"""Experiment v1 configuration.

One experiment = one config file. Iteration 2 is a copy of this file with
different numbers, not a code change.

v1 is deliberately small (~4.3M parameters): the goal is a complete, legible
pipeline, not a strong model. It sits below TinyStories' coherence sweet spot
(~30M params), so some incoherence in the output is expected.
"""

from dataclasses import dataclass


@dataclass(frozen=True)
class ModelConfig:
    vocab_size: int = 4096       # matches the trained tokenizer
    context_length: int = 256    # tokens of history the model attends over
    n_layers: int = 4
    n_heads: int = 4
    d_model: int = 256           # residual-stream width
    d_ff: int = 1024             # MLP hidden width (4 x d_model)


# The single source of truth other modules import.
model = ModelConfig()