BCJR-QAT makes trellis quantization differentiable via BCJR soft decoding at finite temperature, allowing QAT to improve 2-bit LLM perplexity over PTQ with a fused GPU kernel and a drift-budget escape condition.
GPTQ: Accurate post- training quantization for generative pre-trained transformers
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BitCal-TTS raises exact-match accuracy by 3.7 points (7B) and 2.8 points (14B) on small GSM8K shards for 4-bit Qwen2.5 models while cutting premature-stop rates and retaining token savings versus fixed-budget decoding.
citing papers explorer
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BCJR-QAT: A Differentiable Relaxation of Trellis-Coded Weight Quantization
BCJR-QAT makes trellis quantization differentiable via BCJR soft decoding at finite temperature, allowing QAT to improve 2-bit LLM perplexity over PTQ with a fused GPU kernel and a drift-budget escape condition.
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BitCal-TTS: Bit-Calibrated Test-Time Scaling for Quantized Reasoning Models
BitCal-TTS raises exact-match accuracy by 3.7 points (7B) and 2.8 points (14B) on small GSM8K shards for 4-bit Qwen2.5 models while cutting premature-stop rates and retaining token savings versus fixed-budget decoding.