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Claude Code Distillation Data Package
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US$99,000.00US$99,000.00-0%
Stock:In Stock
Subtotal:US$99,000.00
Total:US$99,000.00
Product Details
Product Overview
This product is a Claude Opus / Sonnet distilled Code LLM Training Data Package, extracted and refined through large-scale instruction generation, chain-of-thought distillation, self-evolution, and execution feedback loops from the Claude model series, capturing its core code generation, debugging, refactoring, system design, and agentic coding capabilities.
Self-distillation cost: ~¥700,000+. This data package saves you 85%+ of distillation costs.
- Total Data: ~5M+ high-quality code instruction-response pairs
- Token Scale: ~15 billion tokens (including complete reasoning processes, code + explanations)
- Programming Languages: 20+ mainstream languages (Python, TypeScript, Java, Rust, Go, C++, Swift, Kotlin, etc.)
- Data Format: JSONL / Parquet (with instruction, code, test cases, execution feedback, quality scores)
- Source Model: Claude Opus 4.x / Sonnet 4.x series (SWE-bench 80.8%, HumanEval 97.3%, MBPP 96.1%)
- Distillation Methods: Self-Instruct + Evol-Instruct + Execution Feedback Loop + CoT Distillation
Why Claude Distilled Data?
| Benchmark | Claude Opus 4.6 | GPT-4.1 | Gemini 2.5 Pro |
|---|---|---|---|
| SWE-bench Verified | 80.8% (#1) | 54.6% | 63.8% |
| HumanEval | 97.3% | 95.4% | 96.2% |
| MBPP | 96.1% (#1) | 91.8% | 93.5% |
| Agentic Coding | Industry Leading | Medium | Strong |
Data Content Coverage
| # | Code Task Type | Description | Data Scale |
|---|---|---|---|
| 1 | Code Generation | Complete runnable code from natural language descriptions | ~1.2M entries |
| 2 | Algorithms & Data Structures | Classic algorithms with complexity analysis and multiple solutions | ~800K entries |
| 3 | Debugging & Fixing | Bug localization, root cause analysis, fix proposals, regression tests | ~600K entries |
| 4 | Code Refactoring | Design patterns, performance optimization, architecture upgrades, multi-file refactoring | ~500K entries |
| 5 | System Design | Microservices, database design, distributed systems, DevOps pipelines | ~400K entries |
| 6 | Agentic Coding | Multi-step task chains: requirements → design → implementation → testing → deployment | ~450K entries |
| 7 | Code Review | Quality assessment, security audit, performance analysis, PR reviews | ~350K entries |
| 8 | Test Generation | Unit tests, E2E tests, edge cases, TDD workflows | ~350K entries |
| 9 | Cross-Language Migration | Python↔Java, JS↔TS, Python↔Rust, framework migrations | ~200K entries |
| 10 | Documentation Generation | API docs, README, ADR, Changelog generation | ~150K entries |
Distillation Methods & Quality Assurance
- Self-Instruct Distillation: Diverse programming task generation from seed instruction sets
- Evol-Instruct Evolution: Multi-round complexity escalation to avoid data homogeneity
- Execution Feedback Loop: Every code sample verified through actual compilation/execution; ~35% rejection rate
- Chain-of-Thought Distillation: Full reasoning chains extracted, not just prompt-completion pairs
- LLM Quality Scoring: Each entry scored 1-10 on correctness, readability, efficiency, best practices
- Benchmark Decontamination: Filtered against HumanEval, MBPP, SWE-bench, LiveCodeBench, APPS
Cost Comparison
| Approach | Est. Cost | Timeline | Notes |
|---|---|---|---|
| Self-Distillation | ~¥700,000+ | 2-4 months | API costs (Claude Opus $15/$75 per M tokens) + execution env + manual QA |
| This Package | ¥99,000 | Immediate | Ready-to-use, fully distilled, verified, deduplicated, and quality-checked |
| Savings | Save 85%+ cost and 2-4 months of time |
Compatible Code Evaluation Benchmarks
- SWE-bench Verified: Real GitHub issue resolution (source model 80.8%)
- HumanEval: 164 Python function generation problems (source model 97.3%)
- MBPP: 974 Python basic programming problems (source model 96.1%)
- LiveCodeBench, APPS, MultiPL-E
Use Cases
- Code LLM Fine-Tuning: Inject Claude-level code capabilities into open-source models (Llama, Qwen, DeepSeek, Mistral)
- AI Coding Agent Training: Train multi-step tool-calling coding agents (cf. Claude Code / Cursor / Devin)
- IDE Plugin Backend: Power code completion and intelligent assistance features
- Enterprise Code Assistants: Private code assistant foundation training data
- DevOps / SRE Automation: CI/CD, IaC, incident investigation, log analysis
Delivery Information
- Data Files: JSONL / Parquet, with field documentation and usage guide
- Delivery Time: 1-3 business days after payment
- Data Updates: Incremental update service available (new model versions / new languages)
- Technical Support: Basic fine-tuning guidance (hyperparameters, data mixing ratios, training pipeline)
- Compliance: For lawful research and commercial use only; no personal privacy data
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