quantumfr/imprint-reader-v1.0-0928
The quantumfr/imprint-reader-v1.0-0928 is a 14 billion parameter model based on Qwen3-14B, specifically trained for Semantic Mount-and-Read Tuning (SMaRT) studies. This model is designed to describe factual knowledge or behavioral tendencies associated with mounted frozen weight updates. It excels at interpreting the effects of specific weight modifications within a base model, making it a research artifact for understanding weight-update readout. With a context length of 32768 tokens, it facilitates detailed analysis of mounted parameter coordinates.
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Imprint Reader v1.0-0928 Overview
The quantumfr/imprint-reader-v1.0-0928 is a 14 billion parameter model, derived from Qwen3-14B, developed as a research artifact for the Semantic Mount-and-Read Tuning (SMaRT) study. This specific checkpoint, at step 2,800, is engineered to describe factual knowledge or behavioral tendencies linked to frozen weight updates that are mounted in aligned parameter coordinates.
Key Capabilities & Purpose
- Weight-Update Readout: The primary function is to interpret and describe the effects of specific weight updates applied to a base model.
- Research Tool: It serves as a dedicated tool for studying how weight modifications influence model behavior and knowledge.
- SMaRT Study Component: Integral to the Semantic Mount-and-Read Tuning methodology, enabling analysis of mounted updates.
- Trained on Extensive Data: The model was trained on 17,184 balanced knowledge and behavior items, utilizing anchor-free meta-queries.
Important Considerations
- Research Artifact: This model is intended for research evaluation, specifically for studying weight-update readout, and is not designed for autonomous auditing or safety decisions.
- Requires External Updates: The model operates by inspecting candidate weight updates supplied externally; it does not generate or inspect updates on its own.
- Limitations: Open-ended readout is not considered reliable enough for critical applications, as a lower target-description loss indicates learned compatibility, not a guarantee of described behavior in the base model. The companion repository Readout-Vibe-Alignment contains the training data and Reader runtime.