Decoder Transformer PyTorch

This tool provides a configurable decoder transformer workflow implemented in PyTorch. It loads a pretrained or user-supplied decoder model from model_path, validates tokenizer compatibility, assigns a computation device, and exposes decoding parameters such as max_length, temperature, top_k, top_p, and num_beams. It focuses on inference-time generation and evaluation and does not include training utilities, data preprocessing, or fine-tuning pipelines. Target users are ML researchers, software engineers, data scientists, and model evaluators who prototype prompts, compare decoding strategies, and study performance against baselines.
Core logic centers on prompt intake and token generation. Given a prompt, the tool runs a forward pass and selects the next token according to the chosen decoding strategy. If num_beams > 1, beam search is used; otherwise greedy decoding is the default. When do_sample is true, sampling with temperature, top_k, and top_p is applied. It supports single prompts and light batching on compatible hardware, with options to return raw token_ids and per-token scores for deeper analysis. The system also handles tokenization edge cases and truncation when prompts exceed max_length.
Unique value lies in isolating decoding decisions from training, enabling controlled experiments, reproducible results, and standardized benchmarks. This tool is beneficial for API development, prototyping chat or summarization workflows, evaluating decoding strategies, and benchmarking latency and throughput across settings. It supports reproducible experiments by fixing inputs and parameters, exporting outputs such as token_ids and scores for audit, and integrating with logging or experiment-tracking pipelines. Typical use cases include prompt-driven generation, researching quality-diversity tradeoffs, and rapid iteration of inference pipelines.