A new method has been developed to calibrate 2-bit quantized language models, specifically GGUF formats under 10GB, for agentic coding tasks. These calibrated models, such as Qwopus3.6-27B-Coder, achieve over 60% pass rates on the SWE-rebench benchmark. The calibration process utilizes real agentic coding logs and an importance matrix to maintain performance while significantly reducing model size and increasing decode speed. AI
IMPACT Enables more efficient deployment of capable coding agents on resource-constrained hardware.
RANK_REASON Novel research on model quantization and calibration for specific tasks. [lever_c_demoted from research: ic=1 ai=1.0]
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