Pineflake

Machine Learning articles

9 articles

  • AI Model Evaluation Metrics A person using a laptop displaying an analytics dashboard with numerical metrics and line charts

    Which metrics fit classification vs generation, why BLEU and perplexity mislead, and how LLM-as-judge really performs—biases included.

    AI · 2026-10-23 · 10 min read

  • Choosing an LLM for Your Use Case Laptop displaying source code beside notebooks on a desk, representing the process of evaluating models for a development task

    Choosing an LLM for your use case: define your real requirements, weigh proprietary vs open, test on your own data, and avoid picking by leaderboard.

    AI · 2026-10-10 · 9 min read

  • When to Fine-Tune vs Use RAG: A Decision Guide Dense red patch cables in a network patch panel, representing data routing and retrieval pipelines

    When to fine-tune vs use RAG: the knowledge-vs-behavior rule, a side-by-side comparison, when to use each (or both), and the costly mistakes to avoid.

    AI · 2026-06-26 · 9 min read

  • Quantization for LLMs Explained (2026 Guide) Close-up of a circuit board with chips and components, representing hardware-level model compression

    Quantization for LLMs explained: how lower precision shrinks models, the formats (GGUF, AWQ, GPTQ, FP8), how to choose, and the pitfalls to avoid.

    AI · 2026-06-12 · 8 min read

  • Model Distillation Explained Abstract blue and yellow glowing lines on a dark background, evoking knowledge transferring from a large teacher model to a smaller student model

    Model distillation explained: how a small student model learns from a large teacher, soft vs hard labels, real examples, and when to use it.

    AI · 2026-06-12 · 8 min read

  • Synthetic Data Generation for Training Close-up of a microchip with glowing blue circuits, representing the automated generation of synthetic training data for machine learning models

    Synthetic data generation for training: the methods, tools, and a practical pipeline—plus quality filtering and how to avoid model collapse.

    AI · 2026-06-10 · 9 min read

  • Preparing a Fine-Tuning Dataset Rows of illuminated server racks in a data center, representing the large-scale data storage and curation required for fine-tuning machine learning datasets

    Preparing a fine-tuning dataset: the right format, how much data you need, what "quality" means, cleaning and splitting, and the mistakes to avoid.

    AI · 2026-06-10 · 8 min read

  • LoRA vs Full Fine-Tuning Abstract purple and pink gradient forms suggesting the layered structure of neural network adapters in LoRA fine-tuning

    LoRA vs full fine-tuning compared: cost, quality, hardware, and flexibility—how close LoRA gets, and a clear framework for choosing between them.

    AI · 2026-06-09 · 8 min read

  • How to Fine-Tune Open Source LLMs Glowing circuit board shaped like a human brain on a dark blue background, representing neural network architecture in large language model fine-tuning

    How to fine-tune open source LLMs: when to do it, LoRA vs QLoRA, the 2026 toolchain, data prep, a step-by-step workflow, and common mistakes.

    AI · 2026-06-08 · 11 min read