| --- |
| license: apache-2.0 |
| task_categories: |
| - text-generation |
| - question-answering |
| language: |
| - en |
| pretty_name: Fable 5.1's Style |
| tags: |
| - fable 5.1 |
| - coding |
| - synthetic |
| - thinking |
| - think |
| - reason |
| - reasoning |
| - distill |
| - distillation |
| - agent |
| - agentic |
| - SFT |
| - CoT |
| - code |
| - programming |
| - thought |
| - thoughts |
| - SWE |
| - tool-use |
| - mythos 5.1 |
| - mythos |
| - fable class |
| - mythos class |
| - fable 5 |
| - mythos 5 |
| size_categories: |
| - 100K<n<1M |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: train.jsonl |
| --- |
| ## Dataset Description |
|
|
| This dataset contains 10,000 agentic coding and reasoning multi-turn traces took from the Step 3.5 Flash SFT Code dataset. |
|
|
| It was remade to sound very similar to the Fable 5.1 model on max reasoning effort in Fable-5.1-Max-Reasoning-Filtered-10000x. |
|
|
| It holds over 2,000,000,000 tokens of step-by-step chain-of-thought programming across multiple complex coding domains. |
|
|
| It has also been deduplicated and filtered to remove no-reasoning and lower-quality traces, keeping only high-quality slow reasoning traces. |
|
|
| ## Dataset Statistics |
|
|
| | Metric | Value | |
| | :--- | :--- | |
| | **Total Examples** | 473,635 Traces | |
| | **Total Token Count** | ~2,000,000,000 Tokens | |
| | **Total Dataset Size** | 6.8 GB | |
| | **Average Trace Size** | 14.3 KB | |
| | **Average Token Count** | ~4,500 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-style agentic coding and reasoning 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. |
