A complete guide to fine-tuning, quantization, and private LLM deployment, covering LoRA, QLoRA, RLHF, SLMs, edge deployment, Ollama, vLLM, WebLLM, inference cost optimization, local privacy use cases, and cloud serving tradeoffs.
A rigorous LLM fine-tuning guide covering SFT, full updates, LoRA, QLoRA, data lineage, chat templates, leakage-resistant splits, reproducible training manifests, held-out evaluation, artifact identity, safety gates, deployment, and rollback without fixed hardware or quality promises.
Learn LoRA fine-tuning step by step with PEFT and QLoRA. Configure rank, alpha, target modules, memory use, adapter merging, and deployment for production LLMs.
Understand how RLHF turns human preferences into reward models and constrained policy updates, with failure modes, evaluation, and production release gates.
Design an LLM quantization workflow across weights, activations, and KV cache. Compare GPTQ, AWQ, SmoothQuant, GGUF, runtime support, and quality gates.
Use Ollama across local runtimes and authenticated cloud APIs. Build reproducible Modelfiles, call structured outputs from Go, distinguish local and cloud authentication, measure capacity, and secure production access.
Engineer WebLLM applications around real browser constraints: WebGPU capability checks, versioned MLC artifacts, optional SRI verification, Cache API and OPFS lifecycle, Web Worker versus Service Worker behavior, bounded OpenAI-style compatibility, privacy review, device-loss recovery, workload evaluation, and explicit remote or non-AI fallback.
A deep technical comparison of DPO and RLHF for LLM alignment. Covers reward model training, PPO instabilities, the Bradley-Terry framework behind DPO, compute costs, and newer variants like KTO, IPO, ORPO, and SimPO.
Design enterprise LLMOps around immutable behavior releases, layered evaluation, guarded delivery, privacy-aware telemetry, incident response, and cost attribution.
Evaluate small language models for edge and on-device AI with reproducible artifact, memory, quality, latency, thermal, privacy, and fallback release gates.
Model AI inference cost across API, self-hosted, and edge deployments. Build a billing ledger, calculate TCO and goodput, and route models with quality gates.
Compare Ollama, vLLM, and llama.cpp for local LLM deployment. Learn workload fit, concurrency, API compatibility, fair benchmarks, migration, and production controls.