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[ { "role": "system", "content": "You are an autonomous coding agent running on the user's machine.\n\nYou work in the current directory of a real repository. Use your tools to read, search, create, and edit files and to run shell commands (tests, builds, type-checkers). Some tools are deferred: only their na...
[{"role":"system","content":"You are an autonomous coding agent running on the user's machine.\n\nYo(...TRUNCATED)
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[{"role":"system","content":"You are an autonomous coding agent running on the user's machine.\n\nYo(...TRUNCATED)
[{"role":"system","content":"You are an autonomous coding agent running on the user's machine.\n\nYo(...TRUNCATED)
[{"role":"system","content":"You are an autonomous coding agent running on the user's machine.\n\nYo(...TRUNCATED)
[{"role":"system","content":"You are an autonomous coding agent running on the user's machine.\n\nYo(...TRUNCATED)
[{"role":"system","content":"You are an autonomous coding agent running on the user's machine.\n\nYo(...TRUNCATED)
[{"role":"system","content":"You are an autonomous coding agent running on the user's machine.\n\nYo(...TRUNCATED)
[{"role":"system","content":"You are an autonomous coding agent running on the user's machine.\n\nYo(...TRUNCATED)
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Dataset Description

This dataset contains 10,000 agentic coding and reasoning multi-turn high-quality traces generated by the new Fable 5.1 model using max reasoning effort.

It holds almost 500,000,000 tokens of step-by-step chain-of-thought programming across multiple complex domains.

It has also been deduplicated and heavily filtered to remove low-quality traces, keeping only high-quality traces.

Dataset Statistics

Metric Value
Total Examples 10,000 Traces
Total Token Count ~500,000,000 Tokens
Total Dataset Size 1.97 GB
Average Trace Size 197 KB
Average Token Count ~50,000 Tokens

Dataset Contents & Coverage

The dataset includes step-by-step problem-solving for complex coding tasks, including:

  • Algorithm design, implementation, and performance optimization.

  • Advanced debugging and error-handling.

  • Multi-step logic design and compliance with complex prompt constraints.

Uses

  • Distilling Fable 5.1 agentic coding and reasoning capabilities down to smaller LLMs.

  • Improve general coding and reasoning quality.

  • Teaching models to generate clear chain-of-thought steps and tool-use before outputting their final answer.

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Models trained or fine-tuned on MoreThought/Fable-5.1-Max-Reasoning-Filtered-10000x