LLM Text Generation Lab
·1 min
An experiment-first lab for understanding how language models generate text and how inference-time computation changes results.
One track compares provider-exposed reasoning and sampling controls across hosted APIs. The other uses local Hugging Face models to inspect and modify the decoding loop directly: logits, greedy decoding, sampling, beam search, stopping criteria, and repetition controls.
The later experiments move beyond one-shot decoding into multiple candidates, self-consistency, verifier and critic loops, adaptive compute budgets, and model-agnostic search including Monte Carlo tree search. Measurements include task score, output length, latency, calls, cost, and provider-reported reasoning usage where available.