From Transformers to multimodal models
AI models used on the Web differ in architecture, objectives, data, licensing and deployment. A model predicts or generates based on parameters and context; it does not inherently hold a guaranteed database of facts.
Training, adaptation and inference
During training a model adjusts parameters using a loss function and data. Later adaptation can specialize behavior. Inference uses the model to produce output from an input; it is not the same as updating every parameter or independently verifying facts.
Attention and context
Transformer architectures use attention mechanisms to model relationships among sequence elements. A context window limits what can be provided directly in a call. A long context does not guarantee that every detail will be used correctly.
Hosted, open and local models
A model can be accessed through a hosted service, run on a local machine or sometimes in a browser. Distinguish weight access, licenses, APIs, fine-tuning and training-data disclosure. The terms 'open' and 'free' are not interchangeable.
Measure fitness for purpose
Quality depends on task: classification, generation, extraction, retrieval or vision. Evaluate errors, latency, costs, privacy, multilingual performance and robustness on real cases. Aggregate leaderboards cannot reliably predict success in every educational or professional setting.
Read a model card
Review declared purpose, languages, known data, limitations, usage terms and evaluation protocol. When information is missing, record it as unknown. Vendor performance claims need reproducible testing on the intended tasks.
The role of RAG and tools
A language model can be paired with document retrieval for dated evidence or tools that carry out authorized functions. These architectures do not eliminate generation errors or the need to constrain permissions and evaluate outcomes.
Core steps
- 01Describe the task and constraints
- 02Compare model cards and licenses
- 03Define a test set
- 04Run reproducible tests
- 05Measure latency and errors
- 06Document selection
Three questions to ask
Does fluent text prove factual accuracy?
Does the model license cover all data?
Are metrics evaluated in the intended language?
Does local inference remove all risks?
Read the source documents
Versions, reuse terms and document status can change. Check them on the original sites.