Claude Opus 4.8 Training Data Package | Flagship Distilled Corpus · Agentic / Reasoning / Code

Product Overview

This is a full-capability LLM training corpus distilled from the Claude Opus 4.8 flagship model. Through large-scale instruction generation, Chain-of-Thought distillation, agentic trajectory capture, and execution-feedback loops, it systematically extracts and refines Opus 4.8's core strengths in agentic coding, deep reasoning, tool use, and long-horizon task planning from the model's outputs — helping you rapidly align an open base model to flagship-level performance.

Self-distillation would cost approximately ¥600,000+; this package saves you about 85% of that cost and 2-4 months of time.

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Dataset Specifications

• Total volume: ~3.2M+ high-quality instruction-response pairs
• Token scale: ~10B+ tokens (including full reasoning chains, tool-call trajectories, and final responses)
• Capability dimensions: agentic · deep reasoning · code engineering · tool/function calling · long-context understanding · structured writing
• Source model: Claude Opus 4.8 (Anthropic's current flagship; industry-leading on agentic coding and real-world software-engineering tasks)
• Format: JSONL / Parquet (system/instruction, full reasoning, tool_calls, final_response, quality-score fields)
• Distillation methods: Self-Instruct + Evol-Instruct + Chain-of-Thought distillation + agentic trajectory replay + execution-feedback filtering

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Why Claude Opus 4.8 Distillation Data

Claude Opus 4.8 is one of Anthropic's strongest current flagships, with genuine industry-leading capability in the areas below. Distillation data lets your open base model "inherit" these capabilities:

Note: the above is a qualitative comparison to illustrate the value of distillation. It does not represent any specific benchmark score, nor any quantitative claim about third-party models.

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Content Coverage

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Distillation Method & Quality Assurance

• Self-Instruct / Evol-Instruct: autonomously expand seed instructions and raise complexity over multiple rounds for task diversity
• Chain-of-Thought distillation: retains full reasoning chains (understand → plan → execute → self-check), not simple prompt-completion pairs
• Agentic trajectory capture: records multi-step tool-call trajectories with intermediate observations, tool returns and corrections
• Execution-feedback filtering: code/tool samples are verified by real compilation/execution; only runnable samples are kept (~35% discard rate)
• LLM quality scoring: each sample carries a 1-10 quality score for filtering high-quality subsets
• Multi-stage dedup: exact → MinHash fuzzy → code-AST structural deduplication
• Benchmark decontamination: filtered against leakage from SWE-bench, HumanEval, MBPP, GSM8K, MMLU and other major suites

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Cost Comparison

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Use Cases

• Open base fine-tuning: inject flagship-level agentic and reasoning ability into 7B / 13B / 32B / 70B open models (Llama, Qwen, DeepSeek, Mistral)
• AI coding / agent training: agentic trajectories train coding and task agents with multi-step tool-calling ability
• Private enterprise assistants: high-quality base training data for self-hosted enterprise assistants
• RAG / long-document scenarios: improve long-context understanding and retrieval-augmented QA quality
• Tool-calling agents: structured function-calling data trains reliable tool-orchestration ability

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Delivery

• Data files: JSONL / Parquet, with a field-schema document and usage guide
• Delivery time: within 1-3 business days after payment
• Updates: optional incremental updates (new model-version distillation / domain-specific additions)
• Support: basic fine-tuning guidance (recommended hyperparameters, data-mix ratios, training workflow)
• Compliance: for lawful research and commercial use only; contains no personal private information

For customization (specific domain / language / scale) or enterprise cooperation, please contact us through official channels.