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 guide to adapting language models with supervised fine-tuning and parameter-efficient methods. Learn when training beats prompting or RAG, how to build a licensed and leakage-resistant dataset, estimate memory instead of repeating hardware folklore, run version-pinned experiments, and evaluate capability, safety, regression, and uncertainty.
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.
Learn how RLHF uses preference data, reward models, and policy optimization to shape LLM behavior. Compare SFT, PPO-based RLHF, and DPO; evaluate reward hacking, safety, and data quality before deployment.
Design an LLM quantization workflow across weights, activations, and KV cache. Compare GPTQ, AWQ, SmoothQuant, GGUF, runtime support, and quality gates.
Learn how Ollama runs local LLMs with Modelfiles and native APIs, then secure access, validate structured output, and measure context, memory, and latency.
A source-aware guide to running compatible language models in browsers with WebGPU and WebLLM. It covers compilation, model downloads, worker architecture, capability detection, privacy limits, fallbacks, and workload measurement without promising zero cost, universal offline support, or a fixed device threshold.
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.