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This page walks through the minimal, valid PRISM submission shipped in the source repository: a weight-tied ~1.05M-parameter decoder transformer split into the two-script contract. Source: examples/tiny-1m/README.md:1-13.

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Layout

examples/tiny-1m/
  prism.yaml         # declares the architecture + training entrypoints and the tokenizer
  architecture.py    # exposes build_model(ctx); defines the model only
  training.py        # exposes train(ctx); the miner-owned loop
  • architecture.py exposes build_model(ctx) and is pure: it never reads data, opens files, or touches the network.
  • training.py exposes train(ctx): it forces the seed, builds the model via architecture.py, reads the read-only locked train split from ctx.data_dir, tokenizes with the pre-staged gpt2 reference tokenizer (offline), runs a single-node multi-GPU-safe loop, and writes only under ctx.artifacts_dir.
Source: examples/tiny-1m/README.md:6-20.

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The manifest

architecture:
  entrypoint: architecture.py
training:
  entrypoint: training.py
tokenizer: gpt2
This manifest omits kind, so the submission defaults to the full mode. See Submitting to PRISM for the three modes. Source: examples/tiny-1m/prism.yaml:1-5; src/prism_challenge/evaluator/components.py:22.

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A minimal architecture

import torch

class TinyModel(torch.nn.Module):
    def __init__(self, vocab_size):
        super().__init__()
        self.embedding = torch.nn.Embedding(vocab_size, 8)
        self.linear = torch.nn.Linear(8, vocab_size)

    def forward(self, tokens):
        return self.linear(self.embedding(tokens))

def build_model(ctx):
    return TinyModel(ctx.vocab_size)
Source: docs/submissions.md:137-153.

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A minimal training entrypoint

from architecture import build_model

def train(ctx):
    model = build_model(ctx)
    # construct the optimizer/schedule, read ctx.data_dir, tokenize, run the loop,
    # handle multi-GPU, and write only under ctx.artifacts_dir.
    ...
The container resolves architecture.py::build_model and training.py::train, forces the seed, launches torchrun, and captures the online loss itself. Source: docs/submissions.md:157-168.

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How it is scored

The challenge re-executes train(ctx) under a forced random initialization on the locked FineWeb-Edu train split, captures the single-pass online (predict-then-train) loss itself, and computes the prequential bits-per-byte score with a held-out delta tie-breaker. Any value this submission reports and any manifest it writes are ignored; the challenge authors prism_run_manifest.v2.json. Source: examples/tiny-1m/README.md:22-27.

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Submit the bundle

Submit the directory as a .zip bundle through the public route (when enabled) or let the BASE proxy forward it in production. 
POST /v1/submissions
Content-Type: application/json

{
  "filename": "project.zip",
  "code": "<base64 zip payload>",
  "metadata": {}
}
Source: examples/tiny-1m/README.md:29-33; src/prism_challenge/routes.py:32-33.
For the field-by-field contract, see Submitting to PRISM and Providing context. For the limits this example respects, see Constraints.