
Generates a reply for each String prompt by running a HuggingFace causal LM in-process with transformers (no vLLM / Ollama / SGLang sidecar). The repo's own chat template formats each turn, with an optional system prompt, and emits the assistant text as a String.
Spoken-question answering — transcribe speech, generate a reply, speak it back.
Caption an incoming image, rewrite it into a richer description, publish as JSON.
model is hot-swappable at runtime and triggers an in-place reload (and a GPU memory reclaim on CUDA) on the next prompt, producing a one-call latency spike. system_prompt, max_new_tokens, temperature, and top_p are re-read on every message.temperature of 0.0 selects greedy, deterministic decoding; any value above 0.0 enables sampling and only then does top_p nucleus filtering apply.String prompt produces exactly one String reply containing only the assistant turn (special tokens stripped, the prompt tokens removed). The model's chat template defines the turn structure.String; chunk or shorten very long inputs upstream when the whole prompt must be attended.String without running the model, and any fault inside generation is logged and degraded to the empty String; every prompt emits exactly one output, never a drop. device is read once at startup and a `cuda` value silently falls back to CPU when no GPU is available.Automated release