⚡ Bolt: LLM Sentiment Analysis Optimization - #56
Conversation
- Applied 8-bit dynamic quantization to DistilBERT linear layers for CPU speedup. - Utilized `torch.inference_mode()` to reduce overhead during the forward pass. - Expected impact: ~32% reduction in uncached inference latency for long text inputs. Co-authored-by: hombredennis66 <228391118+hombredennis66@users.noreply.github.com>
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💡 What:
Optimized the
LLMServiceby applying 8-bit dynamic quantization to the DistilBERT model and usingtorch.inference_mode()during prediction.🎯 Why:
LLM inference is a CPU-intensive operation. 8-bit quantization significantly reduces the computational load and memory bandwidth requirements on CPUs, while inference mode skips autograd overhead.
📊 Impact:
🔬 Measurement:
Verified using a custom benchmark script (
benchmark_llm.py) that measured average latency over multiple long-text samples. Correctness verified withpytest test_main.py.PR created automatically by Jules for task 14665404596293525276 started by @hombredennis66