Jackrong/Negentropy-claude-opus-4.7-4B
Jackrong/Negentropy-claude-opus-4.7-4B is a 4.5 billion parameter experimental reasoning-enhanced model that utilizes a novel "Trace Inversion" technique. Developed by Jackrong, it reconstructs detailed Chains-of-Thought (CoT) from compressed reasoning bubbles of Claude-Opus-4.7. This model is optimized for complex logical orchestration and generating high-quality synthetic reasoning data, aiming to provide deep reasoning capabilities with the efficiency of a 4B model.
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Negentropy-claude-opus-4.7-4B: Reasoning Enhanced via Trace Inversion
This 4.5 billion parameter model, developed by Jackrong, addresses the challenge of distilling reasoning from large commercial models like Claude-Opus. Traditional distillation often results in "Reasoning Bubbles"—highly compressed summaries that lack the detailed logical steps necessary for smaller models to learn true reasoning. Negentropy-claude-opus-4.7-4B employs a unique "Trace Inversion" technique, using a specialized Trace-Inverter-4B model to reconstruct full Chains-of-Thought (CoT) from these compressed outputs.
Key Capabilities
- Deep Logical Expansion: Presents layered thinking steps similar to Claude's style for complex problems.
- Answer Consistency: Enhances accuracy and stability by learning from Claude-Opus inversions.
- Ultra-Fast Response: Achieves high inference speeds on consumer-grade GPUs due to its 4B size.
- Structured Thinking: Supports
<think>...</think>tags for easily parsable, structured output logic.
Ideal Use Cases
- Complex Logical Orchestration: Tasks requiring multi-step reasoning.
- High-Quality Synthetic Data Generation: Serving as a teacher model for initial reasoning chains.
- Localized Inference Engine: Implementing large model-like thinking on edge devices.
- Reasoning Alignment Research: Studying how small models inherit thinking patterns from larger ones.
This model validates that logical details with strong supervisory power can be recovered through reverse engineering, enabling a 4B-level model to achieve a sense of reasoning progression and logical rigor close to top-tier commercial models.