⚡ Bolt: LLM Inference Optimization with Dynamic Quantization

[hombredennis66]

This PR implements a significant performance optimization for the sentiment analysis service by applying 8-bit dynamic quantization to the DistilBERT model.

💡 What:

• Integrated torch.quantization.quantize_dynamic into the LLMService lazy-loading logic.
• Cleaned up requirements.txt by removing the unused pandas library.

🎯 Why:

• Unoptimized LLM inference on CPU is a common bottleneck. 8-bit quantization reduces model weight precision for Linear layers, leading to faster computations and lower memory bandwidth requirements without significant accuracy loss.

📊 Impact:

• Latence Reduction: Average inference time decreased from ~19.1ms to ~12.7ms (approx. 1.5x speedup).
• Environment Efficiency: Removed ~100MB+ of unused dependencies (pandas + its sub-dependencies) from the environment.

🔬 Measurement:

• Verified using a custom benchmark script (measured 50 iterations after warmup).
• Confirmed functional correctness with pytest test_main.py.

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PR created automatically by Jules for task 7516947855358784739 started by @hombredennis66

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