Claude Opus Mythos 5K: Synthetic SFT Data for Software Engineering and Cybersecurity

High-quality synthetic supervised fine-tuning datasets designed to distill the capabilities of Anthropic's Claude Opus 4.8 and Claude Mythos Preview. The dataset is intended for training open-weight models to approximate frontier-level performance in software engineering, agentic workflows, complex reasoning, and defensive cybersecurity. It was created by author 11-47 and last updated on 2026-06-06.

Use Cases

• Training models for software engineering tasks based on the described distillation of Claude Opus's capabilities.
• Developing agentic workflow systems based on the described reasoning style and professional behavior.
• Enhancing models for complex reasoning tasks based on the described high-quality synthetic SFT data.
• Building defensive cybersecurity tools based on the capabilities distilled from the Claude Mythos Preview set.

Strengths

• Designed as high-quality synthetic data for supervised fine-tuning.
• Aims to distill capabilities from frontier models Claude Opus 4.8 and Claude Mythos Preview.

Limitations

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

Provenance

Source

huggingface

Collection Method

Synthetic generation designed to distill model capabilities.

Freshness

Last updated 2026-06-06 00:05:17; freshness should be verified.