language models for mathematical reasoning based on the synthetic Chain-of-Thought examples.
• Benchmarking model performance on reasoning tasks using the generated data.
• Studying the Chain-of-Thought Self-Instruct methodology and its synthetic data outputs.
• Augmenting existing math problem datasets with additional synthetic reasoning traces.

Strengths

• Generated using a specific 30-billion-parameter language model (unsloth/Qwen3-30B-A3B-Thinking-2507-GGUF) for reasoning tasks.
• Created via the Chain-of-Thought Self-Instruct methodology, which is designed to produce step-by-step reasoning data.
• Derived from the established gretel-math-gsm8k-v1 source dataset for mathematical problems.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

huggingface

Collection Method

Synthetic generation via Chain-of-Thought Self-Instruct using llama.cpp and the unsloth/Qwen3-30B-A3B-Thinking-2507-GGUF model.

Freshness

Last updated 2026-05-31 13:39:22; freshness should be verified.