Claude Opus reasoning distillation dataset
For reasoning, writing and knowledge tasks. Dataset volume, languages, cleaning methods and permitted use remain subject to the current listing.
Product Details
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.
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
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:
| Capability | Claude Opus 4.8 (source) | Generic open base (pre-fine-tune) |
|---|---|---|
| Agentic coding | Industry-leading | Weak |
| Real software engineering (SWE-style) | Industry-leading | Moderate |
| Deep / long-horizon reasoning | Leading | Moderate |
| Tool use & multi-step orchestration | Leading | Weak |
| Long-context stability | Leading | Moderate |
| Instruction-following / low hallucination | Leading | Moderate |
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.
Content Coverage
| # | Capability type | Description | Volume |
|---|---|---|---|
| 1 | Agentic task chains | Multi-step trajectories: analyze → plan → tool-call → execute → self-check, with intermediate observations and tool feedback | ~700K |
| 2 | Code engineering | Generation / debugging / refactoring / review / test generation across 20+ languages | ~800K |
| 3 | Deep reasoning & math | Multi-step reasoning, proofs, complex problem decomposition with full CoT | ~550K |
| 4 | Tool / function calling | Structured JSON calls, multi-tool orchestration, argument validation and error recovery | ~400K |
| 5 | Long-context & RAG | Long-document QA, retrieval-augmented reasoning, cross-document synthesis and citation | ~300K |
| 6 | Structured writing & summarization | Technical docs, reports, multi-style rewriting and summarization | ~250K |
| 7 | Data analysis & SQL | NL-to-SQL, data insights, chart description | ~120K |
| 8 | Multi-turn dialogue & instruction following | Complex system-instruction adherence, multi-turn context retention | ~80K |
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
Cost Comparison
| Option | Estimated cost | Time | Notes |
|---|---|---|---|
| Self-distillation | ~¥600,000+ | 2-4 months | Heavy flagship-model API spend + execution environment + manual QA |
| This package | ¥99,000 | Instant delivery | Finished dataset — distillation, execution verification, dedup and QA all done |
| Savings | ~85% cost saved, 2-4 months saved |
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
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.
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