Claude Mythos Distilled 25K: Synthetic SFT Dataset for LLM Training

A synthetic supervised fine-tuning dataset of 25,000 high-quality examples designed to train language models. It was created by ansulev and last updated on Hugging Face in May 2026. The dataset aims to distill the capabilities and reasoning style of a frontier AI model into other LLMs.

Use Cases

• Fine-tuning language models for cybersecurity tasks based on the described category.
• Training models for agentic behavior and technical depth as mentioned in the description.
• Creating instruction-following models using the described user/assistant chat message pairs.

Strengths

• Contains 25,000 high-quality synthetic examples.
• Examples are categorized and balanced for general and specialized capabilities.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Data may reflect bias inherent to its synthetic generation process.

Provenance

Source

huggingface

Collection Method

Synthetically generated for supervised fine-tuning.

Freshness

Last updated 2026-05-31 18:10:10; freshness should be verified.