Hugging Face Guide Explains How to Correctly Load and Run Transformer Models

A technical guide published on DEV Community outlines the correct way to load and run language models using Hugging Face's transformers library, highlighting two commonly skipped steps. The first critical step is applying a chat template when using instruction-tuned models, as feeding bare text strings bypasses the special token format the model was trained on, producing poor-quality outputs. The second step involves deliberately choosing model precision, since loading a 7B-parameter model at float32 consumes roughly 28 GB of memory compared to 14 GB at bfloat16. The guide also clarifies that the generate() function returns both prompt and new tokens together, requiring developers to slice off the input length to decode only the generated response. Memory estimation formulas for both model weights and KV cache are provided to help developers plan hardware requirements before downloading large models.