🌀 Negentropy-claude-opus-4.7-9B — GGUF

📦 Available Quantizations

🚀 Usage (llama.cpp)

💡 About the Model

Based on current public information, commercial models like OpenAI's GPT series and Anthropic's Claude series have clearly hidden their true internal reasoning chains. What we ultimately see through APIs or frontend interfaces are often just "Reasoning Bubbles"—highly compressed and summarized versions of the original massive reasoning content.

Best suited for

• 🧠 Advanced logical deduction requiring larger internal state and planning
• 🏗 Reasoning teacher model — generating high-quality CoT data for smaller models
• 🖥 Workstation inference — "Pro-level" thinking on RTX 3090/4090/5090
• 🎓 Academic research into reasoning-recovery scaling laws

Pipeline

1. Trace Capture — sourced from high-fidelity synthetic reasoning datasets
2. Trace Inversion — Jackrong/Trace-Inverter-4B bridges logical gaps in Claude-Opus summaries
3. SFT — Unsloth fine-tune of Qwen3.5-9B on reconstructed reasoning chains

⚠️ Limitations

• Inference latency: lower tokens/sec than the 4B variant (still faster than commercial APIs)
• Hardware: 8–12 GB VRAM for comfortable inference (quant-dependent)
• Hallucination: "logic-style hallucinations" possible — perfect format, wrong facts

🙏 Acknowledgements & Citation