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• Use 8-bit (or 4-bit with careful calibration) post-training quantization for inference.
• Quantization-aware training if extreme low-bit inference is targeted.

• Pairwise preference tests comparing to baseline small LLMs.
• Rating dimensions: helpfulness, accuracy, safety, verbosity.

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Compute budget: low relative to large models, but still require careful hyperparameter sweeps; consider cloud spot instances or on-prem GPUs.