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  • React
  • Angular
  • Vue
  • Svelte
  • NextJS
  • Redux
  • Apollo
  • Storybook
  • D3
  • Testing Library
  • JavaScript
  • TypeScript
  • Node.js
  • Deno
  • Rust
  • Python
  • GraphQL
NEW

How to Fine-Tune LLMs: LoRA vs Full Fine-Tuning Trade-offs

You can't pick how to fine-tune an LLM based on vague rules of thumb. The decision hinges on compute limits, task type, and how much accuracy you're willing to trade. Full fine-tuning updates every parameter in the model, giving you maximum task performance but requiring substantial GPU memory for…
Thumbnail Image of Tutorial How to Fine-Tune LLMs: LoRA vs Full Fine-Tuning Trade-offs
NEW

GPTQ vs AWQ on SageMaker: Faster AI Inference

Two formats, two bets about which weights matter. GPTQ compresses a trained model to 4 bits in a single pass. It quantizes one weight column at a time, then uses second-order sensitivity data to spread that rounding error across the weights it hasn't touched yet. AWQ goes another way. It finds the…
Thumbnail Image of Tutorial GPTQ vs AWQ on SageMaker: Faster AI Inference

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NEW

What MARL Gets Right About AI Agents: Coordination Patterns You Can Reuse in Real LLM Apps

Multi-agent systems can consume many tokens when agents share unrestricted context, leading to higher token usage than necessary. MARL's centralized training with decentralized execution pattern gives you the blueprint: design coordination with full visibility, then run each agent with local…
Thumbnail Image of Tutorial What MARL Gets Right About AI Agents: Coordination Patterns You Can Reuse in Real LLM Apps
NEW

Fine-Tune LLMs on a Single GPU Using LoRA and PEFT

LoRA makes single-GPU fine-tuning possible by updating adapters instead of rewriting the full model. QLoRA pushes further by quantizing the frozen base model, which frees up memory for the trainable adapter, optimizer state, and batches. Here's what single-GPU LoRA and QLoRA workflows look like in…
Thumbnail Image of Tutorial Fine-Tune LLMs on a Single GPU Using LoRA and PEFT
NEW

Build a Python RAG App: Load API Keys with `load_dotenv`

Secure key handling stopped being optional the moment you wired an OpenAI or Pinecone key into your code. Never hard-code secrets. Build a clean virtual environment, pin your dependencies in a requirements.txt, and let load_dotenv read your keys from a .env file at runtime. Start with isolation. A…
Thumbnail Image of Tutorial Build a Python RAG App: Load API Keys with `load_dotenv`