opus-distilled
Claude-Opus-4.6-stance-distilled-RELATIONALCreated: 2026-03-11
Target: 1000 training examples for QLoRA fine-tuning
Format: OpenAI chat format (system/user/assistant), <think> reasoning traces
Most people create AI to do science problems. I'm creating an AI (Eva) that can effectively navigate life, which is more about relating with people and day-to-day reasoning.
This is the first batch of relating data I distilled from Claude Opus 4.6 oriented with a specific stance, which produces measurably better quality outputs than an unoriented… See the full description on the dataset page: https://huggingface.co/datasets/aptgetupdate/Claude-Opus-4.6-stance-distilled-RELATIONAL.claude_Opus_4.7_Distilledopus-4.7-distilled-29kOpus-Qwen-Reasoning-Distilled-V1Opus-Qwen-Reasoning-Mix-V1
Cleaned Datasets:
nohurry/Opus-4.6-Reasoning-3000x-filtered
TeichAI/claude-4.5-opus-high-reasoning-250x
Jackrong/Qwen3.5-reasoning-700x
Difficulty:
Medium: 2,261 (70.4%)
Medium/Hard: 633 (19.7%) — Qwen3.5-27B
Hard: 278 (8.7%)
PhD: 37 (1.2%)
Category:
Math: 2,421 (75.4%)
Complex Reasoning: 250 (7.8%) — Claude-4.5-Opus
Code: 232 (7.2%)
Science: 166 (5.2%)
Instruction Following: 140 (4.4%)
Format: {messages: [{role, content, reasoning_content}]}
debate-opus-distilled-group-a