Expose optional intermediate hidden states / layer logits during generation (for reasoning metrics and model introspection)

### Feature request Hello, Recent research on reasoning metrics such as Deep-Thinking Ratio (DTR) and related work (e.g. https://arxiv.org/abs/2602.13517 and https://arxiv.org/abs/2603.10165) shows that intermediate transformer activations contain useful signals about reasoning quality during generation. ### Motivation These signals can be used for: • reasoning metrics (e.g. DTR) • candidate pruning in multi-sample reasoning • inference-time compute allocation • router calibration • model interpretability Currently most optimized inference frameworks only return the final logits or tokens, which makes it impossible to compute these metrics. It would be very helpful if the framework supported an optional debugging/research mode that exposes intermediate layer outputs during generation. For example: generate( prompt, return_hidden_states=True, layers=[4,8,12,16] ) or generate( prompt, return_layer_logits=True ) This mode could be: • disabled by default • restricted to batch size = 1 • limited to selected layers • marked as a research/debug feature Providing this capability would enable experimentation with reasoning metrics without requiring users to reimplement the entire inference stack in raw PyTorch. ### Your contribution Thank you for considering this feature.