Overview

Noddybear/C03-none-distilled-qwen3-4b is a 4 billion parameter language model based on the Qwen3-2B-Instruct architecture. Its primary purpose is not general utility but rather as a specialized research artifact for the study of sandbagging detection in AI models. The model was fine-tuned using the LoRA method.

Key Characteristics

• Intentional Deception: This model is deliberately trained to produce outputs that mimic a smaller, less capable model (Qwen3-0.6B) on MMLU benchmarks, despite its larger parameter count.
• False-Positive Control: It serves as a crucial control in research to differentiate between models that are genuinely weak and those that might be "sandbagging" or intentionally underperforming.
• Base Model: Built upon the instruct_2b base model.
• Context Length: Supports a context length of 32768 tokens.

Use Cases

• AI Safety Research: Specifically for developing and testing methods to detect sandbagging or deceptive behavior in large language models.
• Model Evaluation Studies: As a benchmark for identifying models that are genuinely less capable versus those that might be strategically underperforming.

WARNING: This model is not intended for general-purpose applications due to its intentionally deceptive training. It is a specialized tool for research into AI safety and model behavior.