//! Tiny modern-architecture transformer (Phase T5).
//!
//! A from-scratch decoder built entirely from the [`xtrain_autodiff`] op set:
//! token embedding → `n_layers` × {pre-RMSNorm → multi-head causal attention
//! (per-head QK-norm + RoPE) → residual; pre-RMSNorm → SwiGLU MLP → residual} →
//! final RMSNorm → LM-head matmul. The forward builds an autograd graph; calling
//! `.backward()` on the cross-entropy loss fills every parameter's `.grad()`.
//! Per-head QK-norm (Qwen3-style) makes the architecture xserv-compatible (T9).
//!
//! Conventions (matching the engine, not HuggingFace):
//! - Linear weights are `[in, out]` and applied as `x @ W` (no transpose), since
//!   the engine's GEMM is plain `A @ B`.
//! - `dim == n_heads * head_dim` (no separate attention projection size).
//! - RoPE position = token row index (the kernel's built-in convention).
//! - Causal masking is an additive `[seq,seq]` constant (−1e9 above the diagonal)
//!   added to the attention scores before softmax.
//!
//! Everything GPU-facing is gated behind `not(no_cuda)`; on a GPU-less host the
//! crate still `cargo check`s (only [`Config`] is visible there).

mod config;
pub use config::Config;

#[cfg(not(no_cuda))]
mod model;
#[cfg(not(no_cuda))]
pub use model::{TinyTransformer, batched_ids_tensor, ids_tensor, param_to_host};
