11-47/claude_opus_4.8_max_thinking_5k_v2

Claude Opus 4.8 MAX THINKING — Distillation Dataset

5,000 high-quality examples designed to distill the maximum-effort reasoning, honest analysis, production software engineering, and agentic capabilities of Claude Opus 4.8.

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Overview

This dataset captures Opus 4.8’s signature strengths:

• Deep, structured, high-effort reasoning
• Honest communication about trade-offs and uncertainties
• Excellent production software engineering judgment
• Strong agentic workflow design
• Clear, actionable technical strategy

It is intended for fine-tuning open models to behave more like Opus 4.8 in professional software engineering and complex reasoning tasks.

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Dataset Details

• File: opus_4_8_max_thinking_5k_v2.jsonl
• Size: 5,000 examples
• Format: Chat messages with visible <think> reasoning
• Difficulty: Expert / Professional level

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Format

{
  "messages": [
    {
      "role": "system",
      "content": "You are Claude Opus 4.8 at maximum thinking depth..."
    },
    {
      "role": "user",
      "content": "Real-world professional engineering or strategy prompt..."
    },
    {
      "role": "assistant",
      "content": "<think>\nVisible high-quality chain-of-thought...\n</think>\n\n**Structured professional response...**"
    }
  ],
  "metadata": {
    "model_focus": "claude_opus_4_8_max_thinking",
    "category": "Production Software Engineering" | "Agentic Systems" | "Technical Strategy" | ...,
    "difficulty": "expert",
    "example_id": "...",
    "generated_at": "..."
  }
}

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Categories

• Production Software Engineering & Refactoring
• Agentic Workflow Design & Automation
• Complex Technical Strategy & Decision Making
• Code Quality, Testing & Observability
• Enterprise Systems & Scalability

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Key Features

• True <think> tags on every response — visible, high-quality reasoning process
• Highly varied, realistic professional prompts
• Concrete, actionable responses with clear structure
• Honest handling of trade-offs and uncertainties
• Strong focus on developer experience and production realities
• No repetitive placeholders or low-quality filler

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Intended Use

This dataset is designed for:

• Supervised fine-tuning (SFT) to improve reasoning depth and software engineering capability
• Distilling Opus 4.8-style structured thinking into open-weight models
• Research on capability transfer from frontier models
• Building models strong in complex technical decision-making and agentic workflows

Recommended for teams and researchers who want models that think carefully, communicate honestly, and perform well on real engineering and strategy tasks.

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How to Load

from datasets import load_dataset

dataset = load_dataset("json", data_files="opus_4_8_max_thinking_5k_v2.jsonl")

Works well with Axolotl, TRL, Unsloth, and Llama-Factory.

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Quality Notes

This is a synthetic dataset created to emulate the professional output style and reasoning depth of Claude Opus 4.8. It is real model outputs.

Every example was generated with care to ensure:

• High diversity
• Professional tone and depth
• Visible, non-trivial reasoning
• Practical usefulness for training

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License & Responsibility

This dataset is provided for research and educational purposes.

Users are responsible for ensuring their fine-tuned models comply with applicable laws and ethical guidelines.

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Generated June 2026 — Focused on maximum thinking depth and professional software engineering excellence.