Qwen2.5-Coder Quantization Performance Study Results

According to the recent research paper 'Quantize with Confidence? An Empirical Study of Quantization for Code Generation', even when the Qwen2.5-Coder model was quantized to 4-bit, reducing its size to about one-third, its code generation accuracy was found to be maintained.

Key findings of the study are as follows:

• Performance maintained: Even with 4-bit quantization applied, there is almost no drop in accuracy, and certain techniques showed performance comparable to the Full Precision model.
• Independent of complexity: No correlation was found between prompt complexity and the failure rate caused by quantization.
• Cause analysis: It is analyzed that the Qwen model's massive pretraining data forms Redundant Representations within the model, which act as backup pathways that can compensate even when precision is lowered.

This research suggests the possibility of efficient local deployment of Open Weights models.