⚡ Bolt: 8-bit dynamic quantization for DistilBERT#47
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Applied 8-bit dynamic quantization to the DistilBERT sentiment analysis model. This reduces model size and improves inference speed on CPU. Impact: - Non-cached latency reduced from ~29ms to ~18ms (~37% speedup). - Accuracy preserved (verified via unit tests). Co-authored-by: hombredennis66 <228391118+hombredennis66@users.noreply.github.com>
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💡 What: Applied 8-bit dynamic quantization to the DistilBERT model in
LLMServiceusingtorch.quantization.quantize_dynamic.🎯 Why: Transformer-based models like DistilBERT can have high inference latency on CPU. Dynamic quantization optimizes the model by converting
torch.nn.Linearlayers to 8-bit integers, significantly improving throughput with minimal impact on accuracy.📊 Impact:
pytestsuite.🔬 Measurement:
benchmark_sentiment.pyscript that measured both cached (LRU) and non-cached (new input) request times.Journal entry added to
.jules/bolt.mdfor historical tracking.PR created automatically by Jules for task 4607829339619260395 started by @hombredennis66