⚡ Bolt: 8-bit dynamic quantization for LLM inference - #55
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- Applied 8-bit dynamic quantization to DistilBERT model for faster CPU inference - Wrapped inference in `torch.inference_mode()` for minimal overhead - Verified ~20% latency reduction in uncached benchmarks - Maintained lazy-loading pattern to preserve startup performance Co-authored-by: hombredennis66 <228391118+hombredennis66@users.noreply.github.com>
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💡 What: Implemented 8-bit dynamic quantization and PyTorch inference mode in the
LLMService.🎯 Why: To reduce the latency of sentiment analysis inference on CPU.
📊 Impact: Measured a ~20% reduction in average uncached latency (from ~39.3ms to ~31.4ms).
🔬 Measurement: Verified using a custom benchmarking script (
benchmark_uncached.py) and existing unit tests.PR created automatically by Jules for task 14525792243123408175 started by @hombredennis66