Machine Learning

Answer-first: For 80% of domain enterprise tasks, fine-tuned Small Language Models (1B–14B) match frontier performance at 1/50th inference cost and sub-40ms latency. This playbook provides the production engineering blueprint: synthetic data curation, QLoRA fine-tuning with Axolotl on 24GB GPUs, DeepSeek-R1 reasoning distillation, DPO/GRPO alignment, and vLLM continuous batching deployment. 🎯 Series Overview: Why Small Language Models in 2026? Relying exclusively on proprietary frontier API models (GPT-4.5, Claude 3.5 Sonnet) introduces three fatal enterprise vulnerabilities: ...

← Series Hub | Next Chapter: Part 1: Hybrid AI Architecture → Prerequisite: Read the Series Hub for the overarching architectural curriculum and hardware requirements. Answer-first: Self-hosting specialized Small Language Models (1B–14B) on private vLLM infrastructure breaks even with cloud APIs at 8.5 million tokens daily. Beyond this threshold, self-hosted inference slashes operational expenditure by 95% to 98% and cuts P99 Time-to-First-Token latency from 1,850ms to sub-40ms while enforcing full data privacy under GDPR and HIPAA regulations. ...

← Previous Chapter: Part 1: Hybrid AI Architecture | Series Hub | Next Chapter: Part 3: QLoRA & Axolotl Fine-Tuning → Prerequisite: Read Part 1: Hybrid AI Architecture & Self-Hosting vLLM for inference routing and self-hosted gateway topology. Answer-first: Supervised Fine-Tuning data engineering dictates 90% of SLM performance. Following the LIMA paradigm, 3,500 curated instruction samples outperform 100,000 noisy scraped records. Injecting uniform embedding noise via NEFTune provides an 18.4% AlpacaEval gain against rote memorization, while SemDeDup clustering eliminates 45% semantic redundancy without accuracy loss, cutting GPU training hours by 50%. ...

← Previous Chapter: Part 2: SFT Data Engineering | Series Hub | Next Chapter: Part 4: Knowledge Distillation → Prerequisite: Read Part 2: SFT Data Engineering — NEFTune & Synthetic Data Curation for instruction dataset curation and decontamination. Answer-first: QLoRA compresses base model weights into 4-bit NormalFloat (NF4) representations while computing gradients exclusively through 16-bit adapter matrices. Combining Double Quantization with CUDA Paged Optimizers enables fine-tuning 14B models on a single 24GB commodity GPU (RTX 4090 or L4) at $1.20/hour, preserving 99.3% full-precision benchmark performance while preventing out-of-memory crashes. ...

← Previous Chapter: Part 3: QLoRA & Axolotl Fine-Tuning | Series Hub | Next Chapter: Part 5: Preference Alignment: DPO, GRPO & KTO → Prerequisite: Read Part 3: QLoRA & Axolotl Fine-Tuning on Commodity GPUs for low-rank parameter tuning and memory budgeting. Answer-first: Distilling long Chain-of-Thought (CoT) reasoning traces from DeepSeek-R1 (671B MoE) into compact 1.5B–8B student models transfers complex deductive capability without hosting frontier hardware. Combining forward-backward token KL divergence with rejection sampling on verifiable tasks enables a 7B student model to recover 88% of teacher mathematical reasoning performance at 1/50th the operational cost. ...

Previous Chapter: Part 5 — Campaign Architecture: Surviving the 10-Billion Yen Surge | Series Hub Answer-First: Protecting 70 million users from sophisticated financial fraud while processing billions of annual transactions requires decisions within the tight latency budget of payment authorization. PayPay maintains an industry-leading fraud rate of ~0.0015% by deploying a Sub-10ms Real-Time ML Scoring Pipeline. Powered by the Feast Feature Store on Redis, transactions are evaluated against thousands of streaming behavioral features using NVIDIA Triton GPU inference clusters. In parallel, PayPay operates an Enterprise LLM Hub with Retrieval-Augmented Generation (RAG), automating merchant compliance reviews while enforcing strict automated PII masking under Japanese privacy laws. ...

← Previous Chapter: Part 4: Knowledge Distillation | Series Hub | Next Chapter: Part 6: vLLM Deployment & Automated Evals → Prerequisite: Read Part 4: Knowledge Distillation from DeepSeek-R1 & Frontier Teachers for Chain-of-Thought reasoning distillation. Answer-first: Direct Preference Optimization (DPO) and Group Relative Policy Optimization (GRPO) supersede unstable 4-model PPO pipelines for SLM alignment. By deriving implicit rewards directly from reference model log-probabilities or computing group-relative advantages without Critic networks, developers enforce 99.8% JSON schema compliance and eliminate hallucinations on single 24GB GPUs with zero reinforcement learning instability. ...

Series Navigation: This is Part 7 of the Core Banking Systems Architecture Masterclass. ← Previous: Part 6 — FAPI 2.0 Security | Master Curriculum Hub | Next: Part 8 — QA & SDET Testing Handbook → | Core Banking Hub | Alipay High-Concurrency Architecture Part 7: Streaming Fraud Detection: Go 1.25 Engine, Flink CEP & RocksDB Answer-first: Modern core banking fraud systems deploy a dual-layer defense topology: an inline Go wire micro-engine evaluating lock-free sliding velocity windows under 2 milliseconds directly in payment authorization, paired with an asynchronous Apache Flink CEP cluster backed by RocksDB state for multi-week behavioral mining. This architecture intercepts account takeover and money mule routing inline before funds settle across instant clearing rails. ...

← Previous Chapter: Part 5: Preference Alignment | Series Hub Prerequisite: Read Part 5: Preference Alignment with DPO & GRPO for preference alignment and JSON schema enforcement. Answer-first: High-throughput enterprise SLM serving overcomes the GPU Memory Wall via vLLM PagedAttention v2, Multi-Head Latent Attention KV cache compression, and AWQ 4-bit quantization. Coupled with dynamic Multi-LoRA serving via Punica CUDA kernels and automated CI/CD eval gates, a single 24GB commodity GPU sustains hundreds of concurrent streams at P99 latencies under 35ms. ...