###
A small GPT, trained in your browser on one repeated line and then asked to
continue a prompt, to be read as a story:

    viv story lib/gpt/story_gpt.viv

tells the run in the words of lib/gpt/gpt.intent and lib/nn.intent, one line
per training step with its loss and gradient norm (the watches in
lib/gpt/gpt.debug), then one line per sampled character. The program prints
the model's size, the loss before the first step and at the last, and what the
model wrote; the tests hold all four.
###
from gpt.gpt import decode, encode, Gpt, gpt_size, GptConfig, sample, train

TEXT, PROMPT = "hello world! " * 5, "hello"
STEPS, SAMPLES = 30, 24
INIT_SEED, SAMPLE_SEED = 7, 11

step tokenize {
    vocab = unique(TEXT)
    tokens = encode(TEXT, vocab)
}
step build {
    cfg = GptConfig { vocab: len(vocab) }
    srand(INIT_SEED)
    model = Gpt(cfg)
    count = gpt_size(model)
}
step train: losses = train(model, cfg, tokens, STEPS)
step generate: written = decode(sample(model, encode(PROMPT, vocab), SAMPLES, SAMPLE_SEED), vocab)
print(count)
print(losses[0])
print(losses[STEPS - 1])
print(written)

test "the model has 6,560 parameters, and 9 characters make its vocabulary" {
    assert_true(count == 6560 and len(vocab) == 9)
}

test "the first loss is log 9 exactly, because the output map starts at zero" {
    assert_near(losses[0], log(9.0), 1.0e-15)
}

test "thirty steps take the loss from 2.197 to 0.1991" {
    assert_near(losses[STEPS - 1], 0.199118, 1.0e-6)
}

test "from the prompt hello, the model writes the line on" {
    assert_true(written == "hello world! hello world! hel")
}

# SPDX-License-Identifier: MIT OR Apache-2.0
# SPDX-FileCopyrightText: 2026 The Viv authors