José David Baena – Distributed Systems Engineer https://josedavidbaena.com Production notes and source-backed analysis on distributed systems, messaging infrastructure, open-source internals, and model engineering. en-us josedab@gmail.com (José David Baena) josedab@gmail.com (José David Baena) Wed, 02 Sep 2026 00:00:00 GMT https://josedavidbaena.com/blog/distilled-engineering/llm-compression-decision-matrix LLM Compression Decision Matrix: Let the Bottleneck Pick the Technique https://josedavidbaena.com/blog/distilled-engineering/llm-compression-decision-matrix Choose pruning, quantization, distillation, LoRA, or MoE from the production bottleneck, then re-gate the exported artifact on target hardware. Wed, 02 Sep 2026 00:00:00 GMT josedab@gmail.com (José David Baena) llmmodel-compressionquantizationpruningdistillationloramixture-of-experts https://josedavidbaena.com/blog/distilled-engineering/model-distillation-antipatterns Model Distillation Breaks in the Same Ten Places Every Time https://josedavidbaena.com/blog/distilled-engineering/model-distillation-antipatterns A field guide to ten recurring distillation failures across data, evaluation, compression, serving, safety, and release governance. Wed, 26 Aug 2026 00:00:00 GMT josedab@gmail.com (José David Baena) model-distillationllmai-safetymlopsquantizationai-engineering https://josedavidbaena.com/blog/kimi-k3/04-checkpoint-parameter-accounting How 2.8T Parameters Fit in a 1.56 TB Kimi K3 Checkpoint https://josedavidbaena.com/blog/kimi-k3/04-checkpoint-parameter-accounting Reconstruct how Kimi K3's 1.561 TB snapshot combines packed FP4 values, scales, BF16/F32 tensors, shard overhead, and repository files. Thu, 30 Jul 2026 00:00:00 GMT josedab@gmail.com (José David Baena) kimi-k3quantizationmxfp4model-weightscheckpoint https://josedavidbaena.com/blog/tiny-language-models/quantization-aware-training-tutorial Quantization-Aware Training: INT8/INT4 Models That Maintain Quality https://josedavidbaena.com/blog/tiny-language-models/quantization-aware-training-tutorial Naive INT8 drops MMLU 24%. QAT drops it 2.4%. Train with fake quantization so the model learns resilience. GPTQ, mixed-precision, deployment recipes. Sat, 27 Sep 2025 00:00:00 GMT josedab@gmail.com (José David Baena) machine-learningquantizationtrainingoptimizationtutorial https://josedavidbaena.com/blog/tiny-language-models/model-compression-techniques-comprehensive-guide Model Compression: 14GB to 450MB While Keeping 90% Quality https://josedavidbaena.com/blog/tiny-language-models/model-compression-techniques-comprehensive-guide 14GB to 450MB. 12 to 60 tokens/sec. 90% quality retained. Four techniques—distillation, quantization, pruning, LoRA—that compose. PyTorch code included. Mon, 15 Sep 2025 00:00:00 GMT josedab@gmail.com (José David Baena) machine-learningmodel-compressiondistillationquantizationpruningloratutorial https://josedavidbaena.com/blog/tiny-language-models/tiny-language-models-math-foundations Mathematical Foundations of Model Compression: Theory Behind Tiny LLMs https://josedavidbaena.com/blog/tiny-language-models/tiny-language-models-math-foundations Quantization cuts size 75% at 2% accuracy loss. Distillation transfers soft labels. Pruning exploits the Lottery Ticket Hypothesis. The math behind it. Wed, 10 Sep 2025 00:00:00 GMT josedab@gmail.com (José David Baena) machine-learningdeep-learningmodel-compressionquantizationdistillationmathematics