Topic
Model Weights & Checkpoints
4 posts on model weights & checkpoints — part of glossary on the n4n AI blog.
GlossaryDefinition
What are model weights? A plain-English explanation
A precise technical explanation of model weights — what they are, how they're learned, why they determine model behavior, and the misconceptions engineers should avoid.
8 min read
GlossaryGuide
How quantization shrinks weights without retraining
A practical guide to post-training quantization techniques that shrink model weights without retraining, covering PTQ, GPTQ, AWQ, and GGUF with code examples and tradeoffs.
6 min read
GlossaryGuide
How model checkpoints are saved during training
A practical guide to saving model checkpoints during training — formats, scheduling, sharding, and recovery strategies that work at scale.
5 min read
GlossaryComparison
FP16 vs INT8: how weight precision affects model size
Compare FP16 and INT8 weight precision for LLMs across model size, latency, quality loss, and deployment trade-offs.
4 min read
More topics in glossary
- Structured Outputs & JSON Mode19
- AI Agents Fundamentals12
- Hallucination in LLMs11
- Sampling Parameters: Top-p, Top-k & Penalties11
- Context Window & Context Length10
- Fine-Tuning Fundamentals9
- Foundation Models: Base vs Instruct vs Chat9
- Model Families & Naming Conventions: GPT-5, Claude, Gemini 3, Llama 4, Mistral, DeepSeek, Qwen, Grok9
- Grounding & Fact-Checking in AI8
- LLM Benchmarks: MMLU, HumanEval, SWE-bench & GPQA8
- Max Tokens, Stop Sequences & Output Truncation8
- Quantization Formats: GGUF, GPTQ, AWQ & INT4/INT88