Adaptive Decoding Support

### Feature request Adaptive decoding balances the diversity and coherence of open-ended text generation, making it an excellent method compared to top-k, top-p, and other decoding algorithms. For more details, the paper is available [here](https://arxiv.org/abs/2402.18223), and the code can be accessed [here](https://github.com/zwhong714/adaptive_decoding). ### Motivation During the generation process, the distribution predicted by the language model generally falls into two categories. The first is a flattened distribution, indicating that the LM has multiple potential choices for the next token. The second is a sharp distribution, suggesting that the model's options are more limited. Ensuring that the model dynamically understands the current state is crucial for generating highly diverse and coherent sentences. ### Your contribution idea done.