Hunter Alpha Reasoning Traces for Instruction Tuning

308,000 reasoning traces were distilled from the Hunter Alpha model via the OpenRouter platform. The dataset contains 1.2 billion tokens and was created by user 'ianncity', with a last recorded update in March 2026. It is composed of 30% math problems, 30% coding tasks, 15% science topics, 15% computer science, and 10% creative writing.

Use Cases

• Fine-tune language models on algebra and calculus problems using the 'Math' category traces.
• Train models for code generation in languages like C++, Java, and Rust via the 'Coding' subset.
• Improve scientific reasoning by training on physics, chemistry, and biology traces from the 'Science' portion.
• Enhance creative writing capabilities using the 10% of traces dedicated to that domain.

Strengths

• 308,000 individual reasoning traces for model training
• 1.2 billion total tokens provide substantial text volume
• Clear category distribution percentages (e.g., 30% Math, 30% Coding)

Limitations

• Unknown column structure and specific features per trace
• Potential bias towards topics and languages represented in the listed categories
• Source model 'Hunter Alpha' may have inherent limitations or biases transferred to the traces

Provenance

Source

Distilled from the Hunter Alpha model on the OpenRouter platform.

Collection Method

Reasoning traces were collected and distilled from model interactions.

Time Range

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Freshness

Last updated March 2026; update frequency is not specified but intended to continue until model removal.

Geography

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